🧠 Knowledge and Memory System in ConnectSoft¶

Overview¶

The Knowledge and Memory System is a foundational pillar of the ConnectSoft AI Software Factory. It provides the architecture, storage models, metadata conventions, and retrieval flows that allow intelligent agents to act with continuity, context, and autonomy across projects, blueprints, modules, and orchestrated lifecycles.

“In ConnectSoft, knowledge is infrastructure — not documentation. And memory is not an optimization — it’s the execution context.”

🎯 Why Knowledge and Memory Matter¶

The ConnectSoft platform is designed to autonomously generate, evolve, and operate thousands of SaaS components: microservices, APIs, libraries, templates, orchestrators, and entire domain platforms. To do this at scale, every agent in the system must:

Understand the current project and its historical context.
Reuse previously generated solutions, code patterns, and architectural decisions.
Reason about decisions and outcomes from earlier flows.
Adapt output based on prior errors, feedback, or refinement signals.

This is only possible when all artifacts, blueprints, outputs, and coordination plans are captured, structured, and retrievable through an intelligent memory model.

🧩 What Is Stored in Memory?¶

The knowledge and memory system is responsible for storing and making accessible:

Category	Examples
📦 Code & Templates	Generated `.cs`, `.bicep`, `.feature`, `.json`, `.md`, `.yaml` files
📚 Documentation	`README.md`, design docs, prompt inputs, blueprint summaries
🧠 Agent Outputs	Generated artifacts from each skill and execution trace
🧰 Templates & Libraries	Metadata, usage stats, README, DI configurations, options, test coverage
📄 Contracts & APIs	OpenAPI specs, gRPC contracts, event schemas, domain models
📊 Observability Metadata	`execution-metadata.json`, `traceId`, `agentId`, `sprintId`, durations
📜 Events & Orchestration	Blueprint events, milestone states, FSM transitions
🔁 Prompt Interactions	User-defined prompts, planner context, injected blueprint memory
🧪 Tests & Results	Generated test cases, results, failure diagnostics

🔄 Memory as Infrastructure¶

Unlike traditional documentation systems, ConnectSoft treats memory as:

Concept	Role in the Platform
Knowledge Graph	Connects artifacts across blueprints, agents, services, and tenants
Execution History	Traceable timeline of all agent actions, failures, outputs
Retrieval Layer	Semantic embeddings + structured filtering based on agent type and skill domain
Reusable Intelligence	Enabling agents to reason from prior outputs instead of starting fresh
Governed Asset Store	Versioned, auditable memory with `agentId`, `traceId`, and storage class

🧠 How Memory Powers Agent Behavior¶

Every agent in the system relies on memory for:

Need	Memory Capability Used
Understanding project history	Per-project memory scope linked to `traceId` and `projectId`
Generating similar outputs	Vector search by embedding of similar prior blueprints
Avoiding redundant generation	Retrieval of existing modules and validations
Learning from past mistakes	Recall of `AgentFailed`, `TestFailed`, `PromptCorrected`
Explaining prior decisions	Linked memory entries with metadata and rationale
Working across multiple modules	Memory scoping by `moduleId`, `tenantId`, `blueprintType`

📎 Link to Platform Principles¶

This system connects directly to key ConnectSoft principles:

Principle	Memory Alignment Example
🔁 Event-Driven	Knowledge is updated on events like `BlueprintCreated`, `PromptRefined`
🧱 Modular Architecture	Memory is scoped per module, template, and domain context
🧠 AI-First Development	Memory fuels autonomous behavior and intelligent orchestration
☁️ Cloud-Native	Memory is served and versioned from scalable, observable infrastructure
📊 Observability-First	All memory access is observable, queryable, and auditable
🔐 Security-First	Per-tenant, per-agent scoped access control

✅ Summary¶

The Knowledge and Memory System in ConnectSoft transforms how agents operate by providing:

Persistent context across flows and executions
Reusable design intelligence and artifacts
Traceability and governance of every output
Semantically indexed, event-driven knowledge graphs
Per-project memory isolation with structured access

This foundation ensures that the platform can scale intelligently, operate autonomously, and learn continuously across thousands of generated services and blueprints.

Types of Knowledge¶

In the ConnectSoft AI Software Factory, knowledge is modular, composable, and versioned. It spans far beyond traditional documentation and includes all traceable, reusable, and semantically meaningful artifacts produced or consumed by agents during blueprint execution, microservice generation, or orchestration flows.

This section categorizes all the types of knowledge modules that are captured, stored, indexed, and retrieved across the platform’s memory system.

🧠 Categories of Knowledge Modules¶

Type	Description	Examples
📄 Blueprint Documents	Human- and agent-authored structured inputs to drive generation flows	`VisionDocument.md`, `ServiceBlueprint.yaml`, `ContextMap.json`
🔧 Generated Code	Agent-produced files representing logic, adapters, APIs, and configuration	`.cs`, `.feature`, `.json`, `.bicep`, `.yaml`, `.proto`
📚 Documentation	Markdown files and structured outputs from the `Documentation Writer Agent` or curated by humans	`README.md`, `ModuleOverview.md`, `HowItWorks.md`
🧪 Tests and Results	Generated BDDs, unit tests, scenario validations, execution results	`BookAppointment.feature`, `TestsPassed`, `test-log.json`
📦 Template Definitions	Reusable `dotnet new` templates or YAML-based bootstrapping skeletons	`ConnectSoft.MicroserviceTemplate`, `ApiGatewayTemplate`
📚 Libraries and APIs	Shared NuGet packages, abstractions, adapters, and extension modules	`ConnectSoft.Extensions.`, `ConnectSoft.Abstractions.`
📄 Contracts and Specs	OpenAPI, domain events, interface definitions	`booking.openapi.yaml`, `AppointmentBooked.event.json`
📁 Project Metadata	Sprint trace matrix, execution metadata, planning records	`sprint-trace-matrix.json`, `execution-metadata.json`
📈 Observability Records	Structured traces, spans, metrics, and logs	`traceId`, `agentId`, `status`, OpenTelemetry export
💬 Prompts and Dialogue	Input prompts, modified context injections, structured planner instructions	`initialPrompt.md`, `promptRefined.md`, `planner-context.json`

🧩 Modular Knowledge Units¶

In ConnectSoft, every artifact is a module, and every module has a corresponding knowledge memory footprint.

Artifact Type	Memory Shape
`BookingService`	Knowledge module scoped to `microserviceId` and `boundedContext`
`BookAppointmentHandler.cs`	Code memory chunk tagged with `skill:GenerateHandler` and `agentId`
`tests/booking.feature`	Test memory chunk with `traceId`, `featureId`, `testId`
`vision-blueprint.md`	Prompt-driven knowledge artifact with `origin=vision-architect`
`Microservice Template`	Template module with README, config files, metadata, and skills used

🧾 Memory Artifact File Formats¶

All memory modules must conform to structured file formats to ensure traceability, observability, and agent reusability:

Format	Used For	Enforced By
`.md`	Vision docs, README, onboarding manuals	`Documentation Writer Agent`
`.yaml`	Blueprints, configs, CI/CD pipelines	`Solution Architect Agent`, `DevOps Agent`
`.json`	Contracts, test matrices, execution logs	`Test Generator`, `Coordinators`
`.cs`	Handlers, commands, adapters	`Backend Developer Agent`, `Adapter Generator Agent`
`.feature`	BDD test cases	`Test Generator Agent`, `QA Agent`
`.bicep`	Infrastructure definitions	`Cloud Provisioner Agent`

All files are embedded into memory using a metadata schema (defined in later cycles), and indexed semantically and structurally.

🔗 Cross-Cutting Tags and Indexes¶

Every memory module is indexed and grouped by:

traceId, projectId, sprintId
agentId, skillId
moduleId, featureId, blueprintId
type (e.g., test, code, plan, doc, event)
tenantId, edition, environment
source (e.g., generated, curated, imported, retrieved)
version, status, validated

These tags allow multi-dimensional search, filtering, and reuse by agents and Studio components.

📘 Example: Memory Snapshot for a Module¶

{
  "moduleId": "booking-service",
  "traceId": "trace-2025-05-123",
  "agentId": "backend-developer",
  "skillId": "GenerateHandler",
  "filePath": "src/Application/Handlers/BookAppointmentHandler.cs",
  "tags": ["handler", "domain", "appointment", "create"],
  "status": "Success",
  "version": "1.0.3",
  "semanticHash": "4e67bbff",
  "embedding": [0.02, 0.73, -0.11, ...],
  "createdAt": "2025-05-12T10:34:00Z"
}

✅ Summary¶

ConnectSoft’s memory model captures every meaningful artifact as a structured knowledge module, including:

Code, templates, and generated output
Agent plans, decisions, prompts, and metadata
Documentation, contracts, and tests
Execution results, logs, metrics, and tags

All memory types are indexed, semantically embedded, and traceable — ensuring that agents can learn, reason, reuse, and regenerate software with context-aware intelligence.

Knowledge Storage Overview¶

The ConnectSoft AI Software Factory is built on the premise that knowledge is modular, retrievable, and distributed across multiple types of persistent storage. These storage systems are optimized for search, versioning, traceability, and AI agent access patterns.

This section explains where memory lives, how it’s organized, and how different storage types interoperate to deliver a unified knowledge experience.

🧠 Knowledge Storage Architecture¶

The platform’s memory system consists of four primary storage backends, each serving a different purpose:

Storage Type	Purpose	Examples
Vector Database	Fast semantic similarity search over text/code/document embeddings	Azure AI Search, Qdrant, Pinecone
Structured Metadata DB	Index and tag knowledge modules with metadata and traceability	Azure Cosmos DB, PostgreSQL
Blob/Object Storage	Store large and versioned artifacts (e.g., `.cs`, `.bicep`, `.md`, `.json`)	Azure Blob Storage, S3
Source Control (Git)	Long-term versioning and code-based traceability of emitted modules	Azure DevOps Git, GitHub Repos

🧭 Storage Role by Memory Layer¶

Layer	Primary Storage(s)	Notes
🧠 Semantic Memory	Vector DB + Metadata DB	Used by agents during planning and blueprint reuse
📦 Structured Artifacts	Blob + Metadata DB + Git	Code files, README, blueprint YAML, execution JSON
📈 Observability	Metadata DB + Logs	Execution trace logs, performance metrics
🔁 Prompt History	Metadata DB + Blob + Vector DB	Stored as structured text + embeddings
📚 Documentation	Blob + Git + Metadata	Markdown docs, API specs, Diagrams

📦 Blob/Object Storage¶

Used to store full versions of large knowledge assets:

Generated files: .cs, .feature, .bicep, .yaml
README.md, ServiceBlueprint.yaml, execution-metadata.json
API definitions and event schemas
Markdown summaries and diagrams

Stored in paths like:

/tenants/{tenantId}/projects/{projectId}/modules/{moduleId}/memory/

✅ Versioned and traceable. Used for Studio preview, raw download, and historical access.

🔎 Vector DB: Semantic Memory Engine¶

All textual knowledge — such as blueprints, prompts, comments, doc summaries, or tests — is embedded using an AI model (e.g., text-embedding-ada-002), and stored in a vector database with attached metadata.

Agents retrieve the top-K closest results by cosine similarity
Embedding vectors are associated with:
agentId, traceId, skillId
type, tags, tenantId, version

{
  "embedding": [0.13, -0.42, 0.91, ...],
  "text": "Generate handler for BookAppointment use case",
  "metadata": {
    "agentId": "backend-developer",
    "traceId": "trace-xyz",
    "tags": ["handler", "appointment", "microservice"]
  }
}

✅ Enables agents to retrieve similar prior implementations or blueprints.

📚 Structured Metadata Database¶

Every memory module (file, doc, blueprint, test, contract, etc.) has a corresponding metadata record in a structured knowledge index.

Stored fields include:

memoryId, moduleId, traceId, agentId, skillId, version
createdAt, source, status, type, projectId, tenantId
tags, semanticHash, filePath
Pointer to blob path or Git repo reference

This index supports:

Fast queries by filters (e.g., “all test files for booking-service”)
Graph traversal across related modules and agent outputs
Prompt-to-output trace reconstruction

✅ Stored in Azure Cosmos DB (JSON) or PostgreSQL (relational).

📁 Git Repositories in Azure DevOps¶

Each generated project, library, or microservice is committed to a dedicated Git repository with:

README.md, *.csproj, *.sln
Layered folders (Domain, Application, API, Tests, etc.)
Auto-committed by Code Committer Agent
PRs created by orchestrators and agents

Git metadata is cross-linked to memory entries via:

repoId, branch, commitHash
agentId, executionId, timestamp
execution-metadata.json → points to memory used and emitted

✅ Enables reproducibility, review, rollback, and memory audit via Studio or DevOps dashboard.

