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🧠 Knowledge Memory Indexes in an AI Software Factory

🧠 Purpose of the Memory System

In the ConnectSoft AI Software Factory, intelligent agents autonomously generate, evolve, and deploy complete SaaS platforms. This spans:

  • ⚙️ Backend services
  • 🖼 Frontend UI & UX
  • 📜 Infrastructure & pipelines
  • 🔐 Security & compliance
  • 📚 Documentation & business artifacts

To operate effectively, these agents must access and reason over shared knowledge — structured and unstructured, design-time and runtime — across all factory domains.

The Memory System is the nervous system of the platform, enabling agents to:

  • 🧠 Reuse: existing code, templates, APIs, stories, diagrams
  • 🔁 Align: features with editions, blueprints with user stories
  • 🧪 Validate: runtime behavior, security policies, test coverage
  • ✍️ Document: code, flows, specs, decisions
  • 📊 Trace: lineage from business goal to running microservice

🧩 What the Memory System Contains

The system ingests, indexes, and semantically maps:

Knowledge Type Examples
Templates Microservice, frontend layout, component scaffolds
Libraries NuGet packages, internal SDKs, UI kits
Code .cs, .ts, .razor, .scss, .html, adapters, DI config
APIs & Events OpenAPI, gRPC, contracts, commands
Tests BDD specs, unit tests, UI validations
Design Assets Figma exports, SVGs, visual flow diagrams
UX & Accessibility Flows, ARIA roles, WCAG validations
Business Models Editions, feature flags, pricing rules
Infrastructure Bicep/Terraform, pipeline YAMLs, environments
Agent Prompts & Outputs System prompts, generated docs, DSLs
Runtime Observability Logs, metrics, spans, chaos scenarios

Each of these is indexed under a memory index (e.g., index-ui-components, index-blueprints, index-security-policies).

🔄 A Shared, Evolving Knowledge Graph

The memory system is not static — it continuously evolves:

  • 🔁 On push or merge: Agents re-ingest changed files and update embeddings
  • 🚀 On release: New templates, test cases, security specs are captured
  • 🤖 On generation: Agent-generated artifacts are stored as memory entries
  • 🧪 On execution: Observability, test logs, feedback enrich context

Think of memory as a living software knowledge graph, enriched by agents, engineers, documents, and runtime behavior.

🧠 What Makes It Agent-Usable?

Each memory record supports:

Capability Description
✅ Semantic Retrieval Embedded with vector representation (OpenAI, Azure AI Search)
✅ Metadata Filtering Tags, source, agent, domain, skill, project
✅ Prompt Injection Ready Memory decorators feed into planner + kernel prompt templates
✅ Traceable memoryId, sourcePath, traceId
✅ Agent Scoped Each record is linked to relevant agentType or agentCluster

🔐 Why It Enables Autonomous Engineering

Without memory:

  • Agents would generate conflicting, redundant, or insecure outputs
  • Frontend wouldn't align with backend contract
  • DevOps wouldn’t know what services exist
  • QA wouldn’t know what to validate
  • Releases would be unstable or incomplete

With memory: ✅ Agents communicate through shared knowledge ✅ Features are reused, traced, enriched ✅ The factory compounds intelligence over time

🧭 What’s Next in This Document

In upcoming cycles, you’ll explore:

  • 🧱 The architecture of the memory system (Cycle 2)
  • 🔎 Types of memory: semantic, metadata, visual, structural (Cycle 3)
  • 🧬 Index definitions across 35+ domains
  • ⚙️ Real-time ingestion and retrieval flows
  • 🔁 Agent-to-index mapping tables
  • 📄 Sample JSON entries and ingestion patterns

🧱 Memory System Architecture Overview

The ConnectSoft Memory System is a multi-layered, cloud-native knowledge architecture composed of:

  • 📦 Metadata DB — stores structured records and relationships
  • 🧠 Vector DB — enables semantic retrieval via embeddings
  • 📂 Blob Storage — preserves source files, prompts, visual artifacts
  • 🧮 Agent Ingestors & Retrievers — populate and query memory at runtime
  • 🔁 Ingestion Pipelines — keep memory updated on Git push or release

🗂️ Memory Architecture Diagram (Mermaid)

flowchart TD
  subgraph Memory System
    A[Metadata DB<br/>(CosmosDB or PostgreSQL)]
    B[Vector DB<br/>(Azure AI Search, Qdrant)]
    C[Blob Storage<br/>(Azure Blob)]
  end

  subgraph Sources
    R1[Git Repos<br/>Templates, Code, Docs]
    R2[CI/CD Pipelines<br/>Artifacts, Logs]
    R3[Runtime Logs<br/>Metrics, Spans, Events]
    R4[Figma, SVG, Images]
    R5[Agent Outputs<br/>Prompts, Plans, DSLs]
  end

  subgraph Agents
    A1[Ingestor Agents]
    A2[Retriever Agents]
    A3[Planners & Kernel]
  end

  R1 --> A1 --> A
  R1 --> A1 --> B
  R1 --> A1 --> C

  R2 --> A1
  R3 --> A1
  R4 --> A1
  R5 --> A1

  A & B & C --> A2 --> A3

  A3 -->|Prompt Memory Decorators| B
  A3 -->|Metadata Filters| A
Hold "Alt" / "Option" to enable pan & zoom

🧩 Architectural Components

Component Purpose
Metadata DB Stores index records for each knowledge entry, searchable by type, tags, projectId, agentId, etc.
Vector DB Provides high-performance semantic search (cosine similarity) on embedded content
Blob Storage Stores raw files, screenshots, prompt artifacts, diagrams
Ingestor Agents Extract metadata and embeddings from files, templates, runtime signals
Retriever Agents Search and return memory entries by type, embedding, or filters
Planner + Kernel Use memory decorators and retrieved content to enrich generation

🛠️ Deployment Technologies (Reference)

Component Azure-Recommended Stack
Metadata Store Azure Cosmos DB (NoSQL or PostgreSQL mode)
Vector Store Azure AI Search (Vector Index), or Qdrant / Weaviate
Blob Storage Azure Blob Storage
Agent Runtime Containerized (.NET 8), deployed on AKS or Azure Container Apps
CI/CD Ingestion Triggers Azure Pipelines, GitHub Actions, Webhooks

🧠 Memory Entry Structure

All memory entries conform to a core record structure:

{
  "memoryId": "mem-lib-001",
  "type": "library",
  "projectId": "connectsoft.sdk.logging",
  "agentId": "bootstrap-ingestor",
  "traceId": "init-load-202506",
  "tags": ["logging", "sdk", "backend"],
  "filePaths": ["src/Logging.cs", "README.md"],
  "description": "Reusable logging abstraction for .NET microservices",
  "embedding": [0.24, -0.11, ...],
  "createdAt": "2025-06-06T12:00:00Z"
}

🔄 Query Flow at Prompt-Time

  1. Planner detects task (e.g. “generate frontend component with ARIA”)
  2. Memory decorators activated
  3. Retriever Agent filters memory (e.g. type = ui-component, tags = accessibility)
  4. Vector similarity match retrieves relevant examples
  5. Results injected into Semantic Kernel prompt

🔁 Update Flow at Runtime

  1. Git push triggers library.yaml change
  2. Bootstrap Ingestor Agent pulls new commit
  3. Extracts metadata + embedding
  4. Stores in DBs
  5. Emits MemoryUpdated event (used by planner logs and agent decisions)

✅ Summary

The ConnectSoft Memory System is:

  • 🌐 Distributed: separates concerns across DBs, blobs, and vectors
  • 🔁 Dynamic: keeps memory updated in real-time
  • 🧠 Agent-Optimized: supports planner and prompt retrieval modes
  • 📊 Traceable: every memory item has metadata, embeddings, and linkage

🧠 Types of Memory: Metadata, Semantic, Structural, Visual

The ConnectSoft Memory System supports multi-dimensional representations of knowledge — enabling intelligent agents to search, reason, generate, and validate across all software domains.

Each memory record may include:

Type Description
🗂️ Metadata Structured fields for filtering, tracing, organizing
🧠 Semantic Embedded vector content for similarity search
⚙️ Structural Parsed symbolic information (code, YAML, JSON schemas)
🖼 Visual Image-based or layout representations (e.g., Figma, SVG)

These layers can coexist in a single memory entry or vary by index type.

🗂️ 1. Metadata Memory

Purpose:

Used for structured querying, traceability, filtering, and agent scoping.

Key Fields:

Field Description
type Memory type (e.g., template, test, blueprint)
tags Domain labels (e.g., frontend, accessibility, security)
agentId Agent that ingested or produced the item
projectId Logical or Azure DevOps project name
sourcePath File or folder from which data was ingested
traceId Execution trace or orchestration step identifier
createdAt Ingestion timestamp

🧠 Used heavily by: RetrieverAgent, KernelPlanner, QA agents, Release agents

🧠 2. Semantic Memory

Purpose:

Used for natural language similarity search, multi-agent coordination, and RAG-style generation.

Characteristics:

  • Embedded using OpenAI Ada, Azure AI Embeddings, or custom model
  • Vector field is called embedding: [float, float, …]
  • Stored in Vector DB (e.g. Azure AI Search)

Retrieval Examples:

searchMemory("reactive retry policy for HttpClient")

Returns similar entries from index-recipes, index-tests, index-chaos-scenarios.

🧠 Used heavily by: Generator agents, Planning agents, Code reviewers, Test suggesters

⚙️ 3. Structural Memory

Purpose:

Used for symbolic reasoning, code rewrites, type checking, code linking.

Common Representations:

Format Example
AST (Abstract Syntax Tree) For C# adapters, handlers, UI components
YAML or JSON Schema For pipeline templates, OpenAPI, configuration
Graph Models For event flows, dependency trees, microservice maps

Examples:

  • index-apis stores parsed OpenAPI specs
  • index-blueprints includes extracted event contracts
  • index-pipelines parses all jobs, steps, triggers

🧠 Used heavily by: Infrastructure agents, Validator agents, Architecture agents

🖼 4. Visual Memory

Purpose:

Used for diagram-aware reasoning, UI layout alignment, visual QA, and UX generation.

Supported Visual Types:

Format Use Case
.svg / .png Architecture diagrams, UI mockups
Figma .json or API export Design systems, spacing, layout rules
Mermaid / PlantUML System flows, bounded context diagrams
Annotated screenshots Error feedback, bug localization

Example Use Cases:

  • index-visual-assets supports svg:SearchBox.svg, figma:UserFlow.json
  • index-accessibility-specs links to color contrast overlays
  • index-design-system embeds Figma tokens as JSON vectors

🧠 Used heavily by: UX Designer Agent, UI Developer Agent, Accessibility Agent

🔄 Hybrid Records

Many memory items combine multiple types:

{
  "type": "ui-component",
  "tags": ["form", "accessibility", "frontend"],
  "description": "Email input with validation and ARIA",
  "embedding": [0.19, 0.22, ...],
  "componentCode": "src/components/EmailInput.razor",
  "figmaLink": "https://figma.com/designs/email-input",
  "accessibilitySpec": "aria-required, label-for, tab-index"
}

This allows the same record to be used by:

  • Code Generator
  • UI Designer
  • Accessibility Checker
  • Test Generator

✅ Summary

Type Power
✅ Metadata Organize, filter, trace
✅ Semantic Retrieve by meaning
✅ Structural Parse, validate, graph
✅ Visual See, test, generate layout

Together, they form a multi-modal memory substrate for fully autonomous agent reasoning and coordination across all SaaS development phases.

🚀 Memory Ingestion: First-Time Bootstrap

First-time bootstrap is the process by which the ConnectSoft Memory System performs a complete ingestion of knowledge from all available internal repositories and assets. This is the foundation layer that allows all agents to begin operating on a rich, consistent, and indexed knowledge base.

Without bootstrap, the factory is blind. With it, agents gain immediate reasoning ability across 3000+ microservices, templates, pipelines, and documentation sets.

📂 Sources for Bootstrap Ingestion

Source Description
📁 Azure DevOps Git Repos Templates, microservices, shared libraries, documentation
📚 Markdown Repositories MkDocs sites, blueprints, architecture docs
🧱 Infrastructure Repos IaC: Bicep, Terraform, environment configs
📜 CI/CD Pipelines Reusable YAML jobs, steps, stages
🧩 NuGet Packages Internal SDKs with .nuspec and readme metadata
🎨 Figma, SVG, Design Assets UI kits, screenflows, component visual specs
📎 DSLs & Prompt Templates AdapterDSL, EventDSL, prompt skeletons
🧪 Testing Artifacts SpecFlow scenarios, test suites, test plans

🧠 What Gets Created During Bootstrap

For each file or logical unit processed, the system creates:

  • ✅ A memory record with:

  • memoryId, type, tags, projectId, traceId

  • Embedded semantic vector (if enabled)

  • Linked source files and derived metadata

  • ✅ An entry in:

  • Metadata DB (Cosmos/Postgres)

  • Vector DB (Azure AI Search, Qdrant, etc.)
  • Blob storage (for raw file/archive reference)

⚙️ Bootstrap Agent Behavior

Agent: BootstrapMemoryIngestorAgent

Responsibility Details
Detect Files Traverse folders by known path patterns
Classify Determine memory type (e.g., template, pipeline)
Extract Parse metadata, tags, domains, descriptions
Embed Generate semantic vectors using embedding model
Persist Store in DBs and emit MemoryItemCreated event

Multiple versions of the agent exist: .NET CLI, Docker container, and Azure Pipelines step.

🛠️ Sample CLI Usage

dotnet run --project ConnectSoft.BootstrapIngestor \
  --source ./templates/MicroserviceTemplate \
  --type template \
  --projectId ConnectSoft.Templates \
  --traceId bootstrap-init-202506

🧪 YAML Ingestion Step (Pipeline)

- task: Bash@3
  inputs:
    targetType: 'inline'
    script: |
      docker run --rm \
        -e MODULE_TYPE=template \
        -e FILE_PATH=templates/MicroserviceTemplate \
        -e OUTPUT_DIR=memory/bootstrap \
        connectsoft/bootstrap-ingestor:latest

🧩 Domain-Specific Directories to Scan

Index Type Folder Patterns
index-templates /templates/*Template/
index-ui-components /src/components/ or /frontend/shared/
index-design-system /tokens/*.json, /styles/theme/
index-recipes /snippets/, /config/, /samples/
index-tests /features/, /tests/, /specs/
index-blueprints /docs/blueprints/
index-apis /contracts/openapi.yaml
index-security-policies /security-policies/*.yaml

📦 Output Artifacts

Each ingestion run generates:

  • .json memory files ready for:
    • Insertion into metadata + vector store
    • Manual QA or test validation
  • Trace logs of what was indexed
  • Summary files (index-summary.yaml)

✅ Summary

Feature Value
📥 Fully automated initial ingestion Yes
🔗 Multi-format support .cs, .md, .yaml, .svg, .figma, .json
🔄 Domain-aware classification Templates, pipelines, APIs, designs, etc.
🔎 Search-ready embedding Yes, with vector similarity
📚 Agent-usable records Immediately usable in planning, generation, validation

🔁 Memory Ingestion: Real-Time Sync

Once the initial bootstrap is complete, the ConnectSoft Memory System must stay continuously up to date as code, templates, documentation, and infrastructure evolve.

Agents depend on fresh, version-aligned memory to:

  • Avoid stale references or outdated components
  • Validate changes against live blueprints or contracts
  • Trigger regeneration or propagation tasks (e.g., update tests after OpenAPI change)
  • Maintain full traceability and temporal understanding of the system

🔄 Triggers for Real-Time Sync

Trigger Source Description
🟦 Azure DevOps Git Push Any commit to main, dev, or release/* triggers smart ingestion
🔁 CI/CD Pipeline Events post-build, post-release, template-publish events notify ingestors
🔔 File System Watchers (local/dev) Dev agents detect file saves for immediate memory preview
📤 Manual Publish Engineers can push knowledge using CLI/REST (e.g., after generating docs)
🧠 Agent Output Hooks When an agent creates a new DSL, blueprint, or prompt, it posts memory directly

📋 Real-Time Ingestion Flow

sequenceDiagram
    participant DevOps as Azure DevOps Repo
    participant Ingestor as RealTimeIngestor Agent
    participant Metadata as Metadata DB
    participant Vector as Vector DB
    participant Blob as Blob Store

    DevOps->>Ingestor: Push detected to /templates/api/*
    Ingestor->>Ingestor: Classify as type: template
    Ingestor->>Vector: Embed semantic vector
    Ingestor->>Metadata: Store structured metadata
    Ingestor->>Blob: Archive file for trace
    Ingestor-->>DevOps: Emit `MemoryItemUpdated` event
Hold "Alt" / "Option" to enable pan & zoom

🧩 Change Detection Patterns

Change Type Resulting Action
.csproj, .nuspec modified Reindex library metadata (index-libraries)
openapi.yaml updated Update index-apis, regenerate sample client
.md file edited in /docs/ Reindex documentation (index-documentation)
*.razor or *.scss updated in /components/ Refresh index-ui-components and accessibility mappings
security-policies.yaml added New entry in index-security-policies
*.feature file added or changed Reindex index-tests
Any .json, .yml with template config Update index-templates or index-pipelines

⚙️ Real-Time Ingestor Agent Behavior

Agent: RealTimeMemoryIngestorAgent

Responsibility Details
Diff-Aware Scan Only updates changed files
Auto-Tagging Reuses domain classifiers (e.g., frontend, security, chaos)
Auto-Embedding Updates vector field in parallel with metadata
Versioned Memory Items Keeps full history of change unless TTL or deduping is active
Event-Driven Output Emits MemoryItemUpdated, BlueprintVersionChanged, PromptUpdated events to message bus

🔧 Azure DevOps Pipeline Hook Example

- task: Bash@3
  name: SyncMemory
  inputs:
    targetType: 'inline'
    script: |
      dotnet run --project ConnectSoft.RealTimeIngestor \
        --source . \
        --traceId post-release-$(Build.BuildId)

🧪 Dev-Local Sync Option

Agents can be configured to auto-ingest when local .cs, .md, .dsl, or .blueprint.yaml files are saved (for live preview and memory debugging).