📊 Execution Metadata Example¶

Each agent’s output is coupled with an execution-metadata.json file:

{
  "agentId": "backend-developer",
  "skillId": "GenerateHandler",
  "traceId": "trace-abc123",
  "projectId": "proj-001",
  "moduleId": "booking-service",
  "filesEmitted": ["BookAppointmentHandler.cs", "CommandValidator.cs"],
  "durationMs": 1104,
  "status": "Success",
  "linkedMemoryIds": ["mem-456", "mem-789"]
}

✅ Captured, indexed, and embedded into both structured memory and semantic search.

🧠 Unified Memory View¶

Agents and Studio users experience a merged view of all these sources:

Browse by module, blueprint, traceId, or type
Search by tags, keywords, or semantic similarity
View rendered outputs (docs, APIs, code) directly
Inspect execution logs, feedback, related events

✅ Powered by Studio’s Knowledge Browser, API-backed metadata search, and semantic query layer.

✅ Summary¶

ConnectSoft uses a multi-storage memory architecture that enables:

Efficient semantic retrieval (Vector DB)
Precise filtering and traceability (Metadata DB)
Durable and versioned artifact storage (Blob + Git)
Reproducible system generation (agent-to-output linkage)

This system enables agents to act with context, reusability, and traceable intelligence across every generated project, feature, and service.

Vector DB for Semantic Memory¶

In ConnectSoft, one of the most powerful aspects of the Knowledge and Memory System is the semantic memory layer, built on top of a vector database. This layer enables agents to retrieve prior knowledge not just by keywords or metadata, but by meaning and similarity.

Instead of re-generating blueprints, tests, handlers, or prompts from scratch, agents can search the vector memory for similar artifacts, instructions, or designs — and reuse or adapt them intelligently.

🤖 What Is Semantic Memory?¶

Semantic memory is a layer of embedded knowledge indexed in a vector space, where each knowledge item (e.g., blueprint, doc, test, prompt, README) is converted into a numeric vector using an embedding model.

These vectors are stored in a vector database, such as:

🔹 Azure AI Search with vector support
🔹 Qdrant
🔹 Pinecone
🔹 Weaviate
🔹 FAISS (local dev)

🧬 Embedding Models Used¶

The platform currently uses:

text-embedding-ada-002 (OpenAI) — default for short-to-medium text
Optional: instructor-xl, bge-large, or e5-mistral for fine-tuned internal use
Embeddings are 1536-2048 dimension float arrays

Each knowledge item is transformed into a vector once and stored alongside metadata.

🧠 What Gets Embedded?¶

All textual or code-based artifacts that are meaningful in natural language or structure:

Type	Embedded?	Notes
`VisionDocument.md`	✅ Yes	Embeds strategic intent and goals
`ServiceBlueprint.yaml`	✅ Yes	Flattened, cleaned, and embedded
`README.md`	✅ Yes	Titles, summaries, usage examples
`.feature` BDD files	✅ Yes	Intent-rich for test generation reuse
Prompt inputs	✅ Yes	Stored as embeddings for later regeneration
Test case summaries	✅ Yes	Clustered to detect redundant cases
Code files	⚠️ Partial	Only embedded if human-labeled or auto-summarized
`.bicep`, `.json` infra	❌ No	Stored in structured memory, not vectorized

🧠 Example Embedded Document¶

{
  "id": "mem-456",
  "text": "Create a service for managing appointments. It should allow booking, canceling, and rescheduling.",
  "embedding": [0.142, -0.031, 0.894, ...],
  "metadata": {
    "type": "vision",
    "agentId": "vision-architect",
    "projectId": "proj-001",
    "traceId": "trace-abc",
    "tags": ["booking", "appointment", "scheduling"],
    "tenantId": "vetclinic",
    "version": "1.0.0"
  }
}

🔍 How Agents Use Vector Memory¶

Agents use a unified SDK method such as:

SemanticMemory.FindSimilarAsync(
  query: "generate handler for appointment booking",
  filter: new { type = "code", agentId = "backend-developer" },
  topK: 5
)

This retrieves a ranked list of similar documents with similarityScore, memoryId, and content.

Use cases include:

Agent	Semantic Use Case
`Test Generator Agent`	Find similar test specs to a given feature
`Backend Developer Agent`	Retrieve past handler implementations
`Blueprint Aggregator`	Suggest prior microservices with the same context
`Vision Architect Agent`	Retrieve similar vision documents or domain patterns
`Knowledge Management Agent`	Tag memory based on similarity clusters

📊 Semantic Query Result Example¶

[
  {
    "memoryId": "mem-789",
    "similarityScore": 0.9372,
    "type": "code",
    "filePath": "src/Application/Handlers/BookAppointmentHandler.cs",
    "tags": ["handler", "booking"],
    "source": "backend-developer"
  },
  {
    "memoryId": "mem-456",
    "similarityScore": 0.9110,
    "type": "blueprint",
    "filePath": "ServiceBlueprint.yaml",
    "source": "solution-architect"
  }
]

🧠 Filtering and Metadata Querying¶

Agents often use hybrid filtering (vector similarity + structured filter) such as:

type = "blueprint"
agentId = "solution-architect"
traceId = CURRENT_TRACE
moduleId = "booking-service"
tags includes ["appointment", "handler"]

This allows for context-aware reuse, avoiding incorrect or cross-tenant leakage.

⚡ Embedding Lifecycle¶

✅ Normalize input: Remove comments, truncate long code blocks
✅ Chunk if needed: For large docs, split into sections
✅ Generate embeddings
✅ Store in Vector DB with metadata
✅ Link to Blob/Git/Metadata DB with memoryId

→ Re-embedding is triggered automatically when:

The file changes substantially
New versions are published
Feedback loop suggests incorrect similarity

🔄 Example: Test Generation via Memory¶

Prompt: “Generate test cases for canceling an appointment”

Agent flow:

Looks up prior .feature files with tags: ["appointment", "cancel"]
Finds 5 relevant tests with high semantic match
Reuses structure and validations
Generates new test suite
Stores new .feature file and embeds it

→ The agent didn’t start from scratch. It reasoned from experience.

🧠 Advanced Use Cases¶

Feature	Description
Similarity-Based Prompt Generation	Planner agent generates a new prompt using closest 3 prompts
Vector-Based Blueprint Deduplication	Prevent generation of near-duplicate microservices
Multi-Vector Comparison	Blend code+test+doc vectors to find related modules
Agent Retry Reasoning	Use memory to avoid same failure cause in retry

✅ Summary¶

The semantic memory layer in ConnectSoft is a powerful accelerator and enabler of intelligence:

Uses vector databases to store and retrieve embeddings of prior knowledge
Powers similarity search across prompts, blueprints, docs, tests, and code
Enables agents to reason by analogy, reuse past outputs, and act with continuity
Indexed, filtered, and scoped by traceId, agentId, skillId, moduleId, projectId, and tenantId

This memory layer bridges the gap between static storage and dynamic, AI-first agent behavior.

Structured Project Memory Model¶

In ConnectSoft, every project has its own isolated, composable memory graph, which evolves as the project progresses from vision to release. This scoped memory enables agents to reason locally — with full context of the domain, blueprint, prompts, decisions, failures, and generated artifacts — without leaking across unrelated modules or tenants.

This section defines the project memory model, how it's structured, when it's created, and how it grows during the software lifecycle.

🧠 What Is a Project Memory?¶

A Project Memory is the collection of all knowledge modules and execution records tied to a single project:

Scoped by projectId
Anchored with a unique traceId
Tagged by sprint, module, blueprint, agent, and skill
Accessed by agents via memory SDKs
Visualized in Studio and orchestrators

Think of it as a self-contained knowledge universe for a SaaS product, service, or blueprint scope.

🧬 Project Memory Initialization¶

When a new project is created (via ProjectBootstrapOrchestrator or Studio), the system:

Issues a unique projectId and traceId
Initializes a structured directory in:
- Blob storage: /tenants/{tenantId}/projects/{projectId}/
- Metadata DB: project_memory table
Emits ProjectInitialized event
Bootstraps metadata entry:

{
  "projectId": "proj-2025-0012",
  "tenantId": "vetclinic",
  "name": "BookingService",
  "status": "initialized",
  "traceId": "trace-123",
  "createdBy": "Studio",
  "modules": [],
  "sprints": []
}

📦 What a Project Memory Contains¶

Category	Contents
🧠 Prompts	Initial user prompt, orchestrator intent, refined instructions
📄 Blueprints	Vision doc, service blueprint, domain models, API specs
📚 Documentation	Generated summaries, readmes, integration notes
🧪 Test Artifacts	Generated `.feature`, `.cs`, test results, retries, logs
🔧 Generated Code	Domain, application, adapter, gateway, and infrastructure files
📜 Events & Contracts	Event contracts, OpenAPI specs, pub/sub configs
📊 Execution Metadata	All `execution-metadata.json` for every agent that contributed
💬 Feedback Records	Corrections, annotations, prompt overrides
📈 Telemetry	Span traces, errors, performance metrics, retry loops

🗂️ Project Folder Structure (Blob + Git)¶

/projects/
  proj-2025-0012/
    trace-123/
      blueprint/
        VisionDocument.md
        ServiceBlueprint.yaml
        DomainModel.json
      src/
        Application/
        Infrastructure/
      tests/
        Booking.feature
        TestResults.xml
      docs/
        README.md
        Architecture.md
      contracts/
        booking.openapi.yaml
        AppointmentBooked.event.json
      metadata/
        execution-metadata.json
        sprint-trace-matrix.json

✅ This layout is consistent across all services and agents.

🔄 Project Memory Lifecycle Phases¶

Phase	Events Triggered	Memory Artifacts Added
Initialization	`ProjectInitialized`	project metadata scaffolded
Vision & Planning	`VisionDocumentCreated`	`VisionDocument.md`, initial prompt
Architecture & Modeling	`BlueprintCreated`, `DomainModelEmitted`	service blueprint, context map, domain aggregates
Code Generation	`MicroserviceScaffolded`, `ApiContractGenerated`	source code, contracts, OpenAPI
Testing	`TestSuiteGenerated`, `TestPassed/Failed`	`.feature` files, logs, results
Documentation	`DocumentationGenerated`	markdown files, diagrams, architecture summaries
Deployment	`ReleaseTriggered`, `InfraPlanCreated`	YAMLs, Bicep, container metadata, deployment logs
Feedback Loop	`AgentFailed`, `PromptRefined`	new prompt versions, regeneration trace

🔍 Traceability Across Project Memory¶

Every artifact in the project memory is linked to:

agentId: Who generated it
skillId: Which skill or plugin was used
traceId: Which orchestration flow it belonged to
moduleId: Which feature or service it supports
version: Incremental version of the artifact
status: success, failed, regenerated

✅ Enables full history reconstruction and replay for CI/CD, debugging, audits, or retraining.

🔁 Project-Level Memory Query¶

Agents can scope memory queries like:

{
  "projectId": "proj-2025-0012",
  "moduleId": "booking-service",
  "type": "blueprint",
  "agentId": "solution-architect"
}

→ This returns all structured and embedded blueprints created during the planning phase.

📊 Studio & Orchestrator Access¶

The Studio and coordinator agents use project memory for:

Snapshot visualizations
Prompt regeneration with embedded feedback
PR trace visualization (CodeCommitterAgent)
Prompt diffing, history tracking, and feedback inspection
Memory replay for new editions or module variants

🔐 Tenant-Scoped Isolation¶

Project memory is always scoped by:

tenantId
projectId
moduleId (when applicable)

This guarantees:

No memory leakage across tenants
Safe reuse within context
Strict RBAC for agent memory access

✅ Summary¶

Every project in ConnectSoft has its own scoped memory universe:

Begins at project creation and evolves over time
Records every prompt, blueprint, artifact, test, and document
Enables traceability, feedback-driven refinement, and intelligent reuse
Powers the event-driven, AI-first lifecycle through memory hooks

This structured memory enables agents to generate entire products with contextual awareness, continuity, and correctness.

Knowledge Schema and Metadata¶

Every piece of knowledge stored in ConnectSoft’s memory system — whether it's a .cs file, .md document, OpenAPI spec, or .feature test — is paired with a rich metadata schema. This metadata ensures traceability, retrievability, observability, and reuse.

This section defines the structure and usage of the Knowledge Metadata Model used across all memory modules.

🧠 Why Metadata Matters¶

Metadata is the index and signature of every memory artifact. It allows agents and humans to:

Filter, query, and retrieve relevant knowledge
Reconstruct execution context and origin
Link outputs to prompts, skills, and agent decisions
Determine reuse opportunities based on similarity and history
Enforce lifecycle, retention, and governance policies

Without metadata, memory is unstructured data. With metadata, it becomes a living intelligence map.