📥 Auto-Generated Sync Output

{
  "memoryId": "mem-template-1098",
  "type": "template",
  "projectId": "ConnectSoft.Templates.API",
  "filePaths": ["templates/api-microservice/openapi.yaml"],
  "tags": ["backend", "http", "openapi", "template"],
  "description": "HTTP-based microservice with OpenAPI and outbox pattern",
  "lastUpdated": "2025-06-06T17:30:00Z",
  "embedding": [0.19, -0.02, ...],
  "traceId": "release-11567"
}

🔄 Emitted Events for System Coordination

Event Trigger
MemoryItemUpdated Any changed record
BlueprintVersionChanged Core design updated (e.g. contract, adapter)
TestPlanOutdated Test needs regeneration
PromptTemplateChanged DSL or agent prompt changed
EditionDefinitionChanged SaaS model modified

✅ Summary

Capability Enabled
Git-aware diff scanning ✅
Per-index classification and routing ✅
Agent-generated output auto-injection ✅
Semantic embedding and tagging ✅
Cross-index event propagation ✅

Real-time ingestion ensures the AI Software Factory remains in sync with reality, allowing agents to operate continuously and confidently on the latest ground truth.

📂 Memory Sources and File Types

A memory source is any location, artifact, or data structure in the ConnectSoft ecosystem from which knowledge can be extracted and transformed into agent-usable memory.

These sources span:

  • 🧱 Code repositories (backend, frontend, infra)
  • 📚 Documentation and Markdown sites
  • 🎨 Design files and UI specs
  • 🔧 Configuration and orchestration layers
  • 🧪 Test and validation systems
  • 🧠 AI-generated artifacts (DSLs, prompts, agent plans)

📁 Source Categories

1. Git-Based Sources

Source Type Purpose
/templates/ Microservice, library, orchestrator, API templates
/src/ Microservice code, adapters, handlers
/frontend/ UI components, layouts, modules
/contracts/ OpenAPI, events, policies
/features/ BDD test specs (SpecFlow, Gherkin)
/docs/ MkDocs documentation, blueprints, READMEs

2. Documentation & MkDocs

File Type Description
*.md Markdown documentation
mkdocs.yml Navigation and structure
blueprint.yaml Architecture or module spec
index-summary.yaml Ingestion summary and memory metadata

Each page is embedded semantically and metadata-tagged by path, domain, and document type.

3. Infrastructure-as-Code (IaC)

File Type Description
*.bicep Azure IaC definition
*.tf, *.tfvars Terraform definitions
*.yaml Pipeline templates
variables.json Pipeline parameterization

These populate index-iac and index-pipelines.

4. Frontend, UI, UX Sources

File Type Purpose
.razor, .ts, .html, .scss UI layout and behavior
theme.tokens.json, variables.scss Design tokens
accessibility-checks.yaml WCAG or ARIA linting rules
*.figma.json, *.svg, .png Visual design assets and diagrams

Frontend knowledge is indexed across:

  • index-ui-components
  • index-design-system
  • index-visual-assets
  • index-accessibility-specs

5. Runtime and Observability Sources

File Type / Format Description
log.json Structured logs (OpenTelemetry, Serilog)
span.json, metrics.json Performance traces
event.json, error.yaml Runtime events (failures, retries)
resiliency-score.yaml Resiliency test results

Used by index-runtime-metrics, index-chaos-scenarios, index-runtime-events.

6. Agent-Generated Artifacts

Source Type Description
.prompt, .skill.json AI system and input prompts
.dsl, .blueprint.yaml AI-generated design structures
code-review-suggestions.md Outputs from analyzer agents
user-story.generated.yaml User stories and epic mappings
observability-plan.yaml Agent-generated telemetry schemas

These are auto-pushed into:

  • index-blueprints
  • index-prompts
  • index-skills
  • index-agent-outputs

🧩 Ingested File Types by Index

Index File Types
index-templates .csproj, .yaml, Program.cs, .template.config
index-libraries .csproj, README.md, .nuspec, .dll
index-frontend-templates .razor, .html, .scss, .ts
index-ui-components .razor, .tsx, .svg, .json
index-accessibility-specs .yaml, .json, .md
index-pipelines .yml, .yaml, pipeline.config.json
index-iac .bicep, .tf, .tfvars, .json
index-tests .feature, .cs, testplan.yaml
index-security-policies security-policies.yaml, rbac.json
index-runtime-events event.json, log.json, span.json

📌 Naming Conventions

Each index record can trace its origin with these fields:

Field Example
sourcePath templates/MicroserviceTemplate/Program.cs
projectId ConnectSoft.MicroserviceTemplate
agentId bootstrap-ingestor
memoryId mem-template-0012
traceId initial-load-2025-06

✅ Summary

Source Class Supported?
Git-based repositories ✅
Markdown documentation ✅
UI layout & tokens ✅
IaC & pipelines ✅
Runtime metrics & logs ✅
Agent outputs ✅
DSLs & test blueprints ✅

Memory ingestion covers every artifact involved in fullstack software delivery, ensuring AI agents reason across code, design, infrastructure, and business layers in real time.

📦 index-libraries: SDKs, Packages, Shared Code

The index-libraries collection contains all reusable code libraries and NuGet packages used across the ConnectSoft AI Software Factory. These include both backend and frontend shared logic, spanning infrastructure, utilities, cross-cutting concerns, and domain SDKs.

This index allows agents to:

  • 🔁 Reuse shared abstractions when generating code
  • 🧠 Learn API surfaces and intended usage patterns
  • 🧪 Validate whether custom code should reuse or extend existing packages
  • 🔍 Search by functionality (e.g., logging, caching, serialization)

Think of this index as the foundation for dependency injection and reuse intelligence.

📂 Examples of Ingested Libraries

Library Purpose
ConnectSoft.Logging Logging abstraction across microservices
ConnectSoft.Messaging.MassTransit Shared bus configuration
ConnectSoft.Orleans.SiloSetup Orleans-hosting glue for actors
ConnectSoft.Localization I18N and string resource helper
ConnectSoft.Extensions.Http.OAuth2 Secure API client with token support
ConnectSoft.UI.Kit Reusable Blazor/Razor UI components
ConnectSoft.Testing.SpecFlowBase Shared test hooks and context

📄 File Types Ingested

File Purpose
*.csproj Name, dependencies, conditional flags
README.md Descriptions and usage guidance
.nuspec (if available) Version, description, tags
public/*.cs Public interfaces, contracts
CHANGELOG.md Versioning and semantic changes
samples/*.cs, examples/ Usability showcase for generators

🧠 Sample Memory Entry

{
  "memoryId": "mem-lib-logging-2025",
  "type": "library",
  "projectId": "ConnectSoft.Logging",
  "tags": ["logging", "backend", "structured-logging"],
  "description": "Cross-platform logging SDK with Serilog and OpenTelemetry support.",
  "filePaths": ["src/Logging.csproj", "README.md"],
  "embedding": [0.111, -0.021, ...],
  "traceId": "bootstrap-init-20250601"
}

🧩 Key Metadata Fields

Field Notes
type Always library
tags Functional tags + domains (frontend, backend, infra, observability, etc.)
agentId Typically bootstrap-ingestor, or real-time-ingestor
projectId Canonical name, e.g., ConnectSoft.Extensions.Http.OAuth2
embedding Descriptive vector from README + API names

🔍 Retrieval Use Cases

Agent Query Example
Code Generator Agent “Use the standard OAuth2 HttpClient abstraction” → finds ConnectSoft.Extensions.Http.OAuth2
Test Generator Agent “Use test context for SpecFlow” → resolves to ConnectSoft.Testing.SpecFlowBase
Code Reviewer Agent “Why was a custom logging adapter used here?” → Suggest reuse from ConnectSoft.Logging

🛠️ Ingestion Rules

Rule Logic
Must have public folder or exported interfaces ✅
Must include a README or description tag ✅
Preferably versioned in .nuspec or .csproj ✅
Optional: sample folder, markdown usage guides ✅

🧠 Frontend Support

The same structure applies to frontend and UI libraries:

Example Use
ConnectSoft.UI.Kit Blazor input components
ConnectSoft.Components.Modals Shared modal logic
ConnectSoft.Localization Frontend string helpers

Agents generating frontend logic or scaffolds can semantically search for UI packages and usage examples.

📊 Memory Health Metrics

Metric Goal
Libraries indexed >300
With valid embeddings 100%
With usage examples ≥ 70%
Cross-agent references Tracked across prompts, blueprints, DSLs

✅ Summary

The index-libraries index enables intelligent code reuse by exposing all internal and third-party shared libraries as agent-readable, semantically searchable memory records.

  • 📚 Source of truth for reuse
  • 🔁 Prevents reinvention
  • 🧠 Enhances context for generation, review, and refactoring

🧱 index-templates: Project Scaffolds and Code Generation Blueprints

The index-templates catalog contains all reusable project and module templates that are used by ConnectSoft agents to:

  • 🚀 Scaffold new microservices, libraries, APIs, or orchestration modules
  • 📦 Ensure consistent structure, patterns, and observability
  • 🔁 Align with clean architecture, event-driven, and multi-tenant SaaS standards
  • 🧠 Accelerate generation through embedded examples and annotations

This is a core knowledge base for generation, mutation, and regeneration workflows.

📂 Examples of Indexed Templates

Template Description
MicroserviceTemplate Domain-aligned microservice with MassTransit, NHibernate, DDD layers
ApiGatewayTemplate YARP-based gateway with routing rules and tenant support
AuthServerTemplate OpenIddict-based authentication and token issuing server
FrontendServiceTemplate Razor/Blazor-based UI scaffold
LibraryTemplate Shared SDK or abstraction with optional DI and test base
TestProjectTemplate MSTest + SpecFlow test project with hooks, mocks, context
AzureFunctionTemplate Worker microservice with queue/event triggers
DSLBasedGeneratorTemplate Specialized for semantic kernel + DSL input scaffolding

📁 Files Processed

File Purpose
.template.config/template.json Template metadata (name, symbols, tags)
Program.cs, Startup.cs Entry points, DI, host setup
*.csproj, Directory.Build.props Package references, conditional logic
README.md, usage.md Developer usage instructions
.blueprint.yaml (if present) Declarative template structure for planning agents
openapi.yaml, events.yaml Sample contracts, if scaffolded

Each of these components contributes to structured memory records with embeddings + metadata.

🧠 Sample Memory Entry

{
  "memoryId": "mem-template-microservice-2025",
  "type": "template",
  "projectId": "ConnectSoft.Templates.Microservice",
  "tags": ["backend", "microservice", "clean-architecture", "event-driven"],
  "description": "DDD-aligned microservice template with NHibernate and MassTransit",
  "embedding": [0.147, -0.033, ...],
  "filePaths": ["templates/MicroserviceTemplate/Program.cs", "README.md"],
  "traceId": "bootstrap-init-202506"
}

📌 Key Metadata Fields

Field Notes
type Always template
tags Tags extracted from template.json + folder name
projectId Canonical logical ID
traceId Ingestion or sync identifier
embedding Semantic vector from README + usage text

🧠 Semantic Usage Patterns

Templates are frequently retrieved using embedded intent such as:

Prompt Resolved Template
“Create API service with retry and outbox” MicroserviceTemplate
“Build OpenIddict server with DI, login, and consent” AuthServerTemplate
“Scaffold UI service for tenant dashboard” FrontendServiceTemplate
“Add library with test harness and options validation” LibraryTemplate

🔄 Versioning & Specialization

Templates can include:

  • 🧬 DSL-aware parameters (for prompt expansion)
  • 🏷 Feature flags (e.g., UseMassTransit, UseNHibernate)
  • 🔁 Variants per domain (e.g., MicroserviceTemplate.Email, MicroserviceTemplate.Tracking)

This flexibility is captured in metadata and indexed for planner use.

🧠 Integration with Prompt Expansion

When agents initiate generation flows, the planner uses:

  • ✅ index-templates to retrieve candidate scaffolds
  • ✅ Embedding to match intent with example usages
  • ✅ Metadata filters to enforce domain or tech stack (e.g., frontend + blazor)

✅ Summary

The index-templates knowledge base enables agents to generate entire working solutions in seconds by leveraging:

  • 🧱 Pre-built, observability-ready scaffolds
  • 🧠 Semantically embedded usage guides
  • 📦 Tagged features and configurations
  • 🔁 Automatic alignment with factory principles

🎨 index-frontend-templates: UI & Web App Scaffolds

The index-frontend-templates catalog provides all frontend and UI project templates used to generate web-based user experiences — from dashboards to admin panels and public websites.

These templates empower agents to:

  • ⚙️ Scaffold complete frontend solutions aligned with design systems
  • 🧠 Understand and reuse layout structure, theming, and interaction flows
  • 🌐 Ensure accessibility (ARIA, contrast, keyboard navigation)
  • 🔁 Align with backend contracts and event flows

This index powers all AI-generated UI, including onboarding flows, feature dashboards, and i18n-enabled experiences.

📂 Examples of Indexed Frontend Templates

Template Description
BlazorAppTemplate Full-featured Razor-based SaaS web application shell
DashboardTemplate Modular dashboard layout with responsive grid
SettingsPanelTemplate Common settings page with form fields and save logic
TenantSwitchTemplate Identity-aware multi-tenant layout
LandingPageTemplate Hero section, CTA, testimonials, and newsletter
UIComponentPackTemplate Library of common controls (button, input, modal, tabset)
OnboardingWizardTemplate Step-based guided experience for new users

📁 Files Processed

File Purpose
.razor, .cshtml UI structure and rendering
.html, .scss, .css Static templates and styling
.ts, .js Client-side interactivity
.theme.json, design.tokens.json Color, spacing, layout tokens
README.md, usage.md Example usage and guidance
.blueprint.yaml Agent-aware scaffolding instructions (optional)

🧠 Sample Memory Entry

{
  "memoryId": "mem-template-dashboard-2025",
  "type": "template",
  "projectId": "ConnectSoft.Templates.Dashboard",
  "tags": ["frontend", "ui", "blazor", "dashboard", "responsive"],
  "description": "Modular responsive dashboard with header, sidebar, widget zones",
  "embedding": [0.171, -0.025, ...],
  "filePaths": ["templates/DashboardTemplate/MainLayout.razor", "theme.tokens.json"],
  "traceId": "bootstrap-init-202506"
}

🧩 Metadata Fields

Field Notes
type Always template
tags Includes UI tech (blazor, angular, react) + layout intent (wizard, form, panel)
projectId Logical ID for retrieval
embedding Captures semantic layout usage from README + layout code

🧠 Common Retrieval Intents

Agent Prompt Matching Template
“Generate onboarding wizard with three steps” OnboardingWizardTemplate
“Add settings page to SaaS portal” SettingsPanelTemplate
“Create tenant-aware sidebar layout” TenantSwitchTemplate
“Add hero + signup call to action” LandingPageTemplate

🧪 Accessibility and Design System Awareness

Templates in this index must follow:

  • ✅ WCAG 2.1 guidelines
  • ✅ Design system color/spacing tokens
  • ✅ ARIA roles and landmarks
  • ✅ Contrast checks via accessibility-checks.yaml (linked)

Agents like the Accessibility Engineer Agent and UI Developer Agent reference this index when scaffolding compliant UI.

🧠 i18n Support

Each frontend template may include:

  • i18n/en.json, i18n/fr.json – localized strings
  • localization.config.yaml – string resource structure
  • Usage of @Localizer["key"] or similar semantic markers

This ensures the Language Adapter Agent can inject multilingual support directly from indexed patterns.

💡 Blueprint Extension Example

A dashboard template can include a companion .blueprint.yaml:

templateId: dashboard
layout:
  header: true
  sidebar: true
  widgets:
    - name: UserActivity
    - name: SubscriptionSummary
    - name: AlertLog
defaultRoutes:
  - /dashboard
  - /settings

This blueprint is semantically indexed for plan-to-UI alignment.

✅ Summary

The index-frontend-templates index provides the base for:

  • 🔁 Reusable, accessible UI scaffolds
  • 🧠 Intelligent layout alignment with user journeys
  • 🌍 Multilingual UI support with design system fidelity
  • 👩‍🦯 Accessibility-compliant frontend generation

It powers all agent-generated frontend entrypoints, interfaces, and interaction flows.