📦 Core Knowledge Metadata Fields¶

Each memory module — regardless of its format — includes the following required fields in its metadata:

Field	Type	Description
`memoryId`	string	Unique ID for this memory entry
`projectId`	string	Which project this memory belongs to
`moduleId`	string	Which microservice/module it is associated with
`traceId`	string	Which execution flow it was generated from
`agentId`	string	Agent that emitted this knowledge
`skillId`	string	Skill or plugin used to generate the artifact
`type`	enum	One of: `code`, `test`, `doc`, `blueprint`, `contract`, etc.
`filePath`	string	Path in blob or Git where the file resides
`version`	string	Semantic version of the artifact (e.g., `1.0.3`)
`createdAt`	datetime	UTC timestamp of creation
`tags`	string[]	Array of tags for filtering and retrieval
`source`	enum	`generated`, `imported`, `curated`, `retrieved`
`status`	enum	`success`, `failed`, `partial`, `deprecated`, etc.
`tenantId`	string	Tenant associated with the memory
`semanticHash`	string	Hash of the original content (used for similarity and diffing)
`embeddingId`	string	Link to vector DB embedding (if available)
`linkedTo`	string[]	List of related memory IDs (e.g., test linked to blueprint)

📘 Example Metadata JSON¶

{
  "memoryId": "mem-1234abcd",
  "projectId": "proj-2025-0012",
  "moduleId": "booking-service",
  "traceId": "trace-abc-2025",
  "agentId": "backend-developer",
  "skillId": "GenerateHandler",
  "type": "code",
  "filePath": "src/Application/Handlers/BookAppointmentHandler.cs",
  "version": "1.0.3",
  "createdAt": "2025-05-12T14:32:17Z",
  "tags": ["handler", "booking", "command"],
  "source": "generated",
  "status": "success",
  "tenantId": "vetclinic",
  "semanticHash": "1fe6be...",
  "embeddingId": "embed-98f22e",
  "linkedTo": ["mem-9876fff", "mem-3322abc"]
}

🔖 Metadata Extension Fields (Optional)¶

Agents and curators can enrich metadata with additional properties:

Field	Description
`usageCount`	Number of times this memory was retrieved
`feedbackRating`	Curated score (1–5) from human review
`curatedBy`	User who approved or enhanced the artifact
`regenerationCount`	Number of regeneration attempts for this output
`blueprintId`	The blueprint document that defined this need
`milestoneId`	Sprint or milestone that included this memory
`auditTrail`	Chain of agent decisions or skill lineage

🧩 Metadata Storage Locations¶

🔹 Structured Metadata DB (CosmosDB / PostgreSQL): primary source of indexed records
🔹 Blob Metadata Layer: stored as sidecar execution-metadata.json or artifact.meta.json
🔹 Git Commits: can include metadata in commit message footer or .meta.json in repo
🔹 Vector DB: stores a subset of metadata used during retrieval (e.g., type, tags, agentId)

🔍 Querying Knowledge by Metadata¶

Agents, Studio, and orchestrators can filter memory using metadata fields:

{
  "agentId": "backend-developer",
  "skillId": "GenerateHandler",
  "projectId": "proj-2025-0012",
  "moduleId": "booking-service",
  "type": "code",
  "tags": ["appointment", "handler"]
}

→ Returns all code memory entries created by the Backend Developer Agent during the handler generation for this project/module.

⚠️ Metadata Validation Rules¶

memoryId, projectId, agentId, and traceId are mandatory
version must conform to semantic versioning (e.g., 1.0.0, 2.1.3)
type must be selected from controlled vocabulary:
code, test, doc, blueprint, infra, contract, event, prompt, metadata
status is validated against execution results or failure events
semanticHash must be generated from artifact content using SHA-256 or similar
embeddingId must exist if memory is in vector DB

🔒 Security and Isolation Fields¶

To support multi-tenant memory safety:

tenantId ensures data is isolated per customer
RBAC policies filter memory by projectId + agentId + type
Access tokens in Studio and Orchestrator API are scoped accordingly

🧠 Metadata Inheritance & Propagation¶

Some fields are inherited automatically when generating derived artifacts:

Parent Memory	Inherited by
`ServiceBlueprint.yaml`	All generated handlers, DTOs, validators
`VisionDocument.md`	Contextual metadata for product spec
`AppointmentBooked.event.json`	Linked test cases and message consumers

This supports lineage tracking and blueprint-to-output auditability.

✅ Summary¶

The Knowledge Metadata Schema defines how ConnectSoft:

Indexes, queries, and traces every knowledge artifact
Ensures tenant isolation, artifact governance, and data versioning
Links memory across agents, traces, modules, and generations
Supports semantic + structured memory fusion across all storage layers

This schema enables the platform to understand not just what was created — but why, when, by whom, and how it can be reused.

Memory-Oriented Agent Behaviors¶

In ConnectSoft, every agent is more than a code generator or prompt executor — it is a memory-aware cognitive entity. Agents leverage memory to enhance autonomy, avoid redundant work, reuse prior solutions, and continuously improve through feedback.

This section describes how different types of agents interact with the knowledge and memory system, including how they query, write, refine, and react to memory during execution.

🧠 Memory as a First-Class Input¶

Before performing a skill or task, every agent queries the memory system to retrieve relevant knowledge based on:

projectId, traceId, moduleId
skillId, type, agentId
Prompt history
Related artifacts (via embedding similarity)

Agents never operate in isolation — they act within a semantic and historical context retrieved from memory.

🧩 Agent Memory Behaviors: Modes¶

Behavior	Description
Memory Query	Find similar blueprints, code, tests, docs, or prior prompts
Memory Write	Persist generated artifacts with full metadata + trace context
Memory Feedback Emit	Mark artifacts as failed, outdated, or needing regeneration
Memory Enrichment	Suggest better tags, references, or annotations
Memory Diffing	Compare current output with prior memory for reuse or improvement

🤖 Agent Examples and Their Memory Usage¶

1. Vision Architect Agent¶

📥 Retrieves similar vision documents
📤 Stores VisionDocument.md with strategic goals
🧠 Learns which language patterns yield best blueprints

2. Product Manager Agent¶

📥 Reuses product requirement prompts from similar domains
📤 Persists feature list and backlog artifacts
🧠 Annotates modules with tags: feature, requirement, edition

3. Solution Architect Agent¶

📥 Queries prior service blueprints for similar business contexts
📤 Stores ServiceBlueprint.yaml and ContextMap.json
🧠 Reuses aggregate patterns from project memory

4. Backend Developer Agent¶

📥 Retrieves similar handler code or DTO classes
📤 Stores .cs files with metadata per layer and aggregate
🧠 Avoids regenerating duplicate services

5. Test Generator Agent¶

📥 Pulls .feature files for similar use cases
📤 Persists new tests with structured BDD metadata
🧠 Avoids generating overlapping tests via semantic match

6. QA Engineer Agent¶

📥 Queries test memory and failure logs
📤 Emits result logs, annotated failure summaries
🧠 Marks unstable flows for review or regression coverage

7. Cloud Provisioner Agent¶

📥 Finds matching Bicep or YAML configs for similar blueprints
📤 Stores infrastructure as code artifacts under infra type
🧠 Reuses deployment patterns across tenants and projects

8. Knowledge Management Agent¶

📥 Clusters related knowledge chunks
📤 Enhances memory with tags, summaries, linked references
🧠 Tracks gaps and emits MemoryImprovementSuggestion

🔄 Memory Lifecycle Hooks in Agent Execution¶

Each agent interacts with memory across these phases:

Phase	Action
Pre-Execution	Semantic + structured memory query (SK + filters)
Execution	Uses memory to shape output (e.g., “copy this pattern”)
Post-Execution	Stores results with full metadata
Post-Failure	Emits `AgentFailed` with diagnostic memory reference
Review/Feedback	Accepts suggestions from other agents or Studio

✅ These hooks are automatically managed by orchestrators and agent scaffolding libraries.

🧬 Skill-Aware Memory Scoping¶

Each skill (function or plugin) has access to scoped memory entries based on:

agentId, skillId, projectId, moduleId
type (e.g., test, blueprint, doc)
Optional filters like tags or semantic similarity

{
  "skillId": "GenerateOpenApi",
  "filters": {
    "type": "contract",
    "tags": ["booking", "appointment"]
  }
}

🧠 How Agents Use Memory to Adapt¶

Scenario	Agent Behavior
Similar use case found	Agent reuses code/test/doc with slight adaptation
No prior memory found	Agent generates from scratch
Failure occurred on prior attempt	Agent adjusts prompt or strategy based on `AgentFailed` memory
Blueprint changes upstream	Agent invalidates cached outputs, regenerates from updated input
Memory was flagged by user or feedback	Agent uses feedback to retrain or suggest replacement

📘 Real-World Interaction Example¶

Prompt: "Generate a service for appointment cancellations."

Flow:

Solution Architect Agent retrieves similar ServiceBlueprint.yaml from project memory
Backend Developer Agent queries handlers related to cancel operations
Test Generator Agent finds existing .feature files from cancellation tests
All new outputs are persisted with links to retrieved memory
Knowledge Management Agent clusters the cancellation-related modules for reuse

🔐 Security, Scoping, and Isolation¶

Agents can only access memory within their authorized boundary:

Scoped by tenantId, projectId, moduleId, agentRole
Cannot retrieve or overwrite memory outside scope
Sensitive prompts or results may be flagged as private

Agents operate under strict memory governance enforced by metadata filters and orchestrator policies.

✅ Summary¶

Memory-aware agents are at the heart of ConnectSoft’s automation strategy. Each agent:

Reads memory before acting
Writes memory after generating outputs
Links outputs to inputs, context, and trace
Learns from memory via feedback and failure signals
Collaborates via memory graphs and shared execution state

This architecture ensures that the factory behaves like an intelligent, evolving system, not just a template engine.

Event-Driven Ingestion¶

In ConnectSoft, the creation and population of memory is not a manual step — it is an automated consequence of event-driven execution. Every meaningful state transition in the software factory — whether a prompt is submitted, a handler is generated, or a test fails — is captured through an event, which in turn triggers memory ingestion.

This section explains how the event-driven architecture (EDA) powers the memory system, ensuring that all knowledge is emitted, recorded, and enriched without human intervention.

⚡ Memory Is Triggered by Events¶

“If it matters, it emits an event. If it emits an event, it creates memory.”

The memory ingestion pipeline listens to lifecycle, domain, and agent events, such as:

Event	Triggers Memory Creation For
`ProjectInitialized`	Project metadata scaffold
`VisionDocumentCreated`	Vision document `.md`
`ServiceBlueprintCreated`	Blueprint YAML + inferred domain model
`MicroserviceScaffolded`	Code files + tests + infra
`AgentOutputGenerated`	Any `.cs`, `.feature`, `.yaml`, `.json` file
`PromptRefined`	Revised prompt → memory snapshot
`AgentFailed`	Execution trace + logs + failure diagnostic memory
`TestSuiteGenerated`	`.feature` files with BDD structure
`DocumentationGenerated`	`README.md`, `Architecture.md`, generated doc pages
`ExecutionCompleted`	`execution-metadata.json` with links to all outputs

🧠 Event-Driven Memory Flow¶

sequenceDiagram
    participant Agent
    participant EventBus
    participant MemoryIngestor
    participant VectorDB
    participant MetadataDB
    participant BlobStorage

    Agent->>EventBus: Emit AgentOutputGenerated
    EventBus->>MemoryIngestor: Deliver event with payload
    MemoryIngestor->>BlobStorage: Store raw files
    MemoryIngestor->>VectorDB: Embed and store vector (if textual)
    MemoryIngestor->>MetadataDB: Index metadata record
    MemoryIngestor->>EventBus: Emit MemoryStored

Hold "Alt" / "Option" to enable pan & zoom

→ This pattern applies to every artifact-producing agent or orchestrator.

🧩 Examples of Event-Powered Ingestion¶

🧠 From Agent Execution¶

Backend Developer Agent emits AgentOutputGenerated
Memory ingestor captures .cs files
Stores in:
- Blob: /src/Application/Handlers/*.cs
- Metadata DB: with traceId, skillId, agentId
- Vector DB: if summary or docstring is extracted

📄 From Blueprint Creation¶

Solution Architect Agent emits ServiceBlueprintCreated
ServiceBlueprint.yaml is ingested as:
- Structured memory (type: blueprint)
- Embedded document for semantic recall
- Tagged with boundedContext, aggregates, eventTypes

🧪 From Test Failure¶

TestRunner emits TestFailed event
Memory record created with:
- Linked .feature file
- Failure logs
- Feedback flag
- Status: failed

🔍 Memory Indexing from Events¶

Every event includes metadata for indexing:

{
  "event": "AgentOutputGenerated",
  "agentId": "backend-developer",
  "traceId": "trace-abc",
  "skillId": "GenerateHandler",
  "projectId": "proj-001",
  "moduleId": "booking-service",
  "outputs": [
    "BookAppointmentHandler.cs",
    "CommandValidator.cs"
  ]
}

The Memory Ingestor service:

Parses outputs
Generates metadata records
Embeds documents if textual
Stores blob pointer
Links to execution metadata

🔁 Idempotent Ingestion via Event Replay¶

All ingestion steps are idempotent and event-replayable:

Events are stored durably in an event log
Memory ingestion can be replayed or backfilled
Coordinators can issue ReplayMemoryFor(traceId) to restore memory for a failed or expired flow

✅ Enables full rebuild of memory state from history.

📊 Observability of Ingestion Events¶

Every ingestion emits:

MemoryStored
MemoryLinked
EmbeddingCreated
MemoryIngestFailed

These are shown in:

Studio trace explorer
Observability dashboards
Audit logs (per tenant, project, artifact)

🧱 Orchestrator Integration¶

All orchestrators — e.g., MicroserviceAssemblyCoordinator, MilestoneLifecycleCoordinator — are memory-ingestion-aware:

They wait for MemoryStored events before advancing FSMs
They link FSM state to ingested memory (fsm-metadata.json)
They annotate decisions with memory references

🔐 Governance & Security¶

Event-driven ingestion respects:

tenantId, projectId, agentId scope
Memory redaction policies (e.g., redact prompt if sensitive = true)
RBAC enforcement at ingestion time

Memory is never ingested globally — always scoped and access-controlled.

✅ Summary¶

In ConnectSoft:

Memory is created automatically through the event system
Every output-producing action emits an event
Events trigger ingestion into blob, vector DB, and metadata stores
Memory stays traceable, observable, and replayable

This event-driven approach ensures that every blueprint, handler, test, and document becomes memory without extra effort — enabling full-context reuse and intelligent agent behavior at scale.

Per-Module Memory¶

In ConnectSoft, every generated module — whether it's a microservice, gateway, library, adapter, test suite, or infrastructure definition — has its own scoped memory zone. This enables agents to reason about, retrieve, regenerate, or improve a module in isolation, while preserving traceability across the broader project lifecycle.

This section describes how per-module memory is defined, structured, retrieved, and used in orchestration.

🧱 What Is a Module in ConnectSoft?¶

Modules are independent, versionable, observable, and reusable units that represent logical or technical boundaries. Examples include:

Module Type	Examples
🧱 Microservice	`BookingService`, `InvoiceService`
🌐 API Gateway	`Gateway.Api.v1.yaml`
📚 Library	`ConnectSoft.Extensions.Http.OAuth2`
📦 Adapter	`TwilioSmsAdapter`, `S3UploaderAdapter`
📊 Test Suite	`BookingService.Tests`
🛠 Infra Module	`booking-db.bicep`, `queue.bicep`
🔧 Domain Contract	`AppointmentBooked.event.json`

✅ All of these are first-class memory scopes.

📦 Memory Structure per Module¶

Each module has:

📁 A physical folder in blob storage and/or Git:
```
/projects/{projectId}/modules/{moduleId}/
```
🧾 A logical index of all memory artifacts tied to moduleId
📄 Individual files (e.g., code, tests, configs, contracts)
🧠 Semantic memory embeddings (if textual)
🔗 Links to execution metadata (traceId, agentId, skillId)

📘 Example Module Memory Structure¶

/modules/
booking-service/
  blueprint/
    ServiceBlueprint.yaml
  src/
    Application/
      Handlers/
        BookAppointmentHandler.cs
        CancelAppointmentHandler.cs
  tests/
    CancelAppointment.feature
    test-log.json
  docs/
    README.md
    Architecture.md
  infra/
    booking-db.bicep
  metadata/
    execution-metadata.json
    memory-index.json

This folder is scoped to moduleId: booking-service.

🔖 Metadata Anchoring by Module¶

All memory entries include:

{
  "moduleId": "booking-service",
  "projectId": "proj-2025-0012",
  "traceId": "trace-xyz",
  "type": "code",
  "agentId": "backend-developer",
  "skillId": "GenerateHandler"
}

This allows:

Full-text + vector + structured queries scoped to module
Trace reconstruction of who/what/when generated the module
Triggering regeneration flows at the module boundary

🔄 Why Per-Module Memory Matters¶

Capability	Benefit
🔁 Selective regeneration	Rebuild only the `NotificationService`, not the whole system
🔍 Targeted memory search	Agents don’t get overwhelmed by unrelated data
⚠️ Failure isolation	Failures tied to a specific `moduleId` can be retried safely
🧪 Modular testing and validation	QA agents can scope to `tests/` of a specific module
📦 Reuse and versioning	Modules can be reused across projects and tenants
🧠 Semantic context precision	Embeddings stored per module = better relevance

🧠 Agent Use Case: Module Memory Query¶

{
  "moduleId": "booking-service",
  "type": "blueprint",
  "tags": ["appointment", "handler", "cancel"]
}

Returns all relevant memory for cancellation logic inside the booking module — including handler code, test cases, and prior failure diagnostics.

🧩 Studio Use Case: Module Memory Dashboard¶

In Studio, each module displays:

✅ Memory size (file count, MB)
✅ Agent contributors (who generated what)
✅ Version history of code and blueprints
✅ Test pass/fail rates
✅ Links to Git commits, blob storage, and execution events
✅ Option to regenerate module memory or clear stale entries

🔁 Ingestion and Retrieval Flow¶

Agent generates artifacts for booking-service
Emits AgentOutputGenerated with moduleId
Memory ingestor:
- Stores files in /modules/booking-service/
- Embeds textual files to vector DB
- Indexes metadata tagged with moduleId
Future agents use this moduleId to retrieve memory context

🔐 Access Control and Isolation¶

Each module’s memory is governed by:

tenantId (multi-tenant isolation)
projectId (cross-project protection)
moduleId (agent-scoped boundary)
agentId/skillId (role-based filtering)

Agents can’t access modules they didn’t help generate unless permissions allow (e.g., Test Generator may see all modules).

📊 Observability and Traceability¶

Each module emits:

MemoryStored (per artifact)
ModuleMemoryUpdated
ExecutionMetadataCreated

These link to:

Execution trace graphs
Sprint milestone dashboards
Studio’s module memory view

✅ Summary¶

Per-module memory is a critical capability that enables:

Isolated generation, regeneration, and reuse
Scoped and secure memory access
Agent specialization per module
Traceability, modular testing, and performance analysis
Evolution of each SaaS component as an autonomous unit

Per-Agent Memory¶

In ConnectSoft, each AI agent is not a stateless function — it’s a cognitive persona with its own memory of past actions, outputs, prompts, decisions, and context-specific knowledge. This memory enables agents to act intelligently, consistently, and adaptively across tasks, modules, and projects.

This section defines how agents maintain personal memory scopes — to reason from history, avoid redundant work, reuse successful outputs, and learn from feedback.

🧠 What Is Per-Agent Memory?¶

Per-agent memory is the subset of the memory system scoped to an individual agent, including:

All outputs generated by that agent
Artifacts created via specific skills or responsibilities
Prompts used and refined
Failures, retries, and corrections
Skill-specific examples, code snippets, templates, and usage patterns

Think of it as the agent’s personal journal — structured, semantic, and versioned.

🤖 What It Enables¶

Capability	Description
✅ Output reuse	Reuse previously generated handlers, prompts, test cases
✅ Prompt refinement	Reuse or improve earlier prompt strategies
✅ Skill grounding	Use skill-specific examples during execution
✅ Learning from failure	Adjust generation strategy based on `AgentFailed` memory
✅ Avoiding redundancy	Prevent agent from regenerating what it already produced
✅ Personalized behavior	Remember preferences, conventions, patterns per agent-role-skill

📦 What Per-Agent Memory Includes¶

Memory Type	Description
`promptHistory`	Prompt/response pairs used by the agent
`artifactHistory`	Files and outputs generated by the agent
`execution-metadata`	Metadata files (duration, status, traceId)
`skillMemory`	Templates, examples, reusable logic per skill
`feedbackLoop`	Past failures, feedback, and correction attempts
`usageStats`	Frequency, reuse count, similarity metrics

🧾 Per-Agent Memory Key Fields¶

Field	Description
`agentId`	Unique agent identity (e.g., `qa-engineer`, `test-generator`)
`skillId`	Skill/function used (e.g., `GenerateHandler`, `ComposeTest`)
`projectId`	Optional scope (if tied to a specific project)
`traceId`	Which execution trace produced this output
`version`	Version of the agent or skill logic
`status`	`success`, `failed`, `retried`, `feedbackAccepted`
`memoryId`	ID of stored memory entries linked to this agent

🧩 Agent Skill Memory Example¶

Let’s take the Test Generator Agent.

It stores memory such as:

{
  "agentId": "test-generator",
  "skillId": "ComposeFeatureTest",
  "type": "test",
  "filePath": "tests/CancelAppointment.feature",
  "tags": ["appointment", "cancel", "edge-case"],
  "prompt": "Write a test for canceling appointments under 1 hour notice",
  "responseExcerpt": "Scenario: Cancel within one hour",
  "feedback": "Generated invalid step syntax. Regenerated successfully.",
  "status": "success"
}

✅ This allows future generations to adapt, reuse, or avoid past mistakes.

🔄 Per-Agent Feedback Loop¶

When an agent fails or is corrected:

AgentFailed or AgentFeedbackSubmitted event is emitted
Memory is updated with:
- Failure context
- Prompt, output, logs
- Correction or regenerated version
Agent learns to:
- Retry with a different skill
- Use a new prompt template
- Avoid the failed approach on similar inputs

🔍 Agent Memory Retrieval Example¶

When a planner invokes api-designer, the orchestrator injects memory:

{
  "agentId": "api-designer",
  "skillId": "GenerateOpenApi",
  "filters": {
    "type": "contract",
    "tags": ["booking", "appointment"]
  },
  "topK": 3
}

✅ The agent receives the 3 most similar OpenAPI specs it previously created, to influence the new one.

📚 Studio Support for Agent Memory¶

In the Studio dashboard:

📊 View all memory entries generated by an agent
🧠 Browse past prompt-response pairs
⚠️ See failures and retry decisions
🔁 Trigger manual feedback or regeneration
🧱 Visualize which skills contribute the most memory

🛠 Agent Templates Using Personal Memory¶

Agents use memory in:

💡 Planning: choosing blueprint strategy
🧪 Generation: grounding outputs with successful past results
🔁 Retry: switching strategy after failure
📤 Documentation: composing based on examples
🔗 Test Creation: adapting reusable test templates

🔐 Agent-Specific Isolation Rules¶

Each agent can read and write only to its own agentId memory zone
Orchestrators may inject memory across agents if permitted (e.g., reuse test memory in blueprint planning)
Memory is tenant-scoped and project-aware

✅ Summary¶

Every ConnectSoft agent maintains a rich personal memory scope that includes:

Prior prompts, outputs, and feedback
Generated files and test cases
Semantic embeddings and trace metadata
Skill-specific artifacts and usage patterns

This enables agents to be:

Smarter with every run
Less redundant over time
More reusable across blueprints, features, and services

Per-Project Memory¶

In ConnectSoft, every SaaS product, module collection, or customer-specific solution is anchored to a Project, and each project maintains a complete, unified memory scope that reflects everything that was planned, generated, tested, failed, refined, or reused across its lifecycle.

This section focuses on how per-project memory enables end-to-end traceability, context-rich orchestration, reuse across modules, and studio-level navigation for intelligent agent collaboration.

🧠 What Is Per-Project Memory?¶

A Project Memory is a container for all knowledge and memory modules tied to a single projectId, including:

Memory Type	Contents
💬 Prompts	Vision prompt, blueprint instructions, refined queries
📄 Blueprints	Vision docs, service definitions, context maps, event topologies
🔧 Code & Artifacts	Handlers, DTOs, APIs, infra, adapters, libraries
📚 Documentation	Markdown docs, `README.md`, `HowItWorks.md`, API summaries
🧪 Test Suites	`.feature` files, test results, logs, validations
📜 Contracts & Specs	OpenAPI, domain events, queue and pub/sub interfaces
📊 Execution Metadata	All `execution-metadata.json`, `sprint-trace-matrix.json`, `fsm-state`
🧠 Agent Reasoning Data	Skill decisions, retries, feedback chains

✅ This memory scope evolves from ProjectInitialized through every milestone and release.