🧬 index-codebase: Domain Code, Adapters, and Implementations

The index-codebase provides agents with deep knowledge of all production source code that powers ConnectSoft-generated SaaS solutions.

Unlike index-templates (scaffolds) and index-libraries (shared SDKs), this index captures:

  • ⚙️ Real domain code from active microservices
  • 🔌 Adapter, handler, and use-case implementations
  • 🔁 Integration points (e.g., HTTP, message bus, ORM)
  • 🧪 Validated business logic used in production

This index is critical for reuse, mutation, validation, and testing across agent-driven feature delivery.

📂 What Gets Indexed

Code Element Description
Adapters Inbound/Outbound boundaries (HttpAdapter, Consumer, Producer)
Handlers Use case logic tied to a command, query, or event
Entities Domain objects with persistence, validation, events
Services Domain services (e.g., retry scheduler, notifier, translator)
Extensions Logging, options, DI, validators
Configuration DI containers, MassTransit setup, NHibernate mapping
Constants/Enums Shared enum types used in events, APIs, DB

📁 Files Processed

File Description
*.cs C# source files from /src/, /Domain/, /Application/, /Infrastructure/
*.ts / *.js Frontend-specific logic in /frontend/
*.razor.cs Blazor components with C# code-behind
*.scss, *.css UI styles only if paired with functional logic
*.json, *.yaml Adapter metadata, config, or route maps (if co-located)

🧠 Sample Memory Entry

{
  "memoryId": "mem-codebase-adapter-notification",
  "type": "adapter",
  "projectId": "NotificationService",
  "tags": ["adapter", "email", "command", "http"],
  "description": "HttpAdapter to receive email notification requests and enqueue SendEmailCommand",
  "filePaths": [
    "src/NotificationService/Adapters/Http/SendEmailAdapter.cs"
  ],
  "embedding": [0.212, -0.033, ...],
  "traceId": "real-time-sync-20250606"
}

🧠 Use Cases for Retrieval

Agent Scenario
Code Generator Agent Extend SendEmailAdapter with retry metadata
QA Agent Generate test suite for all handlers with IEmailService dependency
Release Coordinator Ensure all CreateUserHandler implementations conform to DDD rule
Observability Agent Verify each Adapter emits correct span events
Code Reviewer Agent Validate that all commands follow validation contract rules

🧩 Code Intelligence Features

Feature Purpose
Parsed class/interface names Enable symbol search and graphing
Method signature extraction Feed prompts with realistic API hints
Reference map generation Map usage across services and libraries
Cross-index linking Connect to index-events, index-tests, index-templates

🔄 Real-Time Ingestion

Agents ingest source files:

  • On every Git commit
  • When a file is modified inside /src/, /Application/, /Frontend/
  • Based on commit diff analysis (modified, added, renamed)
  • Auto-generating a type: adapter, handler, entity, ui-component, etc.

📊 Coverage Metrics

Metric Goal
Adapters indexed ≥ 95%
Handlers with embedding 100%
Entities with relationships mapped Yes
Test coverage traceability Enabled (linked to index-tests)

🤖 Cross-Agent Impact

Agent Use
Product Manager Agent Suggests stories by comparing codebase vs requirements
Test Automation Agent Builds test cases directly from indexed handlers
Documentation Writer Agent Describes adapter logic using embedded function names
Runtime Verification Agent Maps telemetry spans back to code source

✅ Summary

The index-codebase provides semantic, structural, and referential knowledge of all working software implementations. It enables agents to:

  • 🔁 Reuse and extend real-world logic
  • 🔍 Validate architectural, observability, and DDD compliance
  • 🧪 Generate tests and docs with contextual precision
  • 🔄 Track live evolution of production code over time

🗺️ index-blueprints: Architecture, Design, DSLs

The index-blueprints serves as the structural knowledge layer for the entire AI Software Factory. It stores:

  • 📐 Architecture and system design specifications
  • 🧬 Domain-driven design (DDD) contexts, aggregates, and boundaries
  • 🔌 Event contracts, message flows, service topologies
  • 📜 DSL-generated service and module plans
  • 📦 Feature-to-code mappings (agent output → microservice)

This is the highest-level declarative index, guiding all generation, validation, and orchestration decisions.

📂 Types of Blueprints

Blueprint Type Description
architecture-blueprint.yaml High-level system design across services/modules
bounded-context-blueprint.yaml DDD-aligned context map
microservice-blueprint.yaml Defines one service’s contracts, adapters, events, commands
dsl-service-blueprint.dsl DSL-generated description of a component (e.g. adapter, handler)
feature-blueprint.yaml Maps a business feature to files, domains, and tests
prompt-blueprint.yaml Defines prompt structure for kernel agents
test-blueprint.yaml Associates tests with features, events, errors

Each blueprint is treated as a first-class semantic and structural memory object.

📁 Files Processed

File Description
*.blueprint.yaml Core format used for manual or agent-defined plans
*.dsl DSL-based blueprints (AdapterDSL, EventDSL, etc.)
*.md (annotated) Markdown blueprint specs with YAML frontmatter
*.drawio.svg, .png, .mermaid Optional visual overlays

🧠 Sample Memory Entry

{
  "memoryId": "mem-blueprint-user-service",
  "type": "microservice-blueprint",
  "projectId": "UserService",
  "tags": ["architecture", "ddd", "events", "bounded-context"],
  "description": "Blueprint for user identity microservice with OpenIddict integration and audit trail",
  "filePaths": ["docs/blueprints/UserService.blueprint.yaml"],
  "embedding": [0.091, -0.148, ...],
  "traceId": "vision-architecture-202506"
}

🧩 Metadata Fields

Field Notes
type blueprint, bounded-context, feature-blueprint, dsl-blueprint, etc.
tags Domains like ddd, events, frontend, adapter, resiliency
embedding Derived from YAML structure, description, and relationships
projectId Targeted project or logical module
agentId Often EnterpriseArchitectAgent, DSLPlannerAgent, etc.

🧠 Retrieval Scenarios

Agent Example Use
Solution Architect Agent Retrieve blueprint for "AuditService" to align new event
Microservice Generator Agent Find DSL blueprint for NotificationHandler
Test Generator Agent Map test paths from feature-blueprint.yaml
Observability Agent Ensure blueprint.observability.spans[] match runtime spans
DevOps Agent Build pipeline based on deployment.strategies in blueprint

✍️ Blueprint DSL Format (Example)

service:
  name: AuditTrailService
  ports:
    - command: WriteAuditLog
    - event: AuditLogCreated
  adapters:
    - HttpWriteAdapter
    - EventLogConsumer
  events:
    - AuditLogCreated
    - AuditLogFailed
observability:
  spans:
    - write_audit_log
    - persist_event

🔁 Real-Time Blueprint Update Flow

  1. Agent defines or modifies a .blueprint.yaml
  2. RealTimeIngestor re-indexes the file
  3. Emits BlueprintVersionChanged
  4. Downstream agents (e.g. QA, Observability) react and align accordingly
  5. Updated embedding + metadata stored in vector + metadata DBs

🧠 Cross-Index Linkage

  • blueprints → index-codebase: references to actual handlers/adapters
  • blueprints → index-events: links to events, commands, contracts
  • blueprints → index-tests: required scenarios or test mappings
  • blueprints → index-observability: expected spans, alerts, KPIs

✅ Summary

The index-blueprints forms the semantic backbone of ConnectSoft, enabling agents to:

  • 🧱 Generate microservices based on structured intent
  • 🧭 Align to architectural boundaries and events
  • 🔍 Validate design → code → observability correctness
  • 🔁 React to blueprint changes in real-time

📡 index-events: Domain Events, Commands, and Contracts

The index-events is the canonical source of truth for all event-driven artifacts in the ConnectSoft platform. It includes:

  • 🔔 Domain events
  • 📩 Integration events
  • 📤 Commands
  • 📥 Message contracts
  • 🧪 Event schemas for validation and simulation

This index powers event-driven generation, trace validation, contract testing, and agent choreography.

📂 Event Artifact Types

Type Description
domain-event Core business signals (e.g. UserRegistered, InvoicePaid)
integration-event External-facing events (e.g. Vetspire.ClientSynced)
command Directed actions (e.g. SendEmailCommand, RetryPaymentCommand)
event-contract YAML or JSON schema of fields and types
event-blueprint Agent-generated metadata for routing, retries, DLQ, etc.

Each record in this index supports both semantic (what it means) and structural (how it looks) memory.

📁 Files Processed

File Description
events/*.cs Strongly typed event/command classes
contracts/*.yaml, *.json JSON schema / OpenAPI contracts
*.event.yaml Event metadata including retries, routing, consumers
*.proto (optional) If gRPC/Protobuf contracts are used
event-blueprint.dsl DSL describing flow and transformation

🧠 Sample Memory Entry

{
  "memoryId": "mem-event-user-registered",
  "type": "domain-event",
  "projectId": "UserService",
  "tags": ["event", "user", "identity", "email", "notification"],
  "description": "Triggered after new user registration. Contains email, userId, and timestamp.",
  "filePaths": ["contracts/events/UserRegistered.cs"],
  "embedding": [0.202, -0.074, ...],
  "traceId": "bootstrap-init-202506"
}

🧩 Key Metadata Fields

Field Description
type domain-event, command, integration-event, etc.
projectId Owning service or domain
eventName Auto-extracted symbol or declared name
tags Derived from fields, path, usage context
consumers[] Derived from blueprint or adapter analysis
publishingService Originating bounded context
embedding Semantic embedding of intent and payload
schema Parsed JSON schema, if available

🔍 Retrieval Use Cases

Agent Query
Code Generator Agent “Find command for retrying email” → RetryEmailCommand
QA Agent “What test cases must fire UserRegistered?”
Blueprint Agent “Show all consumers of Vetspire.ClientUpdated”
Observability Agent “Ensure span exists for event:UserDeleted → audit-log:WriteAuditLog”
Chaos Engineer Agent “Inject PaymentFailed with invalid payload”

🧠 Structural Awareness

Each indexed event includes:

  • 🧾 Full schema (from .cs, .json, or .yaml)
  • 📍 Relationships (what publishes, what consumes)
  • 🕸️ Reference graph across services
  • 🧠 Embedding for similarity, discovery, and planning

🔁 Event Update & Coordination

  1. Event definition modified in source repo
  2. Real-time ingestor re-parses schema, regenerates embedding
  3. Emits:
    • EventContractChanged
    • BlueprintVersionChanged
    • TestPlanOutdated
  4. Triggers:
    • Test re-validation
    • Blueprint reconnection
    • Event-driven UI or API regeneration

📡 Event Mapping Example

event: UserRegistered
schema:
  userId: Guid
  email: string
  timestamp: datetime
consumedBy:
  - WelcomeEmailService.SendEmailCommand
  - AuditTrailService.LogUserSignup
publishedBy: UserService.RegisterUserHandler
tags: [user, identity, welcome, audit]

✅ Summary

The index-events empowers ConnectSoft agents to work seamlessly across event-driven architectures, enabling:

  • 🔁 End-to-end event orchestration
  • 🔍 Accurate event testing, tracing, and validation
  • 🔌 Automatic service choreography and adapter generation
  • 📜 Schema-aware contract enforcement

It is a core semantic + structural index used across nearly every agent cluster.

🧪 index-tests: Feature, BDD, Integration, and Unit Testing Knowledge

The index-tests is the primary index for all test-related memory artifacts in the ConnectSoft platform. It gives agents structured, retrievable access to:

  • ✅ SpecFlow BDD feature files
  • 🧪 Integration, contract, and unit tests
  • 🧭 Coverage mappings to blueprints and codebase
  • 🧠 Test plan declarations and validations
  • 💥 Chaos and resiliency test scenarios

This index ensures that AI agents validate, maintain, and generate tests for every delivered feature or blueprint.

📂 Types of Tests Indexed

Test Type Description
bdd-feature Gherkin .feature files (SpecFlow/Cucumber)
unit-test Logic-level tests, often in *.Tests projects
integration-test End-to-end service or message-based tests
resiliency-test Timeout, retry, chaos, and circuit breaker tests
contract-test Schema validations, e.g. for openapi.yaml or event contracts
test-plan Declarative test plans per service or feature
test-blueprint Agent-generated mapping of what → where → how tested

📁 Files Processed

File Description
*.feature BDD specs (Given, When, Then)
*.Tests/*.cs Test project source files
testplan.yaml Test coverage plan and linked features
test-blueprint.dsl DSL describing coverage and agents
resiliency/*.yaml Failure injection and chaos scenarios
test-matrix.md Optional markdown test tracking tables

🧠 Sample Memory Entry

{
  "memoryId": "mem-test-feature-create-user",
  "type": "bdd-feature",
  "projectId": "UserService.Tests",
  "tags": ["test", "bdd", "create-user", "feature"],
  "description": "SpecFlow test for user creation via HTTP with invalid email validation",
  "filePaths": ["tests/UserService.Tests/Features/CreateUser.feature"],
  "embedding": [0.167, -0.042, ...],
  "traceId": "real-time-sync-202506"
}

📌 Key Metadata Fields

Field Notes
type bdd-feature, unit-test, test-plan, etc.
tags Derived from filename, Gherkin scenario title, blueprint link
relatedFeatureId Links to a feature-blueprint memory entry
coverageType positive, negative, resiliency, edge-case, happy-path
embedding Semantic intent from scenario text or test method name
outputMetrics Optional: test pass/fail %, run history, duration

🧠 Use Cases for Retrieval

Agent Use
Test Automation Agent Generate new tests using missing coverage from blueprint
QA Engineer Agent Align test coverage with requirements, show traceability
Code Reviewer Agent Ensure new handlers have corresponding tests
Chaos Engineer Agent Retrieve test scenarios that inject RetryException
Release Coordinator Agent Verify regression coverage before production cutover

🧬 Mapping to Other Indices

Link From Link To
bdd-feature → feature-blueprint Test for specific user story
test-plan.yaml → index-blueprints Test map for one microservice
unit-test → index-codebase Verifies handler or adapter logic
resiliency-test → index-events Injects and validates behavior during message failure

🧪 Example Gherkin Snippet (Embedded)

Feature: Create User

  Scenario: Create user with invalid email
    Given a JSON payload with invalid email
    When I POST to /users
    Then the response status should be 400 Bad Request
    And the error message should contain "invalid email"

This gets embedded with intent: create user, email validation, negative test, etc.

📊 Metrics Captured per Test

Metric Description
lastRunStatus ✅ / ❌
lastRunTime Time in ms
stability Pass/fail ratio over last N runs
linkedAgent If created by agent, which one

✅ Summary

The index-tests ensures that all testable artifacts:

  • Are semantically searchable and traceable
  • Are linked to features, services, and events
  • Can be validated, regenerated, or extended by agents
  • Are treated as living specs, not static scripts

It is foundational to AI-driven quality assurance and coverage validation.

🚀 index-pipelines: CI/CD, Workflows, Release Logic

The index-pipelines captures all CI/CD pipeline templates, workflow definitions, and deployment orchestrations across the ConnectSoft ecosystem.

This index allows agents to:

  • ⚙️ Automatically generate or update build and release pipelines
  • 📦 Understand the promotion lifecycle (dev → staging → prod)
  • 🔁 Enforce consistency across microservices
  • 🔍 Validate pipeline coverage for tests, scanning, observability, and approvals

It’s the source of operational flow intelligence for the AI Factory.

📂 Types of Pipelines and Workflows

Type Description
build-pipeline Compiles, restores, tests, lints, and packs artifacts
release-pipeline Deploys to environments with gating and approvals
template-pipeline Reusable YAML templates (e.g., build-service.yaml)
infra-pipeline Deploys infrastructure (e.g., via Bicep or Terraform)
agent-workflow Pipelines for agents (e.g., VisionAgentWorkflow)
blueprint-trigger Event-triggered flow (e.g., on blueprint change)

📁 Files Processed

File Purpose
.azure-pipelines.yml Full or partial pipeline definitions
templates/*.yaml Reusable fragments
.github/workflows/*.yml GitHub Actions support (optional)
pipeline.config.json Factory-specific metadata for agents
trigger-map.yaml Events → pipeline relationships

🧠 Sample Memory Entry

{
  "memoryId": "mem-pipeline-build-microservice",
  "type": "build-pipeline",
  "projectId": "NotificationService",
  "tags": ["build", "dotnet", "nuget", "test"],
  "description": "Build pipeline with restore, lint, test, and publish stages",
  "filePaths": ["pipelines/azure-pipelines.build.yml"],
  "embedding": [0.193, -0.057, ...],
  "traceId": "bootstrap-202506"
}

🧩 Key Metadata Fields

Field Description
type build-pipeline, release-pipeline, template, etc.
agentId Can be DevOpsAgent, ReleaseCoordinatorAgent, etc.
environment dev, staging, prod, or dynamic
triggers push, PR, blueprint-event, manual, etc.
usedBy[] List of services that import this pipeline
embedding Semantic vector from YAML structure and comments

🧠 Use Cases for Retrieval

Agent Example Query
DevOps Agent “Find the latest reusable test + coverage template”
Release Coordinator Agent “What services don’t have pipeline approvals enabled?”
Observability Agent “Ensure span-export step exists in all pipelines”
Code Generator Agent “Attach publish pipeline to new microservice”

🔁 Pipeline Component Breakdown

Section Typical Tasks
restore: Uses dotnet restore, conditionally adds cache
build: Compiles and sets build number
test: Runs unit + integration tests, coverage reporting
lint: StyleCop, Roslyn analyzers
pack: NuGet packing for libraries
publish: Push to Azure Artifacts or deploy to environment
notify: Emit events or trigger GitHub/Braze notifications
trace: OpenTelemetry export hooks (optional)

🧪 Observability and Guardrails

Each pipeline can embed or inherit steps for:

  • 🟢 Health checks (/health/live, /health/ready)
  • 🔐 Security scans (dotnet verify, Trivy, Checkmarx)
  • 🧠 Semantic version bump (auto-semver, changelog emitter)
  • 📦 Artifact signing and publishing
  • 🧭 Dashboards or trace IDs for agent traceability

🧬 Example Trigger Mapping

triggerMap:
  on: blueprint-change
  do: regenerate-tests-and-redeploy
  affectedProjects:
    - AuditTrailService
    - NotificationService

✅ Summary

The index-pipelines enables agents to reason about and operate on:

  • 💡 Build and deployment automation logic
  • 🔁 Cross-service consistency and compliance
  • ⚙️ CI/CD traceability across service lifecycles
  • 🧠 Dynamic generation and adjustment of pipelines

This index ensures reliable and traceable software delivery, built into the intelligence layer of the AI Software Factory.