🧩 Project Memory Hierarchy¶

Memory is structured hierarchically per project:

/tenants/
  vetclinic/
    projects/
      booking-platform/
        projectId: proj-2025-0009
        traceId: trace-xyz
        modules/
          booking-service/
          notification-service/
          identity-gateway/
        tests/
          Booking.feature
        blueprints/
          VisionDocument.md
          ServiceBlueprint.yaml
        metadata/
          execution-metadata.json
          sprint-trace-matrix.json

🔖 Metadata Fields Anchored to Project¶

Every memory entry (code, doc, test, contract) is indexed by:

Field	Role
`projectId`	Defines project memory scope
`traceId`	Ties execution flows to the project
`tenantId`	Ensures multi-tenant isolation
`moduleId`	Scoped child memory under the project
`agentId`	Who generated the memory inside the project

🔍 Agent Memory Filtering by Project¶

Agents can query memory with:

{
  "projectId": "proj-2025-0009",
  "moduleId": "booking-service",
  "type": "test",
  "tags": ["cancel", "appointment"]
}

→ Retrieves test cases for cancellation flows inside the current project.

🧠 How Project Memory Enables Smart Agents¶

Scenario	How Project Memory Helps
Generate new module	Provides blueprint context, naming, domain boundaries
Regenerate failed artifact	Retrieves prompt, context, and prior version
Test case creation	Suggests tests from sibling modules in the same project
Retry agent failures	Pulls prior inputs, outputs, logs, and enriched corrections
Edition versioning	Applies memory snapshots from previous edition

🔄 Studio Features Using Project Memory¶

Feature	Description
📚 Memory Browser	Browse all artifacts in a project
🧠 Prompt Replay	Rerun or refine original prompt with new skills
📊 Execution History Viewer	View all agent actions across modules
🧪 Test Coverage Map	See which modules are tested, which are failing
📦 Knowledge Export	Export project memory as reproducible package
🔁 Memory Replay	Replay trace to regenerate artifacts on new stack

🔄 Lifecycle Evolution of Project Memory¶

Phase	Events Triggered	Memory Modules Created
🔹 Init	`ProjectInitialized`	project metadata scaffold
🧠 Planning	`VisionDocumentCreated`, `BlueprintCreated`	vision/blueprint documents
⚙️ Generation	`MicroserviceScaffolded`, `AdapterGenerated`	code files, APIs, tests
🧪 Testing	`TestSuiteGenerated`, `TestFailed`	`.feature`, logs, retry metadata
📄 Documentation	`DocumentationGenerated`	`README.md`, integration notes
🚀 Deployment	`ReleaseTriggered`, `InfraPlanCreated`	bicep, container config, deployment summary

🔐 Security and Multi-Tenant Memory Partitioning¶

Each project memory is isolated via:

tenantId
projectId
RBAC per agent or user
Studio-scoped access tokens

No agent may access another tenant’s project memory unless explicitly permitted.

🧭 Orchestration + Project Memory¶

Orchestrators store per-project:

fsm-state.json
milestone-checklist.json
execution-metadata.json

They retrieve memory scoped to the project at each milestone:

{
  "projectId": "proj-2025-0009",
  "milestoneId": "M4-Generate-Infra",
  "type": "infra",
  "moduleId": "booking-service"
}

✅ Summary¶

Per-project memory enables:

Unified traceable history for every artifact and decision
Intelligent reuse across modules within the same blueprint
Full lifecycle replay, test coverage, and refinement tracking
Modular orchestration and milestone-based retrieval
Studio-powered visibility and human-AI collaboration

Project memory turns your blueprint into a living, growing knowledge graph — not just a one-time generation.

Versioning of Knowledge Artifacts¶

In ConnectSoft, memory is not static. As projects evolve, agents regenerate modules, refine prompts, fix bugs, replan blueprints, or adapt infrastructure — all of which produce new versions of artifacts. Every meaningful knowledge module is versioned, ensuring auditability, rollback, reuse, and continuous improvement.

This section describes how memory artifacts are versioned, how versions are tracked, diffed, compared, and how agents and orchestrators handle version-aware memory.

🔢 Why Versioning Matters¶

Versioning enables:

🔁 Safe regeneration of memory without overwriting past outputs
🔍 Tracking the evolution of modules, prompts, and decisions
📊 Comparing results between blueprint versions or AI skills
💬 Explaining “what changed” and “why it was changed”
✅ Reverting failed generations or experiments to a stable state

📦 Versionable Knowledge Artifacts¶

All of the following memory items are versioned:

Artifact Type	Versioned?	Notes
`ServiceBlueprint.yaml`	✅ Yes	Versioned per blueprint evolution
`.cs` source files	✅ Yes	New handler = new semantic version
`.feature` tests	✅ Yes	Each regenerated test creates a new test version
`.bicep`, `.yaml`	✅ Yes	Infra-as-code tracked with version bumps
`README.md`	✅ Yes	Auto-documented outputs are versioned too
`execution-metadata.json`	✅ Yes	Stored per run with version trace context

🧠 Version Fields in Metadata¶

Each memory module includes:

{
  "memoryId": "mem-xyz",
  "version": "1.3.0",
  "source": "generated",
  "status": "success",
  "isLatest": true,
  "previousVersionId": "mem-abc",
  "changeType": "refactor",
  "changeReason": "Prompt refined after test failure"
}

✅ These fields form the version chain for every artifact and enable safe navigation.

📘 Semantic Versioning Model¶

ConnectSoft uses SemVer style versioning for memory:

MAJOR: Breaking change in contract, signature, or input/output shape
MINOR: Additive or refinable content (e.g., more test cases)
PATCH: Cosmetic or internal improvement without contract change

Example:

Version	Description
`1.0.0`	Initial agent-generated blueprint
`1.1.0`	Added fallback route, more events
`1.1.1`	Removed unused handler, fixed typo
`2.0.0`	Entire new contract schema

🔍 Version Resolution for Agents¶

When agents fetch memory, they may:

Use only the isLatest: true version
Fetch all historical versions for reasoning
Query by version >= 1.2.0 AND < 2.0.0
Detect and diff between v1.0.0 and v2.0.0

→ This allows flexible retrieval strategies based on context, risk tolerance, and trace history.

🧩 Examples of Versioned Artifacts¶

Service Blueprint¶

version: 1.2.0
projectId: proj-abc
traceId: trace-123
moduleId: invoice-service
generatedBy: solution-architect
changes:
  - Added LateFeePolicy aggregate
  - Refactored PaymentProcessing handler

Test File¶

tests/Booking.feature
v1.0.0 - initial case: cancel appointment
v1.1.0 - added edge case: cancel within 1 hour
v1.1.1 - fixed scenario title casing

🔄 Diffing Between Versions¶

When a new version is generated:

The Memory Ingestor computes a semanticHash
Diffs are calculated at the:
File level (textual diff)
Semantic level (e.g., contract shape, behavior change)
Stored as diff.json in the memory folder
Studio renders it as a changelog preview

🔁 Versioning in Studio¶

Users and agents in Studio can:

🕹️ Select a specific memory version
🔍 View changelog and diff preview
🔄 Revert to a prior version
🧪 Run regression tests between versions
📦 Snapshot all modules at version X for backup or edition packaging

🧠 Agents Can Trigger Version Bumps¶

Agents emit versioned artifacts when:

A prompt was changed
A failure required regeneration
A blueprint was updated
A new skill version was used

Coordinators enforce bumpVersion = true if major change detected.

🧾 Versioned Memory Paths¶

Stored per module:

/modules/booking-service/src/v1.0.0/BookAppointmentHandler.cs
/modules/booking-service/src/v1.1.0/BookAppointmentHandler.cs
/modules/booking-service/tests/v1.0.0/Cancel.feature

Each path is linked to its memoryId, version, and trace.

✅ Summary¶

In ConnectSoft, every knowledge artifact is:

Versioned and traceable
Linked to events, prompts, agents, and changes
Retrievable and diffable
Revertible and reusable across projects and editions

This ensures that memory evolves intelligently, safely, and observably — empowering agents to generate software like an iterative, memory-aware development team.

Multi-Project Knowledge Graph¶

ConnectSoft is designed to operate at massive scale — building and evolving thousands of SaaS services, libraries, and templates across industries, domains, and tenants. To support this scale while maximizing reusability and intelligence, the platform constructs a multi-project knowledge graph — a cross-project memory index that connects blueprints, tests, prompts, services, and contracts by meaning, lineage, and modular structure.

This section defines how ConnectSoft builds and queries a global memory graph that spans all projects without breaking tenant boundaries or module traceability.

🌐 What Is the Multi-Project Knowledge Graph?¶

A multi-project knowledge graph is a semantic, structural, and traceable web of memory nodes representing:

💡 Prompts and input intent
📄 Blueprints and context maps
⚙️ Microservice modules and adapters
📚 Libraries and templates
🧪 Tests and validations
📜 Contracts and API interfaces
📊 Feedback and failure diagnostics

Each node is versioned, typed, and scoped — and linked to related nodes via:

🔗 Similarity (vector-based)
🔗 Reuse (shared template, event, code pattern)
🔗 Causality (agent generated from blueprint)
🔗 Correction (version B fixed version A)
🔗 Composition (module A depends on module B)

📘 Graph Structure Example¶

graph TD
  Vision1["Vision: Booking"]
  Blueprint1["Blueprint: BookingService v1.1"]
  Handler1["Handler: BookAppointmentHandler.cs"]
  Test1["Test: Booking.feature"]
  Prompt1["Prompt: Generate service for booking"]

  Vision1 --> Blueprint1
  Blueprint1 --> Handler1
  Handler1 --> Test1
  Prompt1 --> Blueprint1
  Test1 -->|Similar to| Test2["Test: ReserveTable.feature"]

Hold "Alt" / "Option" to enable pan & zoom

→ These nodes span multiple projects but share a semantic relationship.

🧠 What the Graph Enables¶

Use Case	Benefit
🧬 Reuse Discovery	Find prior handlers/tests with same domain or structure
🔁 Similar Blueprint Reuse	Suggest blueprints from other projects to avoid duplication
🧪 Test Case Transfer	Apply validated tests from one context to another
💡 Prompt Completion Aid	Use prompt fragments from similar modules
📊 Cross-Project Analytics	Find most reused templates, agents, or failure patterns
📦 Modular Knowledge Transfer	Recommend design patterns used in similar SaaS modules

🔎 Graph Construction Inputs¶

The knowledge graph is built by:

📥 Collecting metadata from every memory entry
🔁 Linking based on:
- type, tags, agentId, moduleId, skillId
- Semantic embedding similarity
- Manual curation or Studio feedback
🔁 Detecting references:
- Blueprint → module
- Test → contract
- Skill → output

✅ Stored in a graph database or semantic memory index with relationships (e.g., Azure Cosmos DB Gremlin API, Neo4j, or custom graph index on top of vector DB + metadata DB).

🧩 Cross-Project Knowledge Reuse Example¶

ProductManagerAgent initiates blueprint for PetInsuranceClaims
VisionArchitectAgent queries similar vision documents
Finds memory entries from another project:
- HealthInsuranceClaimsService
- ClaimApproved.event.json
Planner proposes reusing ClaimsBlueprint.yaml as a base
Agents adjust and generate domain-aligned variant

🔐 Isolation and Boundaries¶

Despite its global scope, the graph is tenant-aware and project-isolated by default:

Memory links are readable across projects only if:
- Same tenantId
- OR marked as publicReusable = true
Sensitive prompts and artifacts are flagged as private
Cross-project agent access is governed by RBAC policies

✅ Safe reuse without leakage across customer contexts.

🧠 Graph-Aware Agent Behavior¶

Agents can be configured with:

{
  "allowCrossProjectMemory": true,
  "reuseThreshold": 0.92,
  "filters": {
    "type": "test",
    "domain": "appointments"
  }
}

→ This enables semantic retrieval across projects with confidence scoring.

📚 Graph-Based Studio Features¶

Feature	Description
🔍 Memory Similarity Search	Search across projects for semantically similar items
📦 Module Reuse Suggestion	Recommend modules used in similar use cases
📄 Blueprint Comparison Viewer	Compare current vs historical blueprints graphically
🧪 Cross-Project Test Discovery	Suggest test cases used by similar services
🧠 Memory Graph Visualizer	Show how current artifact fits into global knowledge

📊 Knowledge Graph Metrics¶

🔢 Memory node count (by type, agent, module)
🔗 Average links per artifact (reuse density)
🔁 Most reused blueprints, templates, test cases
🧠 Common failure-to-fix patterns
📈 Diff evolution over project versions

These metrics help agents adapt, orchestrators optimize, and curators refine the overall memory ecosystem.

✅ Summary¶

The Multi-Project Knowledge Graph is a key enabler for:

Cross-project discovery and reuse
Agent intelligence evolution
Blueprint consistency and memory-driven planning
Studio-driven knowledge visibility and feedback loops

It turns memory into an interconnected knowledge fabric, powering autonomous orchestration at platform scale.

Documentation as Memory¶

In ConnectSoft, documentation is not external — it is a first-class memory artifact. Every .md file, design explanation, usage note, or auto-generated summary is treated as a versioned, retrievable, and queryable memory unit.