🏗️ index-iac: Infrastructure-as-Code (IaC), Bicep, Terraform, Provisioning

The index-iac captures all infrastructure-as-code definitions across the ConnectSoft platform. It provides AI agents with direct, semantic access to:

  • ⚙️ Bicep, Terraform, and YAML-based infrastructure scripts
  • 🌐 Network, identity, compute, and service provisioning blueprints
  • 🔁 Environment configurations for dev, test, staging, prod
  • 🧠 Agent-plannable infrastructure orchestration

This index turns infrastructure into a fully intelligent, agent-operable layer.

📂 Types of IaC Artifacts

Artifact Type Description
bicep Azure-native infrastructure definitions
terraform Cloud-agnostic infrastructure provisioning
iac-module Reusable component (e.g., aks-cluster.bicep)
env-config Environment-specific variable sets
secret-mapping Mappings to Key Vault or secret sources
infra-blueprint DSL-based infrastructure graph
deployment-strategy Blue/green, canary, region-aware strategies

📁 Files Processed

File Description
*.bicep Native Azure infra definitions
*.tf, *.tfvars Terraform modules and variable sets
infra/*.yaml Environment matrix, service topology
env/*.json Variable declarations, secret maps
infra-blueprint.dsl Agent-readable graph-style infra definitions

🧠 Sample Memory Entry

{
  "memoryId": "mem-iac-aks-cluster",
  "type": "iac-module",
  "projectId": "Infrastructure/Environments/Production",
  "tags": ["aks", "kubernetes", "cluster", "bicep", "azure"],
  "description": "Bicep module for provisioning Azure Kubernetes Service with autoscaler and monitoring enabled.",
  "filePaths": ["infra/modules/aks-cluster.bicep"],
  "embedding": [0.104, -0.053, ...],
  "traceId": "infra-bootstrap-202506"
}

🧩 Metadata Fields

Field Notes
type bicep, terraform, iac-module, infra-blueprint, etc.
tags Derived from resource types (aks, sql, vnet, keyvault)
env Dev, Test, Staging, Prod
resourceGroup, subscriptionId Optional for Azure-linked resources
embedding Semantic vector from script, metadata, and annotations

🧠 Retrieval Use Cases

Agent Example Query
DevOps Agent “Provision PostgreSQL with private endpoint and logging”
Platform Bootstrapper Agent “Deploy dev environment with App Gateway, Key Vault, Redis”
Cost Optimization Agent “Find high-cost VM SKUs across staging environments”
Observability Agent “Which IaC module deploys metrics exporters?”

🧪 Integration with Pipelines and Environments

Each indexed IaC file may link to:

  • ✅ index-pipelines: via deploy-infra.yaml or stage: infra
  • ✅ index-blueprints: maps to infra section of system design
  • ✅ index-codebase: references infrastructure-aware code (e.g., DI for connection strings)

📜 Blueprint Example (Bicep + Metadata)

module aks 'modules/aks-cluster.bicep' = {
  name: 'aksCluster'
  params: {
    name: 'connectsoft-prod-aks'
    enableMonitoring: true
    autoscaler: true
  }
}

With companion metadata:

metadata:
  environment: production
  tags: [aks, azure, cluster, monitoring]
  linkedPipelines:
    - pipelines/azure-infra-deploy.yml

💥 Security and Secrets

Agents validate use of:

  • 🔒 Key Vault references (@Microsoft.KeyVault)
  • 🔐 Secret injection in deployment pipelines
  • ✅ Role assignments and rbac.yaml files (linked to index-security-policies)

📊 Health Metrics

Metric Target
IaC modules with full metadata ≥ 90%
Linked to valid environments 100%
Blueprint-agent-parsable 100%
Agent-deployable via pipeline + blueprint Yes

✅ Summary

The index-iac turns your infrastructure into:

  • 📐 Machine-readable, retrievable blueprints
  • 🔁 Reusable modules for multi-env deployments
  • 🔒 Validated, secure, and auditable configurations
  • 🧠 Agent-operable systems with environment awareness

It forms the foundation for Infrastructure-as-Knowledge (IaK) within the AI Software Factory.

🧩 index-ui-components: Frontend Controls, Layouts, and Patterns

The index-ui-components index captures all frontend reusable components, enabling AI agents to:

  • 🧱 Reuse or extend standard UI blocks like modals, forms, lists, or cards
  • 🎨 Ensure consistency with design system and accessibility standards
  • 🧠 Discover behavior, bindings, and responsiveness logic
  • 🌍 Enable multilingual rendering and i18n-aware scaffolding

This index gives frontend-aware agents fine-grained access to the atomic design surface of the SaaS platform.

📂 Types of UI Components Indexed

Type Description
component UI widget (e.g., Modal, UserCard, Button, InputGroup)
layout Higher-order layout blocks (DashboardShell, SettingsPage)
form-control Form-bound controls with validation and binding
i18n-component Localized variant or wrapper
design-token Shared color, spacing, border-radius values
interaction-pattern Click behavior, keyboard nav, responsiveness

📁 Files Processed

File Description
.razor, .cshtml, .vue, .tsx, .jsx Component markup and logic
.razor.cs Blazor component C# backing
.scss, .css, .tailwind.config.js Styling and theming tokens
*.component.meta.yaml Optional metadata (usage, intent, props)
i18n/*.json, strings.yaml Translations, accessibility labels
*.story.js, *.story.md Storybook-like usage samples (optional)

🧠 Sample Memory Entry

{
  "memoryId": "mem-ui-component-modal",
  "type": "component",
  "projectId": "ConnectSoft.UI.Components",
  "tags": ["modal", "ui", "overlay", "accessibility", "focus-trap"],
  "description": "Re-usable modal dialog with title, body, footer, and focus trap behavior.",
  "filePaths": ["components/Modal.razor", "styles/modal.scss"],
  "embedding": [0.128, -0.089, ...],
  "traceId": "ui-bootstrap-202506"
}

🧩 Metadata Fields

Field Description
type component, layout, form-control, etc.
i18nSupport Whether localized strings exist
accessibility WCAG compliance metadata
props[] List of accepted parameters
defaultSlots[] For content projection
embedding From semantic structure and behavior
tags Visual, behavioral, and accessibility hints

📐 Design Token Awareness

All components are indexed alongside their token usage:

  • 🎨 color.primary, spacing.lg, border.radius.md
  • 📏 Responsive breakpoints
  • 🧠 Semantic themes (dark mode, contrast variant)

Agents use this to ensure pixel-perfect rendering with design system compliance.

🔍 Retrieval Examples

Agent Prompt Resolved Component
UI Developer Agent “Show a modal with form and Save button” Modal, FormGroup, PrimaryButton
Accessibility Agent “Which components lack ARIA labels?” Flagged in accessibility: false
Language Adapter Agent “Which components are missing i18n?” Filter i18nSupport: false
UX Designer Agent “Render a 2-column dashboard layout” DashboardShell with CardGrid

📎 Slot, Prop, and Interaction Mapping

Indexed components can include:

props:
  - name: isOpen
    type: boolean
    default: false
  - name: onClose
    type: EventCallback
slots:
  - header
  - body
  - footer
accessibility:
  focusTrap: true
  role: dialog

🧠 Embedded Examples

Markdown or .story.* files are embedded to guide behavior synthesis:

<Modal IsOpen="@show" OnClose="@Close">
  <Header>Confirm Deletion</Header>
  <Body>This action cannot be undone.</Body>
  <Footer>
    <Button>Cancel</Button>
    <Button Primary>Delete</Button>
  </Footer>
</Modal>

🧪 Validation and Real-Time Indexing

  • Changes to .razor, .vue, .jsx, or .meta.yaml auto-trigger updates
  • Story usage examples re-indexed for improved prompt context
  • Accessibility audits linked to index-accessibility-checks

✅ Summary

The index-ui-components enables:

  • 🧱 Reusable UI at atomic, molecule, and layout level
  • 🌍 Language- and accessibility-aware scaffolding
  • 🎨 Design system enforcement with full token traceability
  • 🤖 Semantic component generation by frontend agents

This index powers all frontend rendering and user interface logic within the AI Software Factory.

📊 index-observability: Metrics, Traces, Logs, Health, Dashboards

The index-observability provides agents with rich, structured access to all observability-related definitions and implementations across the ConnectSoft platform. It includes:

  • 📈 Telemetry standards: logs, spans, metrics
  • 📟 Health check endpoints and availability probes
  • 📊 Dashboards: Grafana, Application Insights, Prometheus, custom JSON
  • 🔍 Log enrichment fields, trace IDs, correlation headers
  • 📡 Event → span → alert mappings

This index turns operational behavior into a machine-readable and agent-actionable knowledge graph.

📂 Types of Observability Artifacts

Type Description
span-template OpenTelemetry-compliant trace templates (e.g., "SendEmail" span)
metric-definition Custom KPIs, counters, gauges, timers
log-pattern Structured log lines (Serilog, ILogger, App Insights)
health-check Live and readiness endpoints
dashboard Grafana, Azure Monitor, JSON dashboards
alert-rule Alerting and SLO thresholds
trace-map Span → event → handler relationships
log-enrichment Enriched keys: tenantId, correlationId, traceId, etc.

📁 Files Processed

File Purpose
spans/*.yaml Span definition templates
metrics/*.json, *.yaml Metric configurations
dashboards/*.json Grafana or App Insights visualizations
alerts/*.yaml Thresholds and alert strategies
.cs, .cshtml (marked) logger.Log, Activity.Start(), Measure.Timer()
healthchecks.json, health-ui.yaml Health metadata
logs/serilog.template.json Log template format

🧠 Sample Memory Entry

{
  "memoryId": "mem-span-user-registration",
  "type": "span-template",
  "projectId": "UserService",
  "tags": ["observability", "span", "trace", "registration"],
  "description": "Span for user registration with sub-steps for validate, save, notify",
  "filePaths": ["spans/UserRegistered.yaml"],
  "embedding": [0.147, -0.038, ...],
  "traceId": "observability-init-202506"
}

🧩 Key Metadata Fields

Field Description
type span-template, dashboard, metric, health-check, etc.
tags Derived from metric name, trace name, event linked
alertLevel info, warning, critical (for alert rules)
linkedBlueprints Referenced services/events (e.g., AuditTrail.LogEvent)
embedding Semantic vector for intent, component, purpose
unit For metrics (ms, %, count)
thresholds For alerts or SLOs

📡 Telemetry Graph

event: UserRegistered
producesSpan: User.Register
spanSteps:
  - ValidateEmail
  - SaveToDatabase
  - PublishEvent: UserRegistered
metrics:
  - user.registration.success.count
  - user.registration.latency.ms
logs:
  - eventId: REG_SUCCESS
    fields: [userId, tenantId, correlationId]

🧠 Agent Use Cases

Agent Task
Observability Agent Validate that all event handlers emit Activity.Start spans
QA Agent Confirm test coverage for alerts in PaymentFailed workflow
DevOps Agent Deploy dashboards linked to NotificationService
Resiliency Agent Simulate span drop for circuit-breaker logic
Release Coordinator Agent Ensure ReleaseCutSpan trace is emitted at release time

📊 Dashboards & Alerting

  • Grafana templates parsed, embedded, and stored
  • Alert rules indexed by service and environment
  • Threshold rules link to metric definitions (95th percentile latency)
  • Correlated with health checks (e.g., /health/live, /health/ready)
  • Linked to index-blueprints, index-codebase, and index-tests

🔁 Real-Time Updates

  • Any change to:
    • spans/*.yaml
    • log pattern classes
    • metrics.yaml triggers:
      • Re-indexing
      • ObservabilityChanged event
      • Test + pipeline sync updates

✅ Summary

The index-observability makes operational insight agent-operable, enabling:

  • 🔍 End-to-end span, metric, and log traceability
  • 🎯 Targeted alert tuning and test validations
  • 📊 Dashboards and SLO tracking by AI
  • 🤖 Observability-aware code and pipeline generation

This index ensures observability is not an afterthought, but a core part of AI Factory automation and feedback loops.

🧮 index-dsl: Domain-Specific Languages and Declarative Inputs

The index-dsl captures all DSL artifacts and templates used by the ConnectSoft platform to declaratively express:

  • 🧠 Software intents (services, adapters, events)
  • 🧩 Infrastructure and pipeline configuration
  • 📜 Prompt input representations for AI agents
  • 🔁 Reusable generation recipes and blueprints

This index acts as the semantic layer between human intent and generated assets in the Software Factory.

📂 Types of DSL Definitions

DSL Type Description
service.dsl High-level service declaration (DDD + clean architecture)
adapter.dsl Input/output boundary declarations (e.g., HTTP, Kafka)
event.dsl Event contract with command/event metadata
blueprint.dsl Multi-part structure for modules and flows
test.dsl Expected behavior and edge cases
pipeline.dsl Declarative CI/CD logic
prompt-template.dsl Agent-specific input prompt logic
recipe.dsl Composite reusable patterns across domains

📁 Files Processed

File Purpose
*.dsl Custom DSL files written for service/component/test
prompt-templates/*.md Prompt blueprints for Semantic Kernel
recipes/*.yaml Pattern recipes that embed DSL fragments
inputs/*.dsl.json DSL-translated structured representations
dsl.meta.yaml Optional metadata about DSL authorship, traceability

🧠 Sample Memory Entry

{
  "memoryId": "mem-dsl-adapter-notification",
  "type": "adapter.dsl",
  "projectId": "NotificationService",
  "tags": ["dsl", "adapter", "email", "http", "command"],
  "description": "DSL describing an HTTP POST adapter for sending email commands",
  "filePaths": ["dsl/adapter/email-send.dsl"],
  "embedding": [0.163, -0.021, ...],
  "traceId": "dsl-index-202506"
}

🧩 DSL Metadata Fields

Field Notes
type DSL purpose: adapter.dsl, service.dsl, etc.
tags Derived from intent (e.g., retry, command, audit)
embedding Semantically derived from DSL syntax + comments
relatedBlueprint If DSL feeds a blueprint.yaml
generatedBy Agent or user ID
targetService Name of the microservice to apply DSL to

🧠 DSL Snippet Example (Adapter)

adapter HttpEmailSend {
  method: POST
  path: /email/send
  input: SendEmailCommand
  output: AcceptedResponse
  headers:
    - x-tenant-id
    - x-correlation-id
  retryPolicy: exponentialBackoff
}

This is converted into:

  • Code adapter
  • Endpoint route
  • Command class
  • Retry policy logic
  • Span + metric mapping
  • Documentation artifact

🔍 Agent Use Cases

Agent Use
DSL Planner Agent Generate adapter DSL from feature intent
Code Generator Agent Convert DSL into C# scaffolds
Test Generator Agent Create BDD from test.dsl
Prompt Builder Agent Enrich prompt inputs using prompt-template.dsl
Infrastructure Agent Deploy infra and pipelines from pipeline.dsl

🧠 Retrieval & Matching

  • Agents perform semantic and symbolic lookup:

    • “I need a retryable Kafka consumer adapter”
    • “What is the DSL for SendAuditLog command?”
    • “What prompt template should I use for VisionArchitectAgent?”

  • DSL entries provide traceable lineage to:

    • Generated code
    • Events
    • Pipelines
    • Prompts
    • Features
Link To Reason
index-blueprints DSL usually generates or maps to blueprints
index-codebase Generated classes from DSL
index-tests BDD and testplan DSL
index-templates DSL selects appropriate generation template
index-pipelines When pipelines are DSL-generated

✅ Summary

The index-dsl gives ConnectSoft AI agents:

  • 📜 Declarative, minimal, domain-aware inputs
  • 🧠 Embedded, context-rich planning prompts
  • 🔁 Full traceability from intent → output
  • 🔄 Reusability across features, prompts, recipes, and pipelines

It is the connective DSL tissue of the AI Software Factory.

🗣️ index-prompts: Agent Prompts, Templates, and Kernel Input Logic

🧭 Purpose of the index-prompts

The index-prompts captures and embeds all prompt templates, system prompts, and assistant input configurations used by agents in the ConnectSoft AI Software Factory. It enables:

  • 🤖 Retrieval of optimal prompts per agent type or task
  • 🧠 Semantic matching of user input to prompt templates
  • 🧩 Dynamic construction of system messages and role contexts
  • 🔁 Multi-agent chaining, planning, and auto-prompting

This index powers the dialogue, instruction, and orchestration logic of every AI agent in the platform.

📂 Prompt Types Indexed

Type Description
system-prompt Base instruction set for each agent
input-prompt-template User-facing templates with interpolated variables
output-expectation Instructions for formatting replies
planner-prompt Prompt used by planners or routers
self-refinement-prompt Retry/improvement prompts
function-call-template For OpenAI function calling or semantic kernel skills
feedback-prompt Used in continuous improvement or user feedback loop

📁 Files Processed

File Description
*.prompt.md Markdown-based prompt template
prompts/*.txt Legacy or plain-text prompt instructions
*.prompt.json Structured prompt metadata
semantic-kernel/*.config.json SK planner definitions and memory references
kernel/functions/*.prompt.txt SK-native prompt functions
input-templates/*.md Agent task-specific prompt with slots ({{feature}}, {{blueprint}}, etc.)

🧠 Sample Memory Entry

{
  "memoryId": "mem-prompt-agent-release-coordinator",
  "type": "system-prompt",
  "agentId": "ReleaseCoordinatorAgent",
  "tags": ["release", "agent", "strategy", "slo", "cutover"],
  "description": "Initial system prompt for coordinating release plans and validation logic",
  "filePaths": ["prompts/ReleaseCoordinator.prompt.md"],
  "embedding": [0.204, -0.063, ...],
  "traceId": "agent-bootstrap-202506"
}

📌 Metadata Fields

Field Description
type system-prompt, input-template, etc.
agentId The agent using this prompt
language Optional: language tag or localization group
embedding Semantic intent embedding
variables[] Interpolated keys used in input construction
skillsUsed[] Kernel skills invoked via function-calling
triggers[] Agent task triggers using this prompt
linkedMemory Other memory references (e.g. dsl, blueprint, docs)

🧠 Prompt Example Snippet

## System Prompt (for Product Manager Agent)

You are a highly skilled AI Product Manager in the ConnectSoft AI Software Factory. Your role is to:
- Interpret user feature descriptions
- Break them into clear user stories and acceptance criteria
- Suggest minimal MVP increments
- Identify dependencies and blockers
Always format output as a Markdown table. Always tag stories with feature IDs.

🔍 Retrieval Scenarios

Agent Query
Planner Agent “What’s the prompt for AdapterDSLGenerator?”
QA Agent “Retry prompt for incomplete test plan generation?”
Vision Architect Agent “Prompt template for strategic goal alignment?”
UX Designer Agent “System prompt for layout suggestions in dark mode?”

🔁 Real-Time Update & Evolution

  • When prompts change:
    • Re-indexed immediately
    • PromptChanged event emitted
    • Planning agents reevaluate routes
    • Feedback loop auto-attached if in experiment mode
  • Prompts versioned and linked to index-agents and index-blueprints

🧠 Prompt Interpolation Structure

{
  "template": "Generate event handlers for the following:\n{{eventBlueprint}}\n\nUse DDD folder structure and include observability.",
  "variables": ["eventBlueprint"],
  "outputExpectation": "Code + Markdown summary",
  "language": "en-US"
}
Index Purpose
index-dsl Prompt templates often refer to dsl.service, dsl.adapter, etc.
index-agents Every agent references one or more prompt sets
index-tests Prompt-to-test generation mapping
index-blueprints Prompts often embed full or partial blueprint input

✅ Summary

The index-prompts gives ConnectSoft agents:

  • 🧠 Consistent cognitive grounding for every agent action
  • 🔄 Dynamic routing and re-prompting logic
  • 📥 Semantically aligned, slot-aware user input templates
  • 🤖 Multi-agent orchestration via self-prompted chains

It forms the instructional and linguistic intelligence layer of the AI Software Factory.

📚 index-docs: Documentation Sites, Markdown, and MKDocs Repos

The index-docs provides a semantic, agent-searchable index of all Markdown-based documentation, technical wikis, and platform knowledge authored or generated within the ConnectSoft ecosystem. This includes:

  • 🧭 MkDocs-based documentation sites (architecture, usage, policies)
  • 🛠️ Internal dev documentation in repos
  • 📄 Rich markdown documents from agents (blueprints, runbooks, changelogs)
  • 🔁 Links to prompt systems and memory graphs for traceable AI-generated docs

It ensures that all platform knowledge is accessible, version-aware, and agent-operable.

📂 Types of Documentation Indexed

Type Description
mkdocs-page Rendered documentation page in MkDocs
markdown-spec Markdown file from /docs, /blueprints, /wikis, etc.
agent-generated-doc Prompt output from blueprinting or planning agents
changelog.md Release or version documentation
readme.md Project-level root documentation
doc-template Reusable structure (e.g., architecture-overview.md)

📁 Files Processed

File Purpose
docs/**/*.md Project or platform-level documentation
mkdocs.yml Defines page tree, nav, site links
*.template.md Templates for doc generation
generated/*.md AI-created feature maps, test reports
wikis/**/*.md Feature breakdowns, DevOps playbooks
changelog/*.md Historical release and feature logs

🧠 Sample Memory Entry

{
  "memoryId": "mem-doc-blueprint-architecture-overview",
  "type": "markdown-spec",
  "projectId": "NotificationService",
  "tags": ["blueprint", "architecture", "overview", "docs"],
  "description": "High-level Clean Architecture overview for NotificationService, includes layers, events, modules",
  "filePaths": ["docs/blueprints/NotificationService/architecture-overview.md"],
  "embedding": [0.211, -0.066, ...],
  "traceId": "docgen-202506"
}

🧩 Metadata Fields

Field Description
type mkdocs-page, readme.md, agent-generated-doc, etc.
siteId Optional: for cross-site linking (e.g., platform-docs, auth-docs)
tags Derived from headings and taxonomy
embedding Semantic vector from section headers and content
sourceAgent If created via AI generation
navPath Logical page path within documentation site

📘 Example Structure (for MkDocs)

nav:
  - Home: index.md
  - Architecture:
      - Overview: architecture/overview.md
      - Event-Driven Mindset: architecture/event-driven.md
  - Templates:
      - Microservice Template: templates/microservice-template.md

Each page is indexed individually with nav context and hierarchical depth.

🔍 Retrieval Examples

Agent Prompt Result
Blueprint Agent “Find architecture guide for NotificationService” /blueprints/NotificationService/architecture-overview.md
QA Agent “Link to feature specification of RetryPolicy” docs/features/retry-policy.md
Observability Agent “Docs for span injection strategy” observability/spans.md
Product Manager Agent “Find changelog describing MVP scope cutback” changelog/2025-04-mvp-trim.md

🔁 Real-Time Update Flow

  • Any .md file added or changed:
    • Parsed for #, ## headers
    • Extracts nav structure and file path context
    • Indexed and embedded semantically
    • Emits DocumentationChanged event

Each document may reference:

  • 🔗 index-blueprints (for referenced feature maps)
  • 🤖 index-agents (for ownership or traceability)
  • 📎 index-prompts (for prompt source if agent-generated)
  • 🧱 index-components, index-events, index-tests for inline code/snippet links

✅ Summary

The index-docs enables:

  • 🔍 Semantic retrieval of all knowledge authored in markdown
  • 🧠 Reasoning and reuse of AI-generated documentation
  • 📚 Nav-aware awareness of doc structure and linkage
  • 🔁 Continuous knowledge graph construction for ConnectSoft

It forms the core knowledge archive and memory index for all textual design artifacts.

🔐 index-security-policies: Security Rules, Trust Boundaries, and Access Controls

The index-security-policies stores all security-related artifacts that define:

  • 🔐 Authentication, authorization, RBAC, and ABAC configurations
  • 🛡️ Trust boundaries between internal, external, sandboxed components
  • 🧱 Field-level data masking, encryption-at-rest policies
  • 🧠 Agent-accessible access control graphs
  • 🔁 Token scope definitions and API security schemas

It equips agents to design, validate, and enforce secure-by-default architectures in every software product.

📂 Types of Security Artifacts Indexed

Type Description
rbac-policy Role-based access control configuration
scope-definition API access scopes and permissions
field-security Masking, redaction, and PII annotations
trust-boundary System boundary classifications
oauth-config OAuth2/OpenID Connect config mappings
jwt-claims-map Token claims to feature permissions
security-blueprint Agent-readable security contract file
audit-policy Event log, trail, or immutable store requirements

📁 Files Processed

File Purpose
security-policies/*.yaml Policy definitions per service/domain
oauth/config.json OIDC or client credential mappings
scopes/*.json, scopes/*.yaml API scopes and descriptions
claims-map.yaml Maps from JWT to authorization permissions
field-retention-map.yaml Field classification: sensitive, transient, encrypted
trust-zones.md Documentation for boundaries and threat models
security-blueprint.dsl Structured policy definitions for agents

🧠 Sample Memory Entry

{
  "memoryId": "mem-security-policy-user-read-scope",
  "type": "scope-definition",
  "projectId": "UserService",
  "tags": ["security", "scope", "read", "user"],
  "description": "OAuth2 scope `user.read` allowing read-only access to user profiles",
  "filePaths": ["scopes/user.read.yaml"],
  "embedding": [0.199, -0.051, ...],
  "traceId": "security-202506"
}

🧩 Metadata Fields

Field Description
type rbac-policy, scope-definition, trust-boundary, etc.
tags Roles, scopes, sensitive fields
affectedServices Targeted microservices or APIs
relatedEvent For audit-linked security actions
embedding Vector from policy description and tags
classification e.g., PII, internal, public, restricted

🔍 Retrieval Examples

Agent Query
Auth Agent “Find scopes for user profile read/write”
Gateway Agent “Retrieve all OIDC scopes for NotificationService”
Code Generator Agent “What fields in User need masking or encryption?”
Product Owner Agent “What roles have access to Billing Reports?”
DevSecOps Agent “Validate services crossing untrusted boundaries”

🔐 Sample Policy Snippet

scope: user.read
description: Grants read-only access to user profiles
roles:
  - UserViewer
  - Admin
endpoints:
  - GET /api/users
  - GET /api/users/{id}

And associated field-level rule:

field: ssn
type: string
security:
  classification: PII
  access: AdminOnly
  masking: last4
  encryption: at-rest

🔄 Real-Time Triggers

Changes in:

  • *.scope.yaml, field-retention-map.yaml, trust-boundary.md
  • OAuth/OIDC discovery (.well-known/openid-configuration)
  • RBAC/ABAC configs in YAML/DSL

Trigger:

  • Re-indexing
  • Validation by SecurityOrchestratorAgent
  • Documentation updates (index-docs)
  • Retesting of index-tests for security scenarios

🔐 Security Boundaries & Agent Awareness

Agents are boundary-aware and can reason about:

  • 📦 Internal vs. External systems
  • 🧪 Sandbox vs. Production safety layers
  • 🔄 Token scope propagation and claims translation
  • 🔍 Audit trail requirements per endpoint/action

✅ Summary

The index-security-policies enables:

  • 🔐 Secure generation of APIs, handlers, and interfaces
  • 🧠 Agent-enforced field-level and endpoint-level authorization
  • 🎯 Traceable mappings of role, permission, token, and field
  • 🔁 AI-driven security audits, test generation, and blueprinting

It ensures that security is embedded into every part of the AI Factory, not bolted on.

📨 index-events: Domain Events, Contracts, Topics, and Event-Driven Architecture

The index-events stores and embeds all event-driven artifacts used within the ConnectSoft platform, allowing agents to:

  • 🔁 Generate or consume events across bounded contexts
  • 📦 Understand message schemas, payloads, and metadata
  • 🔍 Discover service-to-service communication links
  • 🔐 Trace security, observability, and retry policies per event

It forms the event mesh knowledge graph of the AI Software Factory, powering reactive and decoupled system design.

📂 Types of Event Artifacts Indexed

Type Description
domain-event Event raised by a domain aggregate (e.g. UserRegistered)
integration-event Cross-context message (e.g. InvoicePaid)
command Explicit intent to trigger behavior (e.g. SendEmailCommand)
topic Messaging topic or routing key (Kafka, Azure Service Bus, etc.)
event-contract Full schema definition (JSON Schema, C#, Avro, Protobuf)
event-map Maps source → sink relationships
event-handler-spec DSL/metadata about handling logic or retries

📁 Files Processed

File Purpose
events/*.cs, *.proto, *.avsc, *.json Contract source code
event-specs/*.yaml DSL-style specs (schema + metadata + topic)
event-maps/*.yaml Source-sink flow definitions
handlers/*.dsl Adapter/consumer definitions
observability/*.span.yaml Related spans
test-cases/*.event.feature BDD for event flows

🧠 Sample Memory Entry

{
  "memoryId": "mem-event-user-registered",
  "type": "domain-event",
  "projectId": "UserService",
  "tags": ["event", "user", "domain", "registration", "email"],
  "description": "Raised when a new user completes registration. Includes userId, timestamp, and email.",
  "filePaths": ["events/UserRegistered.cs", "event-specs/user-registered.yaml"],
  "embedding": [0.142, -0.034, ...],
  "traceId": "event-bus-index-202506"
}

🧩 Key Metadata Fields

Field Description
type domain-event, integration-event, command
topic Messaging bus topic or route
producerService The service that emits this event
consumerServices[] All services that consume it
schemaType json, avro, protobuf, csharp-class
linkedSpans[] Trace spans emitted during publish/handle
retries, deadLetter, idempotency Event delivery policies
embedding Semantic vector of payload and context

🔁 Sample Event Spec (YAML)

event: UserRegistered
source: UserService
topic: users.user-registered
payload:
  - userId: Guid
  - email: string
  - createdAt: datetime
metadata:
  retries: 3
  traceSpan: User.Register
  correlationId: required

🔍 Retrieval Examples

Agent Query
Blueprint Agent “What events are produced by UserService?”
Observability Agent “Which spans are linked to OrderCreated?”
QA Agent “Find test scenarios for InvoicePaid event”
DevOps Agent “What services consume NotificationDispatched?”
Code Generator Agent “Generate handler for PasswordResetRequested”

🧬 Event Flow Graph (Traceable)

event: EmailDispatched
source: NotificationService
consumers:
  - AuditTrailService (logs delivery result)
  - AnalyticsService (captures email engagement metrics)
topic: notification.email.dispatched

Visual flow is generated and linked to index-blueprints.

🔐 Security & Observability Mapping

Each event is linked to:

  • 🛡️ index-security-policies: PII classification, masking
  • 📡 index-observability: Span name, retry path, metrics
  • ✅ index-tests: Event-based BDD specs
  • 🧱 index-codebase: Producer/consumer adapters

✅ Summary

The index-events enables:

  • 🧠 AI-native reasoning over distributed system flows
  • 🔁 Reactive, event-driven modeling by agents
  • 📦 Schema-aligned message generation and handler logic
  • 🔍 Traceability across services, commands, topics, and actions

It is the event intelligence layer of the AI Software Factory.

🧪 index-tests: BDD, Unit Tests, Coverage, and Test Artifacts

The index-tests gives AI agents full visibility into the testing strategy, quality coverage, and validation artifacts of the platform. It enables:

  • ✅ Test discovery and traceability to features, services, and events
  • 🧠 Generation and refinement of test cases from DSLs or blueprints
  • 🔁 Coverage-aware development, alerting on missing validations
  • 🧪 Behavioral verification for agents generating new code or contracts

This index supports Test-First Thinking and drives agent-based quality assurance across all SDLC stages.