This section defines how all documentation — whether generated by agents or curated by humans — is ingested, tagged, linked, and reused as part of the platform’s knowledge fabric.

📘 What Qualifies as Documentation?¶

All of the following are considered documentation memory:

File Type	Purpose
`README.md`	Explains module usage, features, deployment
`VisionDocument.md`	Describes product goals, constraints, and actors
`Architecture.md`	Diagrams, components, layering, security notes
`ModuleOverview.md`	Summary of logic, skills, scope, responsibilities
`TestSummary.md`	BDD explanation, edge cases, coverage notes
`HowItWorks.md`	Internal behaviors, workflows, non-obvious flows
`IntegrationGuide.md`	API usage, contract structure, example flows

✅ All .md, .rst, and .txt files generated or stored per project/module.

🧠 Documentation = Knowledge Memory¶

Each doc file is:

Assigned a memoryId
Indexed by projectId, moduleId, agentId, skillId, traceId
Embedded into the vector database
Tracked in metadata DB with:
- type: doc
- tags: ["api", "architecture", "usage", "readme"]
- semanticHash, version, createdBy

→ It becomes queryable just like a code file or prompt.

📦 Documentation Memory Example¶

{
  "memoryId": "mem-doc-987",
  "type": "doc",
  "agentId": "documentation-writer",
  "filePath": "modules/booking-service/docs/README.md",
  "tags": ["booking", "handler", "architecture"],
  "version": "1.0.1",
  "traceId": "trace-booking-0123",
  "embeddingId": "embed-9f33",
  "status": "success"
}

✍️ Sources of Documentation in Memory¶

Source	Description
`Documentation Writer Agent`	Generates `README.md`, summaries, visual annotations
`Test Generator Agent`	Produces `TestSummary.md`, scenario notes
`Blueprint Generator Agent`	Outputs `VisionDocument.md`, `ContextMap.md`
`DevOps Coordinator Agent`	May emit `DeploymentSummary.md`, rollback instructions
✍️ Human Contributors	Studio users or engineers writing `.md` or `.txt` files

✅ All are ingested equally with version tracking.

🔄 Documentation Retrieval by Agents¶

Agents (or planners) may retrieve documentation memory using:

{
  "type": "doc",
  "projectId": "proj-001",
  "moduleId": "notification-service",
  "tags": ["architecture", "infrastructure"]
}

→ Fetches README.md and Architecture.md that describe the target module’s behavior.

💡 Use Cases for Documentation as Memory¶

Use Case	Behavior
📦 Bootstrap from prior README	Use `README.md` from similar module as a starting point
🧠 Prompt grounding	Inject description from `VisionDocument.md` into skill context
📄 Doc diff preview	Show changes in architecture across versions
🔁 Test coverage justification	Use `TestSummary.md` to explain what’s covered and what isn’t
✨ API explanation	Reuse `IntegrationGuide.md` to auto-document gateway mappings
📚 Studio search	Find documentation by keyword, tag, or related module/project

🧪 Markdown Embedding Strategy¶

When .md files are ingested:

Headers and sections are parsed (e.g., ## Purpose, ## Usage)
Cleaned, deduplicated, and truncated if too long
Embedded as semantic chunks (e.g., 512-token sliding windows)
Stored with:
- Embedding vector
- Link to blob/Git file path
- Reference to traceId, agentId, projectId

→ Allows partial matching and context injection into other agents.

🧠 Intelligent Reuse in Planning & Generation¶

When planning a new feature, the platform may:

Identify modules with matching VisionDocument.md
Pull README.md and HowItWorks.md to explain internals
Inject these into prompt context for blueprint generation
Reuse structure, naming, and flow diagrams from past memory

✅ Human-like planning and architectural alignment — autonomously.

🧾 Studio Features¶

Feature	Description
🔍 Semantic doc search	Retrieve any doc by meaning, tag, section, agent, or module
🧠 Prompt augmentation	Use relevant documentation in prompt generation context
🕹️ Prompt replay with docs	Inject selected `.md` into semantic kernel system messages
📈 Document evolution view	Track how blueprints, designs, and APIs evolved over sprints
📤 Export doc pack	Download versioned documentation set for a project/module

✅ Summary¶

In ConnectSoft:

Documentation is a core memory component, not an afterthought
Every .md file is indexed, embedded, and traceable
Used by agents for reasoning, planning, testing, and prompting
Versioned and diffable just like code or tests
Exposed in Studio and orchestrator APIs

Documentation is how the factory thinks, communicates, and evolves — in markdown.

Integration with Azure DevOps¶

The ConnectSoft platform treats Azure DevOps not only as a CI/CD tool, but as a core memory gateway. Memory artifacts — such as generated code, blueprints, tests, logs, prompts, and execution traces — are linked to Git repositories, work items, pipelines, PRs, and artifacts managed in Azure DevOps.

This section explains how the knowledge and memory system is deeply integrated with Azure DevOps, enabling traceability, collaboration, governance, and automation across the full software lifecycle.

🔗 Why Azure DevOps Integration Matters¶

Purpose	Benefit
📁 Source of truth	Memory is aligned to Git-tracked codebase
📦 Artifact linking	Generated code and test outputs are tied to builds and deployments
🔄 Traceability & audits	Memory entries are linked to execution traces and change history
🔍 Studio DevOps sync	Users can navigate from memory to Git commits, PRs, pipelines
🧠 Agent memory alignment	Code, docs, and tests are grounded in versioned repositories

📂 Git Repository Memory Anchoring¶

Each generated project or module is committed into a DevOps Git repository:

Repo name: csf/{tenant}/{project}/{module}
Branching strategy:
- main → production-ready
- feature/* → generated in coordination flows
- dev/trace-{traceId} → auto-linked to orchestration flows

✅ Memory entries store Git linkage:

{
  "repoUrl": "https://dev.azure.com/csf/vetclinic/_git/booking-service",
  "commitHash": "a9c1f58",
  "filePath": "src/Application/Handlers/CancelAppointmentHandler.cs",
  "branch": "dev/trace-abc123",
  "pullRequestId": 421,
  "artifactId": "mem-xyz"
}

🧾 Execution Metadata in Git¶

Every orchestration creates:

execution-metadata.json in /metadata/ folder
sprint-trace-matrix.json for milestone linkage
Memory entries pointing to Git commit ID + trace context

This makes memory reproducible and queryable by Studio or API.

🔁 Pull Request Automation¶

Orchestrators and agents open PRs after generation:

Title: feat: scaffold booking service (trace abc123)
Linked memory: handler code, blueprint YAML, README.md
Description includes prompt input + blueprint snapshot
Auto-attaches work items and build pipelines

✅ Memory is enriched during PR creation and completion.

⚙️ Pipeline Memory Integration¶

Memory is linked to Azure Pipelines via:

Build steps:
- dotnet build, dotnet test, publish, bicep build
Test results: pushed to TestRuns + ingested as memory
Logs: execution logs pushed to blob + linked to execution-metadata.json
Artifacts: .nupkg, .zip, .dll with traceable metadata

→ Every pipeline run is also a knowledge update event.

📌 Work Item Linking¶

Memory entries can be associated with:

Features
User Stories
Tasks
Bugs
Epics

Studio and orchestrators emit events like:

{
  "event": "MemoryLinkedToWorkItem",
  "memoryId": "mem-abc123",
  "workItemId": 4512,
  "type": "test",
  "linkType": "Validates"
}

✅ Enables true test-to-feature-to-blueprint traceability.

📦 Artifact Indexing¶

Memory entries are linked to DevOps build artifacts:

Artifact Type	Stored In	Indexed As Memory?
`.nupkg`	Azure Artifacts	✅ Yes (type: library)
`.zip` service app	Azure Pipelines	✅ Yes (type: deployable)
`.dll`, `.json`	Release pipelines	✅ Yes (type: binary/config)
`.md`, `.yaml`	Git repo	✅ Yes (type: doc/blueprint)

📊 Studio: Azure DevOps-Linked Views¶

Studio memory UI includes:

🧾 Git commit trace for each memory entry
🔄 PR + memory side-by-side diff preview
📦 Build + test result link from execution metadata
🧠 “Memory used in this PR” trace graph
🕹️ Replay orchestration with original commit + inputs

🧠 Orchestrator-Level Automation¶

Coordinators (e.g. MicroserviceAssemblyCoordinator) use DevOps integration to:

Clone template repo into feature branch
Track traceId in trace.json
Trigger memory ingestion pipeline
Push changes + open PR
Store all agent decisions + outputs in DevOps-linked memory index

→ Every repo = traceable memory container.

🔐 Security and RBAC Enforcement¶

Memory linked to DevOps repos enforces:

Repository-scoped permissions (read/write)
Role-based memory access via DevOps groups
Token-scoped access via Studio agents or services
Redaction of sensitive content from public repos

✅ Summary¶

Integration with Azure DevOps transforms ConnectSoft’s memory system into a developer-native, traceable, and governable knowledge infrastructure:

Memory lives alongside code, tests, pipelines, and features
PRs and builds are memory-aware
Studio and agents reason over DevOps-linked artifacts
Full traceability from prompt → output → commit → release

DevOps becomes the memory backbone — not just a delivery channel.

Semantic Kernel & Prompt History¶

In ConnectSoft, agents use the Semantic Kernel (SK) as a runtime layer to execute AI-powered skills and orchestrate prompt-based reasoning. Each prompt interaction — whether generated, refined, retried, or reused — becomes part of the agent’s prompt history memory.

This section defines how prompts and responses are treated as memory artifacts, how they are embedded for reuse, and how SK-integrated agents reason from past conversations.

💬 What Is Prompt History?¶

Prompt history is the collection of prompt-response exchanges:

Generated by any agent using SK
Stored with prompt, response, resultMetadata
Linked to traceId, skillId, agentId, and blueprint or module
Embedded semantically and indexed structurally

✅ Prompts are not ephemeral — they are first-class memory units.

🧠 Why Prompt History Matters¶

Use Case	Memory Benefit
🧱 Reuse of effective prompts	Agents reuse high-performing prompt patterns
🔁 Prompt refinement tracking	Trace evolution of instructions after failure
💡 Prompt-to-output traceability	Connect prompt to test, code, or blueprint output
🔍 Prompt similarity queries	Retrieve similar prompts by goal or domain
🧪 Grounded prompt replay	Inject memory into replays or planning context

📦 Prompt Memory Metadata Structure¶

Each prompt exchange is stored as:

{
  "memoryId": "mem-prompt-5121",
  "type": "prompt",
  "agentId": "test-generator",
  "skillId": "ComposeTest",
  "traceId": "trace-abc",
  "prompt": "Write BDD tests for cancelling appointments",
  "response": "Scenario: Cancel appointment...",
  "version": "1.0.0",
  "status": "success",
  "tags": ["test", "appointment", "cancel"],
  "embeddingId": "embed-8132",
  "semanticHash": "b1740c8e",
  "projectId": "proj-123",
  "moduleId": "booking-service"
}

🔗 Prompt + Output Linkage¶

Prompt memory entries are automatically linked to the artifacts they generated:

Link Type	Stored In
`outputFiles`	`.cs`, `.feature`, `.yaml`, `.json`
`linkedMemoryIds`	Points to file-based memory entries
`executionMetadata`	Stores full input-output pair + agent details

✅ Enables reverse lookup: “Which prompt created this handler?”

🧠 Embedding Prompt History¶

All prompts and responses are embedded using vector models (e.g. text-embedding-ada-002) and stored in the vector DB:

Queryable by similarity:
- “Find all prompts that generated a blueprint for appointments”
Clustered to:
- Detect best prompts
- Eliminate redundancy
- Suggest improvements

🔁 Prompt Lifecycle Events¶

Event	Triggered When	Memory Result
`PromptSubmitted`	Agent issues initial prompt	New prompt memory created
`PromptRefined`	Prompt is changed after failure or feedback	New version + diff stored
`PromptUsedAsContext`	Prompt reused in another flow	Linked reference tracked
`PromptFailed`	No usable response	Memory flagged with `status: failed`
`PromptCorrected`	Human/agent overrides failed output	Corrected prompt saved + diff

🧠 Prompt Reuse Example¶

Planner needs to generate tests for appointment cancellation

Flow:

Agent queries vector DB:
- "Compose test for cancelling booking"
Top 3 similar prompts are retrieved:
- mem-prompt-918, mem-prompt-202, mem-prompt-541
Agent uses top prompt’s structure:
- Injects key steps and variations
New .feature test is generated and linked to reused prompt

✍️ Prompt Enrichment with Metadata¶

Prompts are enriched with:

Skill name (e.g., GenerateHandler, ValidateContract)
Blueprint context
History of prior attempts
Linked tags: ["feature", "payments", "retry-safe"]

→ This allows agents to select prompts based on context and compatibility.