📂 Types of Test Artifacts Indexed

Type Description
bdd-feature Gherkin-style .feature files
unit-test Test method or class for functional logic
integration-test End-to-end or service-to-service tests
contract-test Consumer/provider verification for event/API contracts
load-test-script Load profiles and simulation patterns
chaos-test Resiliency assertions under injected faults
test-report Output summary with coverage/metrics
assertion-map Blueprint-to-test mapping (e.g., Given > handler > Then)

📁 Files Processed

File Purpose
*.feature Gherkin test specifications
*.specflow.cs Step definitions in C#
*.Tests.cs, *.Unit.cs MSTest/NUnit/XUnit tests
contract-tests/*.json Pact, OpenAPI verifier specs
loadtests/*.yaml Artillery, k6, Azure Load Testing
chaos/*.dsl Failure simulation definitions
TestReports/*.json, coverage.* Code coverage and metrics
assertion-map.yaml Maps features to testable blueprints

🧠 Sample Memory Entry

{
  "memoryId": "mem-test-feature-user-registration",
  "type": "bdd-feature",
  "projectId": "UserService",
  "tags": ["test", "bdd", "registration", "feature"],
  "description": "BDD test for user registration flow with success, failure, and email confirmation branches",
  "filePaths": ["tests/features/UserRegistration.feature"],
  "embedding": [0.119, -0.087, ...],
  "traceId": "qa-blueprint-202506"
}

🧩 Metadata Fields

Field Description
type bdd-feature, unit-test, load-test, contract-test, etc.
linkedBlueprint Associated blueprint, feature, or DSL
tags Features, modules, test types
coverageScope Function, API, service, event
executionStatus passing, failing, skipped
embedding Semantic vector of test purpose and behavior

🧠 Example BDD Snippet

Feature: User Registration

  Scenario: Successful Registration
    Given a valid new user request
    When the user submits the registration form
    Then a UserRegistered event is emitted
    And the user receives a confirmation email

Traceable to:

  • SendEmailCommand
  • UserRegistered span
  • NotificationService blueprint

🔍 Agent Use Cases

Agent Task
QA Agent Generate missing BDD tests for BillingService
Code Generator Agent Validate OrderConfirmedHandler logic is covered
Chaos Agent Link tests that validate retry under DatabaseTimeout
Observability Agent Assert span emission per test outcome
Release Coordinator Ensure all regression tests pass before cutover

🧪 Coverage Linkage

Each test artifact links to:

  • ✅ index-blueprints (feature → test assertion map)
  • ✅ index-events (event triggers verified)
  • ✅ index-pipelines (pipeline-stage test execution)
  • ✅ index-prompts (test-generation prompts)
  • ✅ index-docs (test summary pages and changelogs)

🔁 Real-Time Indexing

  • Every new .feature, .Tests.cs, or report triggers:
    • Re-indexing
    • Test plan linkage validation
    • TestAdded or TestFailed event for observability

✅ Summary

The index-tests enables:

  • 🧠 Traceable, agent-reasonable test coverage
  • ✅ Verification-aware prompt planning and code generation
  • 🔁 Continuous quality gating via automated assertions
  • 🧪 Cross-agent and cross-domain behavior validation

It transforms testing into a core memory layer of the autonomous Software Factory.

🚀 index-pipelines: CI/CD Templates, Stages, Agents, and Environments

The index-pipelines stores, indexes, and semantically maps all CI/CD pipelines, YAML templates, automation scripts, and build/test/deploy definitions across the ConnectSoft ecosystem. This enables:

  • ⚙️ AI generation of end-to-end deployment pipelines
  • 🧪 Validation of test and coverage enforcement in CI stages
  • 🔁 Continuous integration and delivery automation across services
  • 🧠 Reusable pipeline stages and templates across projects

It equips agents with infrastructure-as-code knowledge to automate, validate, and evolve build-release flows.

📂 Types of Pipeline Artifacts Indexed

Type Description
pipeline-definition Main YAML or JSON pipeline file
template-stage Shared reusable pipeline step/stage
pre-build-step Linting, restore, secrets, init
test-stage Unit, integration, or BDD test steps
deploy-stage Release definition: environment, slot, region
release-definition High-level multi-service cutover flow
infra-pipeline IaC deployment: Bicep, Terraform, ARM
agent-pool-map Maps jobs to runner types or hosted agents

📁 Files Processed

File Purpose
azure-pipelines.yml, *.pipeline.yaml Top-level build/release pipelines
.ado/templates/*.yaml Reusable job/stage templates
deploy/bicep/**/*.bicep Deployment scripts linked to pipelines
infra/*.tf, main.bicep Infra pipeline targets
pipeline-docs/*.md Documentation and runbooks
pipeline-metadata.json Structured representation for agents

🧠 Sample Memory Entry

{
  "memoryId": "mem-pipeline-build-notification-service",
  "type": "pipeline-definition",
  "projectId": "NotificationService",
  "tags": ["pipeline", "ci", "build", "deploy"],
  "description": "Azure DevOps pipeline for building and deploying the NotificationService, with template reuse for test + deploy stages.",
  "filePaths": ["azure-pipelines.yml"],
  "embedding": [0.097, -0.112, ...],
  "traceId": "pipeline-index-202506"
}

🧩 Metadata Fields

Field Description
type pipeline-definition, template-stage, etc.
projectId Source project/service
agentPool Azure DevOps agent pool used
environments[] Dev, QA, Staging, Production
linkedBlueprint Feature or microservice triggering this pipeline
embedding Semantic embedding of pipeline tasks and structure

📘 Sample Structure (Pipeline Template)

stages:
  - stage: Build
    jobs:
      - job: BuildJob
        steps:
          - task: DotNetRestore
          - task: DotNetBuild
  - stage: Test
    jobs:
      - job: TestJob
        steps:
          - task: DotNetTest
          - task: PublishTestResults
  - stage: Deploy
    condition: succeeded()
    jobs:
      - deployment: DeployToQA
        environment: qa

Agents parse these YAMLs into prompt-aware logical plans.

🔍 Retrieval Scenarios

Agent Task
DevOps Agent “Find deploy template for microservice with GRPC + Redis”
QA Agent “Does this pipeline run SpecFlow and report code coverage?”
Infrastructure Agent “Trigger infra pipeline after successful build”
Release Coordinator “Cutover template for blue/green deploy in prod?”
CI Optimizer Agent “Suggest parallelization for test matrix”

🔁 Real-Time Update Handling

  • When a pipeline YAML or template is updated:
    • Semantic re-indexing occurs
    • PipelineChanged event is published
    • Impacts:
      • index-tests: test hooks
      • index-blueprints: linked features
      • index-observability: span + metrics trace
      • index-security-policies: secrets, tokens

🔄 Agent-Aware Execution Mapping

Each pipeline is mapped to:

  • Build → Test → Lint → Security Scan → Deploy → Notify
  • Environment-specific variables
  • Agent pool and parallelization strategy
  • Linked services, domains, and modules

Agents can reason, suggest, or refactor pipelines using this structure.

✅ Summary

The index-pipelines enables:

  • 🚀 Autonomous CI/CD planning and code-pipeline co-generation
  • 🧪 Test visibility and enforcement inside delivery flows
  • 🔄 Reusable build/deploy logic across 3000+ services
  • 🧠 Cross-agent infrastructure and observability traceability

It is the deployment nervous system of the AI Software Factory.

🔗 index-integrations: External APIs, SaaS Tools, SDKs, and Connectors

The index-integrations collects and maps all external system interfaces that ConnectSoft SaaS platforms integrate with, including:

  • 🌐 Third-party SaaS APIs (e.g., Stripe, Twilio, Segment, Braze)
  • 🔌 SDKs and platform-specific connectors
  • 🧠 Integration blueprints and adapter DSLs
  • 🔄 Webhook, polling, or API-based data sync patterns
  • 🔑 OAuth/OpenID scopes, tokens, and secrets for each integration

It enables agents to orchestrate integrations at design time, validate flows, and automate secure and scalable external connectivity.

📂 Types of Integration Artifacts Indexed

Type Description
integration-definition Full integration metadata (target, auth, retries, endpoints)
api-adapter Connector class, HTTP client, SDK interface
webhook-consumer Listener and parser for incoming webhook
integration-test Contract verification or simulated API test
oauth-config Token, refresh, client credentials
sync-job Recurring or event-triggered sync config
integration-pipeline CI/CD step to validate or deploy integration logic

📁 Files Processed

File Purpose
integrations/**/*.yaml, *.json Integration manifest files
adapters/**/*.cs, *.ts SDK wrappers or HTTP clients
webhooks/*.dsl, *.handler.cs Webhook handler logic
oauth/*.json OAuth2 token discovery or secrets
test/integration/*.spec.cs Contract tests for external APIs
integration-docs/*.md Documentation and runbooks

🧠 Sample Memory Entry

{
  "memoryId": "mem-integration-braze-api",
  "type": "integration-definition",
  "integrationName": "Braze",
  "tags": ["crm", "api", "braze", "http", "segment"],
  "description": "Braze user profile sync via HTTP API with batch track and identify methods",
  "filePaths": ["integrations/braze/braze.api.yaml"],
  "embedding": [0.176, -0.044, ...],
  "traceId": "integration-202506"
}

🧩 Metadata Fields

Field Description
integrationName Name of external platform
authType OAuth2, API Key, None, HMAC
endpoints[] API URLs used
linkedAdapters[] SDKs or wrapper classes
webhookEndpoints[] For inbound integrations
dataSchemas[] JSON or object schema linked to sync
retryPolicy Backoff, circuit breaker, timeout configs
embedding Semantic representation of integration purpose and flow

🔌 Sample Integration YAML

integration: Stripe
auth:
  type: OAuth2
  scopes:
    - charges.read
    - customers.write
endpoints:
  - POST /v1/customers
  - GET /v1/charges
retry:
  policy: exponentialBackoff
  attempts: 3
handlers:
  - OnPaymentSucceeded
  - OnCustomerUpdated

🔍 Retrieval Examples

Agent Task
Integration Agent “How do I connect to Segment API?”
Product Manager Agent “Find integration with Mailchimp for marketing tags”
QA Agent “Which test validates retry logic for CallRailWebhookHandler?”
Pipeline Agent “Include integration tests for Mixpanel before deploy”

🔁 Real-Time Triggers

  • New integration or modified endpoint → triggers:
    • IntegrationChanged event
    • Security + observability validation
    • Blueprint relinking (e.g., EventUserCreated → Braze → Segment)
    • Prompt and DSL regeneration if agent-driven

🤖 Agent Reasoning with Integration Index

  • Maps integration usage across features
  • Resolves SDKs and APIs to appropriate adapters
  • Enforces token and credential flows
  • Validates webhook and event sync reliability
  • Generates automated data transformation and sync jobs

✅ Summary

The index-integrations allows ConnectSoft to:

  • 🌐 Seamlessly plug into 100+ SaaS APIs
  • 🧠 Generate, test, and manage external connectivity
  • 🔁 Trace sync jobs, secrets, retry logic, and SDK use
  • 🤖 Automate generation of adapters, tests, and pipelines

It enables agents to handle external ecosystem interactions autonomously, as a core part of full SaaS delivery.

🧩 #index-ui-components: Reusable Frontend Elements, Patterns, and Style Contracts

The index-ui-components index provides semantic access to all reusable UI components, layout patterns, design tokens, and accessibility-aware elements across ConnectSoft projects. This empowers agents to:

  • 🧱 Generate accessible, consistent UIs using design system constraints
  • 🎨 Align frontend code with UX rules, themes, and ARIA contracts
  • 🔁 Suggest component reuse during UI feature planning
  • 🤖 Collaborate with UI/UX agents to build full flows

It serves as the visual and interaction memory graph of the Software Factory’s frontend layer.

📂 Types of UI Artifacts Indexed

Type Description
component Atomic or composite UI building block
layout-pattern Grids, navigation bars, dashboard panels
design-token Color, font, spacing, shadows
accessibility-hook ARIA, screen reader, contrast compliance logic
style-guide-rule Branding, spacing, naming conventions
storybook-entry Sample or documented component usage
ui-blueprint Agent-authored UI feature or screen spec

📁 Files Processed

File Purpose
components/**/*.razor, *.tsx, *.vue UI component source files
tokens/*.json Design system definitions
styleguide/*.md, design-rules.yaml Theming and layout guidance
storybook/**/*.stories.* Preview and doc metadata
ui-specs/*.dsl AI- or UX-authored blueprints
accessibility/*.lint.yaml ARIA/keyboard/navigation rules

🧠 Sample Memory Entry

{
  "memoryId": "mem-ui-component-button-primary",
  "type": "component",
  "componentId": "ButtonPrimary",
  "tags": ["button", "ui", "component", "accessible", "clickable"],
  "description": "Primary action button component with ARIA roles, hover/focus states, and dark mode support.",
  "filePaths": ["components/shared/ButtonPrimary.razor"],
  "embedding": [0.145, -0.039, ...],
  "traceId": "ui-catalog-202506"
}

🧩 Metadata Fields

Field Description
componentId Unique logical name
type component, layout-pattern, token, storybook-entry
tags[] Visual and semantic cues (e.g., modal, input, contrast)
linkedStories[] Storybook usage examples
accessibilityScore Optional field from auto-lint tools
embedding Semantic representation of purpose and props
usedInFeatures[] Downstream blueprints or UI flows using it

📘 Sample Component Reference (Razor)

<ButtonPrimary OnClick="@HandleSave" AriaLabel="Save form">
    Save
</ButtonPrimary>

Linked to:

  • Design token: button.primary.backgroundColor
  • Accessibility contract: aria-label, role=button

🔍 Retrieval Examples

Agent Task
UI Designer Agent “Suggest input components with inline validation”
Frontend Developer Agent “What card patterns are available for dashboard tiles?”
Accessibility Agent “Which components support keyboard navigation?”
Code Reviewer Agent “Validate reused components in SettingsScreen.razor”

🧠 Agent Design Flows

Agents use this index to:

  • Generate UI fragments aligned with factory design
  • Resolve style + layout constraints
  • Inject accessibility annotations
  • Enforce branding tokens and naming conventions
  • Plan UI test cases (index-tests) based on layout paths

🔁 Real-Time Update Handling

  • Component change triggers:
    • Re-indexing by UIRegistryAgent
    • Embedding regeneration
    • ARIA validation scan (optional)
    • Event: ComponentChanged → Blueprint agents replan

✅ Summary

The index-ui-components enables:

  • 🎨 Design-consistent frontend generation across agents
  • ♿ Accessibility-compliant component reuse
  • 🧠 Semantic understanding of UI feature needs
  • 🧩 Modular UI construction aligned with DDD + clean architecture

It forms the visual construction kit of the AI Software Factory.

📡 index-observability: Spans, Metrics, Dashboards, and Logs

The index-observability builds a structured, agent-readable view of the entire observability layer across ConnectSoft microservices and pipelines. It captures:

  • 📍 Distributed tracing spans and instrumentation contracts
  • 📊 Metrics definitions, aggregation logic, and thresholds
  • 📈 Grafana dashboards and Prometheus query templates
  • 🧾 Structured logging schemas and correlation formats
  • 🚦 Alerts, SLOs, and health check rules

It enables AI agents to validate, generate, and connect observability across code, deployments, and business workflows.

📂 Types of Observability Artifacts Indexed

Type Description
trace-span OpenTelemetry or custom tracing span spec
metric-definition Gauge, counter, histogram with tags
dashboard Grafana JSON or YAML layouts
log-format Structured log line schema
alert-rule Threshold-based alert spec
health-check Readiness/liveness or custom endpoint validator
slo-policy Uptime, latency, or error rate targets

📁 Files Processed

File Purpose
observability/spans/*.yaml Span names, inputs, trace propagation
metrics/*.prom, *.yaml Metric definitions and queries
dashboards/*.json, *.yml Grafana or dashboard templates
logs/*.schema.json Log formats for service/subsystem
alerts/*.yaml AlertManager rules
slo/*.policy.yaml Error budgets and thresholds
health-checks/*.cs Endpoint status validators

🧠 Sample Memory Entry

{
  "memoryId": "mem-span-user-registration",
  "type": "trace-span",
  "serviceId": "UserService",
  "tags": ["span", "tracing", "user", "registration"],
  "description": "Span emitted during user registration with context propagation and log enrichment.",
  "filePaths": ["observability/spans/UserRegistered.yaml"],
  "embedding": [0.136, -0.077, ...],
  "traceId": "obs-index-202506"
}

🧩 Metadata Fields

Field Description
type trace-span, metric-definition, dashboard, etc.
serviceId Service emitting span or metric
embedding Semantic fingerprint of observability logic
linkedFeature Feature or blueprint producing signal
alertSeverity If part of alert rule: info, warning, critical
dashboardId Link to dashboard view if applicable

📘 Sample Span Spec

span: User.Register
description: Traces full lifecycle of user registration
attributes:
  - userId
  - method: POST
  - outcome: success | failure
logs:
  - timestamp
  - event: UserCreated
  - event: EmailSent

🔍 Retrieval Examples

Agent Task
Observability Agent “Which spans exist for NotificationService?”
QA Agent “Does OrderCreatedHandler emit trace + log?”
Performance Agent “What histogram metrics track latency for InvoiceService?”
Release Coordinator “Which SLO must pass before prod deploy?”
Target Index Usage
index-events Ensure span is tied to emitted/handled event
index-tests Confirm test spans match real-time metrics
index-blueprints Feature trace graph auto-generated
index-pipelines Pre/post deploy observability gates

🔁 Real-Time Ingestion

  • New span or dashboard added:
    • Triggers ObservabilityChanged event
    • Validated by observability agent cluster
    • Linked to deployment logic and agent workflows

📊 Dashboards and Alerts

  • All *.json dashboards semantically embedded
  • Metric + label indexing supports dynamic queries
  • Alert thresholds extract severity, owner, linked feature

✅ Summary

The index-observability enables:

  • 📡 Complete tracing of user + system behavior
  • 🧠 Observability-aware agent generation (e.g., logging, alerts)
  • 🚦 Deployment gating by health and telemetry
  • 📊 Metric reasoning, visualization, and cost-aware testing

It is the telemetry nervous system of the AI Software Factory.