🧪 Prompt Diffing¶

When a prompt is refined:

Semantic diff is calculated
Linked to previousPromptId
Studio displays:

- Write a handler for cancelling an appointment.
+ Write a handler for cancelling an appointment within 1 hour notice.

✅ Helps in understanding “what changed” and “why.”

🧱 Prompt Storage in Memory Graph¶

Prompt nodes are stored in the same knowledge graph, linking to:

👤 Agent and skill
📦 Generated modules
📄 Documentation
🧪 Test results
🧠 Feedback ratings

→ Prompts become traceable assets across projects and skills.

🧩 Studio Features¶

Feature	Description
🧠 Prompt search	Query prompt history by keyword, tag, embedding
🔁 Prompt replay	Rerun or refine prompt with new agent context
📊 Prompt performance	Track success rate, failure rate, average output quality
🔎 Prompt output trace	See which files and tests were generated by each prompt
🔧 Prompt diff + compare	View versions and changes over time

✅ Summary¶

In ConnectSoft:

Every prompt issued by an agent becomes a versioned memory object
Prompt history is embedded, indexed, and linked to outputs
Prompts are reused, refined, scored, and replayed
The Semantic Kernel acts as the runtime layer, but memory turns it into a learning agent

Prompt engineering is not trial-and-error — it’s a traceable, evolving, and sharable knowledge discipline.

ConnectSoft is not just an autonomous factory — it’s a learning system. Every agent, orchestrator, and knowledge artifact participates in a feedback-driven refinement loop, ensuring continuous improvement of prompts, outputs, and agent strategies.

This section details how feedback — from users, other agents, or test results — is captured, stored, and applied to evolve memory, regenerate outputs, and guide future decisions.

🔁 What Is the Feedback Loop?¶

The feedback loop is the process by which:

🧪 Agents receive outcomes (pass/fail, success/error)
👤 Users or other agents provide qualitative feedback
🔄 The system stores feedback as a memory object
🤖 Agents modify their next actions based on feedback

✅ The loop is continuous, structured, and observable.

📘 Feedback Memory Types¶

Type	Triggered When	Stored As Memory?
`AgentFailed`	An agent emits a result that fails validation	✅ Yes
`PromptRefined`	Prompt is manually or programmatically adjusted	✅ Yes
`OutputCorrected`	A generated artifact is edited or fixed	✅ Yes
`TestFailed`	Generated test or code fails in CI or validation layer	✅ Yes
`FeedbackSubmitted`	Human or agent explicitly rates, flags, or comments	✅ Yes

🧠 Feedback Memory Schema¶

{
  "memoryId": "mem-fb-901",
  "type": "feedback",
  "linkedMemoryId": "mem-456",
  "agentId": "test-generator",
  "skillId": "ComposeFeatureTest",
  "traceId": "trace-789",
  "status": "failure",
  "rating": 2,
  "comment": "Test uses incorrect Given/When/Then pattern",
  "feedbackSource": "Studio QA Engineer",
  "createdAt": "2025-05-10T12:34:00Z"
}

💡 Agent Behaviors Triggered by Feedback¶

Trigger Condition	Agent Reaction
`AgentFailed` + `status: critical`	Skip current skill, escalate to alternate skill set
`PromptRefined`	Store new version, regenerate with `bumpVersion`
`OutputCorrected`	Archive old file, ingest corrected file, flag cause
`FeedbackSubmitted.rating <= 2`	Mark output as low-quality, deprioritize reuse
`FeedbackSubmitted.rating >= 4`	Mark output as high-quality, prioritize for reuse

🔍 Feedback Target Types¶

Feedback can apply to:

🎯 Prompts
📄 Blueprints
📦 Generated code
📚 Documentation
🧪 Test cases
🧠 Agent skills

All are linked to their originating memoryId, agentId, and skillId.

🧬 Feedback Loops Across Orchestration¶

TestFailed → triggers regeneration from PromptRefined
AgentFailed → MemoryMarkedUnusable + agent replacement
ManualCorrection → new artifact version stored + linked to original
FeedbackSubmitted → studio user flags bug or improvement

Each feedback event is stored and used in future agent prompts.

Agents may emit a PromptRefined event when:

Prompt failed to produce usable output
Output was correct but suboptimal
Feedback indicated style, structure, or clarity issues

The refined prompt becomes a new memory entry:

{
  "previousPromptId": "mem-prompt-789",
  "reason": "Original prompt failed test validation",
  "diff": "Adjusted step definitions in scenario description"
}

→ Supports traceable evolution of prompt strategies.

Failures in .feature tests automatically trigger:

Feedback memory: status: test-failed, target: handler
Marked memory as deprecated = true
Prompt + output regeneration
Diff between versions and reasons embedded in memory

✅ Ensures test results inform next generation.

✍️ Human Feedback Capture¶

Studio users can submit:

⭐ Rating (1–5)
📝 Comments
🧭 Suggest regeneration
📎 Attach screenshots or logs (stored in blob + linked to memory)

All submissions are stored and shown in memory viewer.

📈 Feedback Metrics in Studio¶

Studio and orchestrators expose:

Metric	Description
💬 Feedback volume	How much feedback was submitted per agent/project
📉 Failure rate trends	What skills fail the most across modules
🔄 Regeneration frequency	How often memory is re-generated
✅ Success-after-retry	Rate of successful second attempts after feedback
🧠 Top-rated memory chunks	Highest-rated prompts, handlers, test suites

🧠 Memory Diff Chain View¶

Agents and users can trace memory evolution:

graph TD
  P1["Prompt v1"]
  H1["Handler v1 (Failed)"]
  FB1["Feedback: Invalid code"]
  P2["Prompt v2 (Refined)"]
  H2["Handler v2 (Passed)"]

  P1 --> H1 --> FB1 --> P2 --> H2

Hold "Alt" / "Option" to enable pan & zoom

✅ This chain is stored and queryable for audit and learning.

✅ Summary¶

The feedback and refinement loop ensures ConnectSoft:

Learns from failures and corrections
Evolves its prompts, skills, and outputs
Improves memory reusability over time
Tracks all agent behaviors and user input with traceability

Memory that doesn’t improve is just storage. Memory that learns is intelligence.

Studio Knowledge Base UI¶

The ConnectSoft AI Software Factory includes a powerful Studio interface where humans can interact with the knowledge system. The Studio’s Knowledge Base UI is more than a static viewer — it’s a dynamic, searchable, traceable, and diffable portal into the memory system.

This section explains how the Studio empowers users to navigate, search, audit, compare, replay, and refine memory — including prompts, documents, code, test results, diagrams, and AI agent behavior.

🧠 Key Goals of the Studio Memory Interface¶

Capability	Purpose
🔍 Search and filter memory	Find relevant artifacts across projects, modules, types
🧱 Browse by trace or module	View memory scoped to execution trace or feature module
📄 View documentation and code	Render markdown, code files, and blueprints with metadata
🔁 Prompt replay and refinement	See prompts and re-execute them with new context
🔎 Compare versions and diffs	See memory evolution and changelogs
🧪 Review test status and coverage	Check which artifacts are validated, failing, or untested
🧠 Feedback and rating	Submit comments, scores, corrections to guide future agents

📦 Memory Browser by Project¶

Every project page in Studio includes a Knowledge Browser, displaying:

📁 Memory grouped by:
- Type (code, doc, blueprint, test, etc.)
- Module
- Trace/session
📌 Filter by:
- Agent
- Skill
- Tags
- Version
- Status (failed, success, pending, outdated)
🔍 Search:
- Textual
- Semantic (vector match)
- Metadata-driven

📘 Memory Detail View¶

Clicking on a memory entry shows:

📄 Rendered content (e.g., markdown or code preview)
📎 Metadata (traceId, agentId, skill, type, tags, etc.)
🔁 Linked memory (e.g., prompt → output → feedback)
📉 Execution data (duration, logs, retries)
💬 Comments and feedback (from users or agents)
🔧 Actions:
- Replay
- Refine prompt
- View version diff
- Flag as outdated

🧬 Visual Studio-Like Views¶

For developers, the UI mimics familiar IDEs:

Panel	Description
📂 Solution Explorer	Per-module memory with project-structured navigation
🔍 Search Bar	Fuzzy, tag-based, or full semantic query
🧠 Trace Graph	Orchestration timeline linked to agent-generated memory
🧾 Metadata Panel	All memory metadata in structured and editable form
🧪 Test Result View	Success/failure badges and rerun actions per test

📊 Prompt History Explorer¶

In the "Prompt Memory" tab, users can:

🔁 Replay previous prompts
🔍 Compare prompt versions
📈 See performance analytics (pass/fail rate)
🧠 Filter by prompt type, domain, skill, and quality score
✍️ Inject prompts into new planner flows

📄 Documentation Viewer¶

Markdown-based documents are rendered with:

Header navigation
Expand/collapse sections
Linked diagrams
Related code/tests sidebar
Feedback submission interface (suggest changes, highlight issues)

🔁 Diff Viewer for Versioned Memory¶

When multiple versions of a memory artifact exist, users can:

📊 See visual diffs (code, doc, prompt)
🔁 Switch between versions
🧠 See reason for change (e.g., “Test failed → Prompt refined”)
📌 Pin a version as the “current gold copy”
🧪 Trigger regression test for changed memory

🧪 Test Coverage and Status View¶

For each module or project:

🟢 Passed test memory
🔴 Failed test artifacts
🟡 Missing test detection (based on blueprint feature map)
📎 Link to related handlers and prompts
🔁 Rerun tests or regenerate from feedback

✍️ Feedback and Suggestion Panel¶

For every memory entry:

⭐ Rate from 1 to 5
💬 Add feedback comment
🧠 Suggest regeneration
📎 Upload reference (e.g., screenshot, bug trace)
🔄 View impact of feedback (e.g., prompt refined, memory diffed)

🔐 Role-Based Access¶

Studio memory UI respects permissions:

Role	Memory Access
Architect	Full read/write, regeneration, feedback
QA Engineer	Read-only code/docs, write feedback on tests
Product Manager	Read-only blueprints/prompts, suggest refinements
Tenant Operator	View only project memory, no prompt access
AI Agent	API token-based scoped access via orchestrators

🧠 Integrations with Other Studio Features¶

🔗 Agent Orchestration Graph → trace memory lineage per agent
🔄 SaaS Factory Workflows Launcher → start generation with memory injection
📊 Observability Panel → show memory usage, quality, and access stats
🔁 Knowledge Replay Mode → step through trace and regenerate flow

✅ Summary¶

The Studio Knowledge UI makes memory:

Searchable
Visualized
Replayable
Diffable
Auditable
Feedback-ready

It’s the lens through which humans and agents collaborate, learn, and evolve software together.

Memory-Aware Traceability¶

In ConnectSoft, every memory entry is traceable — to the blueprint that inspired it, the prompt that generated it, the agent that emitted it, and the project or sprint that produced it. This deep linkage enables full traceability across the autonomous software development lifecycle.

This section describes how trace IDs, milestone metadata, and execution chains are used to track, audit, and replay memory evolution from vision to release.

🧠 What Is Memory-Aware Traceability?¶

Traceability means that every artifact in memory can answer:

Who generated it?
Why was it generated?
From which input?
For what purpose?
What execution or sprint was it part of?
What has changed since?

✅ These questions are answered using traceId, agentId, skillId, milestoneId, and versioned metadata.

🧾 Core Traceability Identifiers¶

Field	Description
`traceId`	A unique ID for each agent orchestration or blueprint flow
`projectId`	Project-wide scope anchor
`moduleId`	Microservice/module within the project
`agentId`	Responsible agent that generated memory
`skillId`	Specific skill or plugin used to generate memory
`milestoneId`	Sprint milestone or phase when memory was produced
`executionId`	Optional: detailed runtime execution UID

📘 Example: Memory Entry with Traceability¶

{
  "memoryId": "mem-123456",
  "type": "code",
  "agentId": "backend-developer",
  "skillId": "GenerateHandler",
  "projectId": "proj-booking",
  "moduleId": "booking-service",
  "traceId": "trace-xyz789",
  "milestoneId": "M3-GenerateHandlers",
  "status": "success",
  "createdAt": "2025-05-13T10:15:00Z"
}

🔁 Trace Graph in Execution Metadata¶

Each execution-metadata.json contains:

traceId
agentsInvolved
outputsGenerated
promptsUsed
duration
inputBlueprints
linkedArtifacts

✅ Enables reconstruction of how a single blueprint yielded test cases, handlers, contracts, and feedback.

🧠 Memory Usage Tracing¶

Memory entries are linked across flows:

Origin Memory	Used By Memory
Prompt `mem-p123`	Code file `mem-c456`
Blueprint `mem-b100`	Tests `mem-t300`, `mem-t301`
Failed artifact `mem-f001`	Refined version `mem-f002`
Test result `mem-tx99`	Triggers prompt `mem-pr-refined-33`

→ These links are stored in linkedMemoryIds[].