🤖 index-agent-skills: Semantic Kernel Skills, Plugins, and Agent Capabilities

The index-agent-skills provides a semantic and functional map of all capabilities an agent can invoke, including:

  • 🧠 Semantic Kernel plugins and functions
  • 🔌 Tool usage (e.g., HTTP, Azure DevOps, code synthesis, CLI)
  • 🗣️ Prompt engineering and rewriting utilities
  • 🧰 Internal DSL interpreters, validators, and transpilers
  • 🧱 Domain-specific AI tools or adapters

This index allows the Software Factory to compose agents from a graph of skills, enable intelligent routing, discover missing skills, and perform automated agent optimization.

📂 Types of Skills Indexed

Type Description
semantic-skill Natural language-based functions (prompt-based or embedding-enhanced)
function-skill Native code plugin callable as a Kernel function
tool-skill Wraps external tools, APIs, or commands
validation-skill Used to post-process or validate agent outputs
meta-skill Prompt restructuring, summarization, intent detection
interaction-skill Designed for direct user/agent communication
composition-skill Orchestrates other skills or agents

📁 Files and Sources

File Type Examples
skills/**/*.skprompt.txt Prompt templates used by Semantic Kernel
skills/*.cs, *.js Skill implementation logic
plugin-manifests/*.json Plugin registry files
agent-descriptors/*.yaml Maps agent to available skills
function-index/*.json Auto-generated function metadata
prompt-functions/*.md Documentation of input/output structure

🧠 Sample Memory Entry

{
  "memoryId": "mem-skill-azuredevops-create-wiki",
  "type": "tool-skill",
  "agentId": "DocumentationWriterAgent",
  "tags": ["azuredevops", "wiki", "docs", "automation"],
  "description": "Creates or updates a markdown wiki page in a given Azure DevOps project.",
  "filePaths": ["skills/AzureDevOps/CreateWiki.cs"],
  "embedding": [0.101, -0.022, ...],
  "traceId": "skill-index-202506"
}

🧩 Metadata Fields

Field Description
agentId Agent using the skill
skillId Unique logical name
type semantic-skill, tool-skill, etc.
inputSchema, outputSchema Optional structured definitions
embedding Semantic understanding of capability
sourceFile Plugin, script, or template source
usedInProjects[] Where this skill was used during generation

📘 Sample Function Declaration

[KernelFunction]
public async Task<SkillResult> CreateRepositoryAsync(string org, string project, string repoName)

Paired with prompt:

Write a REST API call to create a repository using Azure DevOps API.

🔍 Retrieval Examples

Agent Task
OrchestratorAgent “What skills can handle markdown to OpenAPI transformation?”
Vision Architect Agent “Show all UI sketching skills available for frontend flows”
Blueprint Agent “Suggest skills for converting DSL to event contracts”
Validation Agent “Run FieldRedactorValidatorSkill on User schema”

🔁 Real-Time Ingestion

  • New skills added:
    • Registered in plugin manifest or declared with [KernelFunction]
    • Automatically indexed and embedded
    • Linked to:
      • index-codebase for physical source
      • index-prompts if prompt-based
      • index-agents via descriptor file

🔗 Composability & Optimization

The index enables:

  • 📚 Dynamic skill discovery during task planning
  • 🧩 Agent self-reflection: “What can I do?”
  • 🔄 Feedback loop on skill success/failure
  • 🪄 Runtime chaining or replacement of skills by planner agent

✅ Summary

The index-agent-skills provides:

  • 🔎 Intelligent discovery of agent capabilities
  • 🧠 Semantic fingerprinting of all agent tools and plugins
  • 🔁 Observability-aware execution and logging of agent functions
  • 🔌 Reusable, swappable skill libraries across personas

It forms the actionable capability graph of the Agentic AI Software Factory.

🧠 index-memory-graphs: Agent Memories, Embeddings, Traces, and Long-Term Storage

The index-memory-graphs captures the structural, temporal, and semantic layout of all memories generated, updated, or referenced by agents during software generation. It includes:

  • 🗂️ Embedding memory stores (vector DBs)
  • 🕸️ Traceable links between memories (e.g., "UserService → EmailSent span → prompt → test → log")
  • 📍 Memory scopes: short-term (context), long-term (vector store), persistent (blob/table)
  • 🔄 Memory update policies and TTLs
  • 🔄 Memory-sharing graphs across agents, prompts, and tools

It allows agents to retrieve, reason, update, and share knowledge across cycles, projects, and personas.

📂 Types of Memory Entities Indexed

Type Description
embedding-store Vector DB storage for long-term semantic memories
conversation-context In-memory or session-bound token contexts
memory-trace Link between memory entries (e.g., blueprint → test → span)
memory-scope TTL-based grouping (session, project, global)
pinned-memory Fixed artifacts kept across projects (e.g., DSL syntax, template base)
memory-update-hook Observers, rewriters, validators of evolving memories
memory-access-policy Role or agent-based access scoping

📁 Files and Data Sources

Source Description
vector-db/embeddings/*.json Vector DB memory entries
memory-graphs/*.yaml Agent-generated memory traces
context/*.token.json Session-bound prompt memory
longterm-store/*.md, .json, .blobref Files, wiki, storage
memory-hooks/*.cs Code-based update or access logic

🧠 Sample Memory Entry

{
  "memoryId": "mem-longterm-user-service-blueprint",
  "type": "embedding-store",
  "scope": "project",
  "embedding": [0.109, 0.301, ...],
  "tags": ["blueprint", "user", "microservice"],
  "linkedArtifacts": ["feature:UserLogin", "event:UserLoggedIn", "span:User.Authenticate"],
  "pinned": false,
  "expiresInDays": 90,
  "traceId": "memgraph-202506"
}

🧩 Metadata Fields

Field Description
scope session, project, global, agent
type embedding-store, memory-trace, access-policy
linkedArtifacts[] Index IDs or memoryIds that reference this node
embedding High-dimensional representation
createdByAgentId Originator agent
pinned true if this memory is permanent
expiresInDays TTL if applicable

🔍 Retrieval Examples

Agent Task
Vision Architect Agent “Recall all vision docs and blueprints from this session”
QA Agent “Find all test memory entries linked to EmailService”
Planning Agent “Follow memory path: Feature → Event → Test → Span → Metric”
Optimization Agent “Find stale or unused memories across past 10 projects”

🧠 Memory Traces Example

traceId: blueprint-user-registration
nodes:
  - mem-user-blueprint
  - mem-event-user-registered
  - mem-test-user-registration.feature
  - mem-span-user.register
  - mem-dashboard-user-metrics

🔁 Memory Evolution and Hooks

Agents may attach:

  • onCreateMemory: Summarize or rewrite artifact
  • onExpireMemory: Archive or emit analytics
  • onRecall: Merge into active context
  • onUpdate: Re-embed or replace semantic vector

🛡️ Access & Scoping

Each memory has a policy:

  • 🔒 Only accessible to creator agent (session, sandbox, etc.)
  • 🔄 Shared with specific agent roles (e.g., all-agents-on-project)
  • 🌍 Global pinning for factory-wide skills, templates, DSLs

✅ Summary

The index-memory-graphs provides:

  • 🧠 A semantic knowledge map across all factory processes
  • 🔄 Agent memory reuse, transfer, and evolution
  • 🕸️ Traceability from code to span to prompt to outcome
  • 🔒 Scoped and secure memory access models

It is the neural substrate of the ConnectSoft AI Software Factory’s collective intelligence.

🧠 index-agent-error-feedback: Categorization of Agent & LLM Failures

The index-agent-error-feedback captures structured feedback about agent misbehavior, prompt misunderstandings, and generation failures during AI-driven software production cycles.

It helps the system to:

  • 🚫 Identify root causes of failed outputs
  • 🔄 Improve prompt planning and retries
  • 📚 Feed learning loops with error context
  • 🧠 Adapt memory or DSLs based on failure trends
  • ⚠️ Track recurring misunderstanding patterns

It’s the self-correction index for ConnectSoft’s AI agent collective.

📂 Types of Error Feedback Tracked

Type Description
prompt-misunderstanding LLM misunderstood or hallucinated instruction
output-malformed Output structure was invalid, unparseable, or non-compliant
missing-context Prompt lacked required memory or DSL context
agent-crash Execution failure within agent pipeline (code or orchestration)
retry-success Initial failure recovered successfully via retry or self-correction
invalid-dsl-injection Wrong DSL injected into prompt pipeline
trace-break Output could not be traced back to DSL, span, or memory correctly

📁 Files and Data Sources

Source Description
feedback-agent/*.yaml Structured agent error logs
llm-output-validator/*.json Validator or schema checker failures
retry-orchestrator/logs/*.trace.json Retry metadata, resolution status
agent-audit-logs/*.trace.failure.json Composite trace failure reports
prompt-failure-reports/*.md Annotated hallucination or deviation

🧠 Sample Feedback Entry

{
  "feedbackId": "err-agent-v1-invite-prompt",
  "agentId": "PromptPlannerAgent",
  "projectId": "connectsoft-notification",
  "promptTemplate": "prompt-user-invite-handler",
  "errorType": "prompt-misunderstanding",
  "symptoms": [
    "Generated response ignored DSL",
    "Omitted required span link",
    "Introduced field not in model"
  ],
  "resolvedBy": "retry-cycle-2",
  "linkedMemoryIds": ["mem-blueprint-invite", "mem-span-user.invite"],
  "timestamp": "2025-06-06T10:42:17Z"
}

🧩 Metadata Fields

Field Description
feedbackId Unique ID of the feedback event
agentId Agent who produced the faulty or rejected output
promptTemplate Name of the prompt template used
errorType Standardized type (see table above)
symptoms[] Natural language observations
linkedMemoryIds[] Related memory or DSL contexts
resolvedBy Retry method, correction path, or N/A
timestamp UTC datetime of the error occurrence

🔍 Usage Examples

Agent Task
GovernanceAgent Flag DSLs or prompts that cause high hallucination rates
PromptPlannerAgent Refine template paths to reduce misunderstandings
RetryOrchestrator Select best retry strategy based on past outcomes
KnowledgeGraphAgent Visualize agent failure clusters by domain or role
ObservabilityAgent Emit incident spans if error exceeds threshold

📉 Aggregated Signals

  • 🔁 Retry frequency: per agent, per prompt, per DSL
  • ❌ Failure rate: % of failed traces vs. valid completions
  • 📚 Correction accuracy: Retry → success ratio
  • 🧠 Prompt drift alerts: significant hallucination compared to baseline
  • 🪵 Failure trace trees: output → DSL → span → feedback

✅ Summary

The index-agent-error-feedback provides:

  • 🔬 Detailed introspection into AI failures
  • 📊 Statistical signal for governance and improvement
  • 🧠 Self-healing retries, smarter prompt planning
  • 🚦 Early detection of DSL or prompt misalignment
  • 🔁 Foundational loop for continuous prompt-agent refinement

It’s a diagnostic nervous system for the ConnectSoft AI Factory, enabling adaptive intelligence at scale.

🧠 index-dsl-dependency-graph: Structural Relationships Between DSL Elements

The index-dsl-dependency-graph captures explicit and inferred dependencies between DSL units, enabling agents to understand how one DSL concept or artifact links to others in the software generation flow.

This graph forms the logical backbone of:

  • Multi-step generation chains (e.g., Event depends on Command)
  • DSL-driven prompt planning
  • Scope-limited change propagation
  • Reusability and impact analysis

It serves as the semantic DAG (Directed Acyclic Graph) of all DSL assets.

📂 Types of DSL Dependencies Captured

Type Description
command -> event A command triggers or emits an event
event -> span Event instrumentation DSL tied to observability span
event -> blueprint Event requires or references logic blueprint for implementation
span -> test A span is validated via a specific test DSL or assertion
dsl -> dsl Generic DSL-to-DSL link (e.g., state machine → transitions)
dsl-extension One DSL semantically or syntactically extends another (extends, base)
dsl-input-output DSL defines transformation or mapping (e.g., input DTO → domain model)

📁 Files and Data Sources

Source Description
dsl/*.dsl.yaml Structured DSL files with declared links
graph-generated/*.edges.yaml Agent-inferred semantic DSL relationships
memory-trace/*.mem.json Memories that reveal transitive dependencies
dsl-tests/*.features Behavior-based link discovery (e.g., triggers)
prompt-plans/*.dsl-plan.json Planner-resolved DSL link flows

🧠 Sample Dependency Entry

{
  "linkId": "link-user-register-command-to-event",
  "fromDslId": "dsl-user-register-command-v1",
  "toDslId": "dsl-user-registered-event-v1",
  "type": "command -> event",
  "traceId": "trace-3428",
  "agentId": "DslDependencyResolverAgent",
  "confidence": 0.94,
  "inferred": false,
  "origin": "dsl/user/dsl-user-register-command.yaml"
}

🧩 Metadata Fields

Field Description
linkId Unique ID for this dependency edge
fromDslId Source DSL (e.g., command)
toDslId Target DSL (e.g., event, span)
type Human-readable or standardized edge label
confidence Confidence score (esp. for inferred links)
traceId DSL usage trace that proves or observes this link
inferred true if not explicitly declared but observed or derived
origin Path to original DSL or source that defined the relationship
agentId Agent who recorded or verified the dependency

🔍 Retrieval Examples

Agent Task
PromptPlannerAgent Determine which DSLs must be included when emitting Command
DslImpactAgent Assess which DSLs are affected by changes in Event definitions
QA Agent Generate tests for all spans linked to given Command
GovernanceAgent Freeze all dependent DSLs when parent DSL is deprecated
KnowledgeGraphAgent Visualize domain-wide DSL chains and interactions

🌐 Graph Shapes and Examples

Example: Microservice User Registration Flow

nodes:
  - dsl-user-register-command
  - dsl-user-registered-event
  - dsl-user.register.span
  - dsl-user-register.feature
edges:
  - { from: command, to: event, type: "command -> event" }
  - { from: event, to: span, type: "event -> span" }
  - { from: span, to: test, type: "span -> test" }

🧠 Use Cases for Agents

  • Impact Analysis: Understand what to regenerate after a DSL change.
  • Traceability: Show full generation lineage and DSL links per output.
  • Incremental Planning: Skip recomputation for unaffected DSL paths.
  • Optimization: Find minimal regeneration sets for large DSL graphs.

✅ Summary

The index-dsl-dependency-graph enables:

  • 📊 Fine-grained, multi-level DSL relationship reasoning
  • 🔁 Smart propagation during changes, forks, or template evolution
  • 🕸️ Graph-style generation workflows and optimization
  • 🧠 Foundation for end-to-end semantic tracing of software artifacts

It is a knowledge-aware DSL linkage engine, powering traceability and precision.

🧠 index-semantic-paths: Precomputed DSL → Artifact → Span → Outcome Chains

The index-semantic-paths captures linear or graph-based semantic chains that represent how a single feature or concept flows through the AI Software Factory — from DSL to execution, test, and telemetry.

These paths are essential for:

  • 📊 Root cause analysis
  • 🔄 Regeneration scoping
  • 🔍 Agent navigation and backtracking
  • 🔁 Prompt routing and fallback reasoning

Think of this as the supply chain of meaning in the factory.

🔗 Path Examples

Example Path Meaning
dsl-feature → blueprint → test → span → log A feature DSL’s full runtime trace
command → event → handler → test → metric Full propagation chain across DSL and observability layers
dsl-user-delete → span:User.Delete → test:UserDeletion.feature DSL mapped directly to runtime and validation artifacts
dsl → prompt → llm-output → feedback → retry → correction End-to-end generation+correction cycle

📂 Stored Path Types

Path Type Description
dsl-trace-path DSL to output trace (blueprint, handler, etc.)
test-validation-path Feature to test spans to assertion results
telemetry-path DSL → Span → Metric or Error path
prompt-failure-path Prompt → Output → Feedback → Retry → Fix
drift-detection-path DSL → memory snapshot → semantic drift → replacement

📁 Files and Data Sources

Source Description
semantic-paths/*.yaml Manually or agent-generated semantic chains
trace-graphs/*.json Graph-format representation of chains
agent-memory/*.path.json Embedded memory chain data
prompt-runs/*.trace.json Prompt-to-output lineage
feedback-corrections/*.json Retry and self-healing context links

🧠 Sample Path Entry

{
  "pathId": "path-feature-user-registration",
  "pathType": "dsl-trace-path",
  "nodes": [
    "dsl-user-registration.feature",
    "blueprint-user-registration",
    "span-user.create",
    "test-user-registration.feature",
    "log-span-user.create.success"
  ],
  "tags": ["feature", "registration", "span", "trace"],
  "agentId": "SemanticPathBuilderAgent",
  "projectId": "connectsoft-user-core",
  "createdAt": "2025-06-06T08:12:00Z"
}

🧩 Metadata Fields

Field Description
pathId Unique identifier for the semantic path
pathType One of predefined types (see table above)
nodes[] Ordered list of DSL, blueprint, test, span, memory, etc.
tags[] Optional semantic categories
agentId Agent that constructed or validated the path
projectId Scoped project or context
createdAt UTC timestamp of path creation or last validation

🔍 Usage Examples

Agent Purpose
ImpactAnalyzerAgent Regenerate only what’s in the same semantic path
PromptPlannerAgent Load the exact span/test for context-aware generation
ObservabilityAgent Navigate from test failures to span and blueprint origins
OptimizationAgent Collapse unused or repeated paths for memory reuse
FeedbackLoopAgent Learn which generation paths led to retries or hallucinations

📈 Path Evolution Patterns

Paths may:

  • 🔁 Drift when a DSL changes or test gets deleted
  • 🧠 Fork when a tenant overrides a DSL or adds a branch span
  • ⏱️ Expire when the memory TTL invalidates part of the path
  • 🔄 Heal after agent retry flows reconstruct or replace broken edges

✅ Summary

The index-semantic-paths provides:

  • 🧠 Contextual flow understanding across agents and DSL layers
  • 🔁 Backtracing and repair assistance for failed generations
  • 🔍 Impact zone targeting during updates or governance actions
  • 📊 Optimization signal for factory-scale memory reuse and generation scope

It enables precision-guided orchestration of the full lifecycle of AI-generated software artifacts.

🧠 index-agent-quality-score: Output Precision, Effectiveness, and Reliability Metrics

The index-agent-quality-score captures quantitative and qualitative signals reflecting each agent’s output success, resilience, prompt mastery, and impact on software correctness.

It provides a unified scoring layer used to:

  • 📊 Compare agent runs across projects and time
  • 🔄 Tune retry and fallback orchestration
  • 🧠 Train agents by reinforcing top-performing patterns
  • 🧬 Feed adaptive planner decisions and skill routing

It’s the fitness function of ConnectSoft's agentic evolution system.

🧪 Quality Signals Tracked

Signal Name Description
passRate % of outputs accepted without retry or correction
retryRecoveryScore How well agent recovers from initial failures
validationSuccessRate Tests, linters, analyzers passed post-output
promptFidelityScore How closely output matched original prompt intent
feedbackRating Manual or automatic feedback score from other agents or users
semanticConsistency Vector similarity between output and target memory / DSL goal
tokenEfficiency Output quality / total token usage ratio

📁 Sources and Data

Source File / Stream Description
agent-output-log/*.trace.json All output lifecycle traces
feedback/*.feedback.json Agent-generated or human feedback records
validation-results/*.score.json Lint/test pass rates and assertions
retry-orchestrator/*.recovery.yaml Retry success, prompt rewrites, output changes
semantic-metrics/*.embedding.json Similarity scores, hallucination deltas

🧠 Sample Entry

{
  "agentId": "BlueprintBuilderAgent",
  "projectId": "connectsoft-notification",
  "runId": "run-20392",
  "timestamp": "2025-06-06T10:14:33Z",
  "passRate": 0.92,
  "retryRecoveryScore": 0.8,
  "validationSuccessRate": 0.95,
  "promptFidelityScore": 0.91,
  "feedbackRating": 4.7,
  "semanticConsistency": 0.88,
  "tokenEfficiency": 0.63,
  "qualityScore": 0.89
}

🧩 Metadata Fields

Field Description
agentId Identifier of the evaluated agent
runId Unique agent run instance
projectId Contextual project (if scoped)
timestamp UTC time of score calculation
qualityScore Final normalized score across all metrics (0–1)
feedbackRating Optional average human/star or peer agent rating

🔍 Use Cases

Agent Purpose
RetryOrchestratorAgent Prefer highest-scoring prompt branch when retrying
PromptPlannerAgent Rank DSL-prompt-template combos by past score per agent
GovernanceAgent Identify degraded agent performance over time
SkillSelectorAgent Route planning task to agent with highest domain-quality score
ObservabilityAgent Raise alerts if agent quality dips below SLA threshold

📈 Time Series / Long-Term Views

Tracked via agent-version scoped metrics:

  • 📉 Quality decay across iterations or factory upgrades
  • 🔁 Impact of memory changes on output quality
  • 🧠 Skill retraining events and score delta tracking

🧠 Agent Learning Actions

Agents may:

  • 🔄 Replace prompt templates or DSL variants for low-scoring paths
  • 🧠 Merge high-scoring outputs into reusable memory chunks
  • 🧪 A/B test DSL rewrites to improve future promptFidelityScore

✅ Summary

The index-agent-quality-score enables:

  • 📊 Measurable quality benchmarking per agent and run
  • 🔁 Informed fallback, retry, and planner routing decisions
  • 🧠 Closed-loop learning, DSL refinement, and prompt optimization
  • 🚦 Performance monitoring and observability across software cycles

It powers the evolutionary intelligence behind ConnectSoft's agentic orchestration.

🧠 index-pipeline-snippets: Reusable CI/CD, DevOps, and Workflow Fragments

The index-pipeline-snippets captures reusable, modular pipeline steps — such as build blocks, test jobs, publish scripts, and deployment plans — used by agents to assemble full CI/CD workflows for generated microservices, libraries, and documentation.

This index allows:

  • ⚙️ Dynamic pipeline generation per project/template
  • ♻️ Reuse and versioning of DevOps steps
  • 📦 Agent-driven publishing to NuGet, Docker, etc.
  • 📋 DSL-to-YAML mapping for pipeline-as-code

It is the DevOps DNA registry of the ConnectSoft AI Software Factory.

🧩 Snippet Types

Type Description
build-snippet .NET, Node, Python, Blazor, Angular build jobs
test-snippet Unit, integration, BDD (SpecFlow), load test fragments
publish-snippet NuGet, Docker, Azure Artifacts, GitHub Release pipelines
deploy-snippet Helm, Bicep, Terraform, Azure Web App deploy jobs
infrastructure-step Resource provisioning, Key Vault, Redis, Service Bus
doc-snippet MkDocs, Swagger generation, Mermaid export jobs
conditional-snippet Matrix conditions, flag-based logic paths

📁 Files and Data Sources

Source Path Description
pipelines/snippets/*.yaml Canonical reusable pipeline steps
pipeline-dsl/*.pipeline.yaml Agent-constructed pipelines using these blocks
agent-generated/ci-template.graph Snippet graph usage across services/templates
knowledge-memory/*.pipeline.json Embedded snippet reference memory
dsl-blueprints/*.yaml Maps DSL to pipeline inclusion requirements

🧠 Sample Snippet Entry

snippetId: build-dotnet-microservice
type: build-snippet
language: dotnet
tags: [build, dotnet, msbuild, csproj, microservice]
version: v1.2.0
usedInTemplates:
  - microservice-api
  - auth-service
ciPlatform: azure-devops
agentGenerated: true
steps:
  - task: UseDotNet@2
    inputs:
      packageType: 'sdk'
      version: '8.x'
  - script: dotnet build --no-restore --configuration Release
    displayName: Build Solution

🧩 Metadata Fields

Field Description
snippetId Unique ID or canonical name of the snippet
type Category (build, test, deploy, etc.)
version Snippet version (semver-style)
language Language or stack supported (dotnet, js, python, etc.)
ciPlatform Target platform (azure-devops, github-actions, etc.)
usedInTemplates[] Microservice templates or projects referencing this
tags[] Keywords or DSL tags triggering this snippet
agentGenerated True if assembled or templated by agents

🔍 Usage by Agents

Agent Purpose
PipelineAssemblerAgent Compose pipelines for new projects using DSL + snippets
DevOpsBlueprintAgent Insert snippets into Bicep/Terraform workflows
TemplateBuilderAgent Inject snippets into .template.config pipelines
RetryOrchestratorAgent Resume failed pipelines using known snippets and diagnostics
DocumentationAgent Include job descriptions and diagrams in DevOps documentation

🔄 Versioning and Reuse Strategies

  • ✅ Snippets support semantic versioning with changelog memory
  • 🔁 Shared across templates using tags like build:dotnet or deploy:azure-appservice
  • ⚠️ Agents check for deprecated or insecure steps during planning
  • 🔐 Snippets may include secrets policies, parameter injection, and environment matrix logic

🧠 Path Inclusion Example (Semantic Path)

pathId: pipeline-notification-deploy
nodes:
  - dsl-service:notification
  - snippet:build-dotnet-microservice
  - snippet:test-specflow
  - snippet:publish-nuget
  - snippet:deploy-azure-webapp

✅ Summary

The index-pipeline-snippets enables:

  • ⚙️ Smart pipeline construction per project, DSL, or template
  • 📦 Modular, reusable, and secure DevOps logic blocks
  • 🤖 Agent-friendly workflows with traceable steps and feedback
  • 📋 Consistent CI/CD generation aligned to factory principles

It is the connective tissue between DSLs and software delivery pipelines.

🧠 index-infra-component-templates: Reusable Infrastructure Modules for Agents

The index-infra-component-templates captures agent-discoverable, reusable infrastructure building blocks such as databases, queues, caches, and secrets stores. These templates are referenced by DSLs, microservices, and deployment pipelines to assemble cloud-native environments on demand.

They power:

  • ⚙️ Infrastructure-as-code (IaC) composition
  • 🧱 Repeatable environment scaffolding
  • 🔁 Shared provisioning logic across services
  • 🌐 Tenant-aware multi-environment orchestration

It enables modular, agent-driven provisioning of platform components with traceable DSL-to-IaC mappings.

🧩 Types of Infra Components Indexed

Type Description
database Templates for SQL Server, PostgreSQL, Cosmos DB
message-bus Azure Service Bus, RabbitMQ, Kafka
cache Redis, distributed memory, sidecar caching
secrets Azure Key Vault, Vault.io, config maps
storage Azure Blob, File Storage, S3
auth-provider IdentityServer, OpenIddict, Azure AD
ingress API Gateway, NGINX, Azure Front Door
monitoring-agent Grafana, Application Insights, Prometheus

📁 Files and Data Sources

Source Path Description
infra-templates/components/*.bicep Component-level IaC modules
dsl-blueprints/*.infra.yaml DSL-to-component mappings
agent-output/infra-resources/*.json Resolved resource definitions
devops-scripts/provision/*.sh Custom shell scripts or CLI templates
terraform/modules/*.tf Terraform-based reusable infra blocks

🧠 Sample Entry

{
  "componentId": "redis-cache-v1",
  "type": "cache",
  "cloudProvider": "azure",
  "templatePath": "infra-templates/components/redis.bicep",
  "usedIn": ["template-notification-service", "template-realtime-hub"],
  "parameters": {
    "sku": "Standard",
    "capacity": 1
  },
  "agentGenerated": false,
  "version": "1.0.3"
}

🧩 Metadata Fields

Field Description
componentId Canonical unique name for the infra component
type One of predefined types (cache, bus, db, etc.)
cloudProvider e.g., azure, aws, gcp
templatePath File or script used to provision the component
parameters Default or required configuration inputs
usedIn[] Templates or services that reference this component
agentGenerated Whether generated by the AI Software Factory
version Semantic version (e.g., v1.0.0)

🔍 Agent Use Cases

Agent Purpose
DeploymentPlannerAgent Inject required infra based on DSL or blueprint
DevOpsBlueprintAgent Merge infra templates into Helm/Bicep/Terraform stacks
GovernanceAgent Enforce consistency across tenants/environments
RetryOrchestratorAgent Restore failed resources using known component template
CostEstimationAgent Estimate resource costs from reusable infra component memory

🔄 Composition & Reuse Strategy

  • 📦 Modular templates reference one another using submodules
  • 🔁 Parameterizable via factory config DSL or prompts
  • ⛳ Scoped to project, environment, or template
  • 🔐 Supports secrets and secure identity injection

🧠 Example: Blueprint-Triggered Provisioning

dsl-feature: "Notification.Email.Sender"
requiredInfra:
  - componentId: redis-cache-v1
  - componentId: azure-service-bus-queue-v2
  - componentId: app-insights-logger

✅ Summary

The index-infra-component-templates enables:

  • 📦 Provisioning of reusable, cloud-native infrastructure modules
  • 🔁 DSL-aware resource injection during microservice generation
  • ⚙️ Seamless DevOps integration with IaC tools and pipelines
  • 🔍 Traceable, versioned, and modular infrastructure composition

It is the foundation layer for automated environment creation and deployment in ConnectSoft’s agentic platform.

🧠 index-cross-project-reuse: Factory-Wide Reuse of DSLs, Templates, and Components

The index-cross-project-reuse captures where and how DSLs, libraries, templates, infrastructure components, test suites, and pipelines are reused across multiple projects, tenants, or product lines in the ConnectSoft AI Software Factory.

This index powers:

  • 📈 Reuse scoring and marketplace publication
  • 🔄 Dependency tracking across boundaries
  • 🛠️ Template optimization and consolidation
  • 🔍 Change impact analysis across consumers

It is the reuse and propagation map of the factory’s evolving knowledge base.

🔁 Types of Reuse Tracked

Reuse Type Description
dsl-reuse DSLs (e.g., Command, Event, Span) used in multiple microservices
template-reuse Templates used across editions or verticals
pipeline-reuse Shared CI/CD pipeline snippets or flows
infra-reuse Shared infrastructure components across projects
test-suite-reuse Common SpecFlow features reused across similar bounded contexts
prompt-strategy-reuse Reused prompt scaffolds, correction flows, and retry tactics

📁 Data Sources

Source File Description
reuse-map/*.json Canonical mapping of reusable elements
memory-index/project-usage/*.yaml Agent-inferred cross-project references
blueprints/*.dsl.yaml DSLs linked to multiple project IDs
pipeline-dsl/*.reuse.yaml Shared CI/CD usage tracked across repo roots
templates/*.template.yaml Shared templates with usage metadata

🧠 Sample Reuse Entry

{
  "reuseId": "reuse-dsl-user-auth-event",
  "elementId": "dsl-user-authenticated-event",
  "elementType": "dsl-reuse",
  "usedInProjects": ["auth-service", "notification-service", "session-tracker"],
  "timesUsed": 3,
  "firstUsed": "2025-03-10",
  "lastUsed": "2025-06-05",
  "agentId": "ReuseIndexAgent",
  "linkedTemplates": ["template-auth-service", "template-notification-service"]
}

🧩 Metadata Fields

Field Description
reuseId Unique identifier of the reuse record
elementId DSL/template/component being reused
elementType One of: dsl-reuse, template-reuse, infra-reuse, etc.
usedInProjects[] List of project IDs referencing this element
linkedTemplates Templates using or exporting the reusable element
timesUsed Count of usage across unique projects
firstUsed Earliest known usage timestamp
lastUsed Most recent project reference
agentId Agent that recorded the reuse relationship

🔍 Use Cases

Agent Purpose
GovernanceAgent Detect and score reusable DSLs for promotion or validation
OptimizationAgent Collapse duplications into common modules
TemplateBuilderAgent Suggest generalization of overused project templates
MarketplaceAgent Surface top reused components for publishing to the factory marketplace
TenantDiffAgent Detect drift or inconsistency across tenant forks

📈 Reuse Graph Example

reuseGraph:
  nodes:
    - dsl-user-authenticated-event
    - template-auth-service
    - template-notification-service
  edges:
    - from: dsl-user-authenticated-event
      to: template-auth-service
      type: dsl-reuse
    - from: dsl-user-authenticated-event
      to: template-notification-service
      type: dsl-reuse

🧠 Insights Enabled

  • 📊 Which components are foundational or overloaded
  • 🚨 Where change in one template could cascade to others
  • 🧱 When to abstract DSL into common domain (e.g., identity-core)
  • 💡 What reusable elements deserve documentation or badges

✅ Summary

The index-cross-project-reuse enables:

  • 🔁 Factory-wide visibility into software reuse and modularity
  • 📦 Consolidation of scattered DSLs, pipelines, and infra into canonical components
  • 🧠 Smart agent recommendations and generation patterns across domains
  • 📊 KPIs for reuse, duplication, and template optimization

It is the semantic reuse ledger that makes the AI Software Factory scalable and efficient at enterprise scale.

📌 Planned Knowledge & Memory Indices (Backlog)

The following indices are in the design or backlog stage and will extend memory, traceability, and reuse across software generation workflows:

Index ID Purpose
index-token-usage Tracks token count, cost estimation, and efficiency per agent/prompt
index-agent-effort-matrix Quantifies effort, retries, and time per agent per task
index-prompt-template-links Maps DSLs and artifacts to reusable prompt templates
index-template-lineage Captures derivation and versioning trees of microservice templates
index-retry-patterns Catalogs retry logic, failure types, and prompt rewrites
index-feedback-loop Connects prompt → feedback → retry → success cycles
index-agent-version-diff Tracks agent prompt/skill differences across versions
index-project-drift-signals Flags divergence of projects from their base templates
index-validation-outcomes Logs pass/fail results of generated output across validators/tests
index-skill-invocation-log Tracks which agent skills were invoked and their success rate
index-agent-scope-boundary Defines boundaries of what each agent is allowed to generate/edit
index-memory-hotspots Highlights frequently used, rewritten, or stale memories
index-dsl-usage-statistics Records how often DSL elements are used, forked, or versioned
index-agent-specialization-map Maps agents to DSL clusters where they perform best
index-template-quality-profile Aggregates test, feedback, and quality scores for templates
index-agent-dependency-graph Shows how agents depend on each other’s output or memory
index-agent-training-snapshots Captures agent behaviors and skills at specific training epochs
index-observability-coverage Tracks which spans, metrics, and logs are linked to which artifacts
index-compliance-checks Tracks security, accessibility, PII, and other policy validations
index-governance-flags Records violations, overrides, or exceptions across projects

🧩 These indices will be introduced incrementally to cover blind spots, optimize prompt cycles, enhance governance, and improve agent self-awareness.