📊 Sprint and Milestone Trace Matrix¶

Every project maintains:

sprint-trace-matrix.json

{
  "milestoneId": "M3-GenerateHandlers",
  "traceId": "trace-xyz789",
  "start": "2025-05-12",
  "end": "2025-05-13",
  "agents": ["backend-developer", "test-generator"],
  "memoryEmitted": ["mem-c456", "mem-t123"]
}

Studio can render sprint progress from trace-driven data.

🔎 Studio Features Based on Traceability¶

Feature	Description
🧠 Memory by Trace	View all memory created in one execution or trace
📦 Memory by Milestone	Filter outputs by sprint goal or orchestration phase
🔁 Prompt + Output + Feedback	View full chain of input, output, result, and refinement
📊 Timeline Explorer	Show who generated what, when, and why
⏪ Memory Replay Mode	Rebuild a flow using original prompts + skills
📎 Trace Summary Download	Export all trace-linked memory for audit or packaging

🔁 Rebuilding Context from Memory¶

With full traceability:

Select traceId = trace-xyz789
Load:
- Original prompt
- Blueprint YAML
- Handlers generated
- Tests linked
- Execution logs + metrics
Option: replay with updated skills or regenerate using same input

✅ Enables repeatability and confidence in generation pipelines.

🔐 Auditable Change Chains¶

Each memory artifact includes:

Versioning history
linkedMemoryIds to:
- Source prompt
- Feedback or failures
- Regenerated artifacts
Time-stamped status and origin

Auditors and architects can trace:

“Why did this test exist, what created it, and how did it evolve?”

🧠 Integration with Orchestration Layer¶

Each Coordinator uses trace-aware checkpoints:

Coordinator	Trace Behavior
`ProjectBootstrapOrchestrator`	Emits initial `traceId` + project memory scaffold
`SprintExecutionCoordinator`	Tracks memory per `milestoneId`
`MicroserviceAssemblyCoordinator`	Records `trace → artifact` links
`ReleaseCoordinator`	Bundles memory by trace/version for packaging

✅ Summary¶

Memory-aware traceability enables ConnectSoft to:

Reconstruct every generation event
Replay orchestration flows with precision
Audit who did what, when, and why
Correlate prompts, outputs, tests, and failures
Enable Studio users to explore and trust the AI software lifecycle

Traceability transforms memory from data into a provenance graph.

Knowledge Reuse Mechanisms¶

One of the most powerful capabilities of the ConnectSoft platform is automated knowledge reuse. Instead of reinventing the wheel for every project, module, or prompt, agents can identify existing memory artifacts — including blueprints, test cases, adapters, and prompt patterns — and reuse them with or without adaptation.

This section describes the strategies, metadata models, and intelligence patterns that power memory reuse across projects, modules, agents, and tenants.

🧠 Why Reuse Matters¶

Benefit	Description
⏱ Saves Time	Avoids redundant work by agents
✅ Increases Quality	Leverages validated, tested, high-performing memory chunks
📈 Improves Continuity	Aligns new modules with existing design and documentation
🔄 Enables Cross-Project IQ	Reuses knowledge across related verticals (e.g. Booking → Claims)
🧪 Drives AI Learning	Enables agents to prefer better-performing generations

📦 What Can Be Reused?¶

Artifact Type	Reuse Strategy	Use Case
Prompts	Reuse by similarity	Generate new blueprint with same intent
Blueprints	Reuse as partial or base	Use service blueprint from another domain
`.feature` Tests	Reuse by variation	Copy test, adjust for new aggregate
`.cs` Files	Reuse as template/code-similarity	Clone a handler logic across modules
OpenAPI/Event Specs	Reuse from past services	Clone structure for new contract
README/docs	Reuse for template documentation	Use past documentation layout and phrasing

🔍 Reuse Matching: Three Layers¶

Semantic Similarity
- Based on vector embeddings
- Match prompt, blueprint, doc, or test
Structural Compatibility
- Same boundedContext, moduleType, eventShape
- Same interface or API pattern
Metadata Tags
- Filter by agentId, skillId, type, domain, status

📘 Example Reuse Query¶

{
  "projectId": "proj-123",
  "type": "test",
  "tags": ["appointment", "cancel"],
  "similarityThreshold": 0.92,
  "status": "success"
}

→ Returns .feature files for similar flows, which can be adapted and injected into a new module.

🧠 Agent-Driven Reuse Flow¶

Agent (e.g. Blueprint Aggregator) receives a task
Queries memory with:
- Semantic embedding of intent
- Structured filters
Receives top reusable candidates:
- Blueprint A (match: 93.2%)
- Test Suite B (match: 91.0%)
- Prompt C (match: 88.9%)
Agent adapts or clones outputs
Stores as new versioned memory with reuse lineage

🔄 Reuse with Adaptation¶

Reused memory is not blindly copied — it is annotated and transformed:

🧬 Semantic fields changed
🧪 Prompts adjusted for new business rules
📎 Linked to original memory via linkedFrom

{
  "memoryId": "mem-999",
  "type": "test",
  "linkedFrom": "mem-322",
  "reuseType": "clone+adapt",
  "adaptationNotes": "Updated cancel policy to 1-hour threshold",
  "similarityScore": 0.93
}

🧠 Reuse Scoring and Priority¶

The platform scores reusable memory chunks based on:

Similarity (vector + tag match)
Feedback score (human/agent ratings)
Test pass rate (for code/tests)
Generation context match (same domain or aggregate)
Version age (latest preferred)

Only entries above reuseThreshold (default: 0.85) are considered candidates.

🔗 Memory Lineage and Reuse Chain¶

Memory modules record their reuse ancestry:

graph TD
  mem101["Booking Blueprint"]
  mem202["Cancel Test v1"]
  mem303["Cancel Test v2 (adapted)"]
  mem404["Test Documentation"]

  mem101 --> mem202 --> mem303
  mem303 --> mem404

Hold "Alt" / "Option" to enable pan & zoom

→ Enables tracing the knowledge DNA of every output.

🧠 Reuse by Planners and Orchestrators¶

Planners and Coordinators leverage memory reuse to:

Clone templates and scaffold modules
Reuse domain events and handlers
Bootstrap test cases from known-safe flows
Copy deploy pipelines and infra patterns
Apply previously refined prompt strategies

✅ All with full lineage, traceability, and adaptation logs.

📎 Studio Reuse Features¶

Feature	Description
🧠 Reuse Suggestions	Memory browser shows “reuse candidates” by similarity
🔁 Reuse with Adaptation Flow	Select item → edit → store as adapted memory
📄 Compare Before/After	See reused content and what was modified
📦 Reuse Dashboard	View most reused memory across modules, tenants, or agents

🔐 Reuse Access Control¶

Tenants can share reusable modules across projects
Some memory is marked publicReusable: true
Private memory cannot be reused unless:
- Shared by edition
- Whitelisted by governance
Reuse access is enforced at the memory API level

✅ Summary¶

Knowledge reuse in ConnectSoft enables:

Smarter agents with less generation overhead
Safer and faster time-to-delivery
Higher consistency across modules and products
Traceable lineage of all reused content

Reuse turns memory from history into active intelligence.

Security & Access Control¶

In ConnectSoft, the memory system contains valuable business knowledge — prompts, code, blueprints, contracts, tests, documentation, and infrastructure plans. To protect this intellectual property, the platform enforces multi-layered, tenant-aware, role-based access control (RBAC) over all memory.

This section defines how security boundaries are enforced across tenants, projects, agents, and memory types.

🔐 Core Security Goals¶

Goal	Description
🔒 Tenant isolation	Ensure memory is never shared across tenants by default
🔁 Scoped reuse	Allow reuse only within permitted boundaries
👤 Role-based visibility	Limit access to memory based on user/agent roles and permissions
✅ Traceable access	Log all memory reads, writes, and modifications
🔄 Memory mutation audit	Track who changed what, when, and why

🧱 Multi-Layered Security Model¶

ConnectSoft enforces security at 5 layers:

Tenant Boundary
- All memory is tagged with tenantId
- Cannot be queried or reused outside the tenant without explicit permission
Project Boundary
- Scoped access to projectId
- Memory is grouped and versioned per project
Module Boundary
- Memory is filtered by moduleId when required (e.g., when generating within booking-service)
Agent/Role Boundary
- Agents can only access memory related to their role/skill unless granted cross-role access
Memory Type Boundary
- Certain types (e.g., prompt history, documentation) may be flagged as private or redacted

🔐 Memory Metadata Fields for Security¶

Each memory entry includes:

{
  "tenantId": "vetclinic",
  "projectId": "proj-2025-0098",
  "moduleId": "payment-service",
  "accessScope": "private",
  "allowedRoles": ["solution-architect", "documentation-writer"],
  "publicReusable": false,
  "ownerAgentId": "backend-developer",
  "createdBy": "agent",
  "readOnly": false
}

✅ This metadata is enforced at the query and ingestion layers.

👤 Role-Based Access Control (RBAC)¶

RBAC is applied across:

Role	Allowed Memory Actions
`VisionArchitect`	Read/write vision, blueprints, prompt history
`BackendDeveloper`	Read/write code, tests, contracts
`TestGenerator`	Read blueprints, write tests, read test coverage
`DevOpsEngineer`	Read code, write infra memory, read deployment metadata
`StudioUser (Product)`	Read-only docs, feedback, feature prompts
`StudioUser (QA)`	Read tests, submit feedback, view trace
`ProjectOwner`	Full access within their projects

RBAC is implemented via scoped Studio tokens and orchestrator agent configuration.

📦 Access Tokens & Memory APIs¶

Agents and users interact with memory via secure APIs
Tokens include:
- tenantId
- projectId
- agentId or userId
- role
- scope (read, write, reuse)
All API requests validate token scope before allowing memory access

🚫 Redaction & Private Memory¶

Certain memory entries may be flagged:

accessScope: private
redacted: true
sensitive: true

These entries:

Are excluded from global search or reuse
Cannot be embedded in prompts unless permitted
Require explicit RBAC override to view or modify

🧠 Controlled Reuse Across Tenants¶

Cross-tenant reuse is possible but requires governance:

Shared artifacts must be:
- Marked publicReusable: true
- Approved by governance policies
Only agents with allowCrossTenantReuse: true may request such memory
Example: a template library published to multiple customers

🧾 Memory Access Audit Logs¶

Every memory action is logged:

{
  "event": "MemoryAccessed",
  "memoryId": "mem-abc123",
  "accessedBy": "agent:qa-engineer",
  "timestamp": "2025-05-14T09:22:00Z",
  "operation": "read",
  "traceId": "trace-xy999",
  "tenantId": "vetclinic"
}

Studio users and platform auditors can view:

Access logs by memory
Read/write counts
Agent usage trends
Memory modification history

🧠 Example: Agent Access Rules in Practice¶

Scenario: Test Generator Agent attempts to access README.md from another project

Memory metadata:

{
  "type": "doc",
  "accessScope": "private",
  "projectId": "proj-billing"
}

Agent’s token:

{
  "agentId": "test-generator",
  "projectId": "proj-booking",
  "role": "QA",
  "scope": "read"
}

→ Result: Access denied (projectId mismatch + private scope)

🧱 Isolation by Edition and Module¶

For multi-edition SaaS platforms:

Memory can be segmented by:
- editionId
- featureFlag
- environment (e.g., staging, production)

→ Allows different editions to use different memory patterns or regenerate independently.

✅ Summary¶

Security in ConnectSoft’s memory system ensures:

💡 Intelligent agents operate within authorized boundaries
🧱 Tenants and projects remain isolated
👤 Human and agent access is enforced and audited
🔁 Reuse is powerful — but controlled and traceable

Memory is power — and power must be governed, scoped, and secured.

Garbage Collection & Expiry¶

In ConnectSoft, the memory system can grow quickly — storing every prompt, test, code file, blueprint, document, and trace across thousands of projects and orchestrated flows. To maintain performance, cost efficiency, and knowledge relevancy, the system enforces a structured Garbage Collection (GC), Archival, and Expiry Policy.

This section defines the lifecycle of memory objects, when they are expired or archived, and how agents and curators can intervene.

🧠 Why Memory Management Is Critical¶

Problem	Solution Provided by GC & Expiry
🚀 Memory bloat	Automatic cleanup of unused, low-quality, or expired artifacts
🧪 Test result noise	Expire failing test results after retry or correction
🗃 Version sprawl	Archive outdated or superseded versions
🔁 Prompt evolution clutter	Retain only meaningful prompt versions
💰 Storage cost	Keep memory footprint lean for vector DB and blob stores

🧾 Memory Lifecycle Phases¶

Phase	Description
`active`	Most recent version, used by orchestrators and agents
`stale`	Superseded by newer version, not accessed for X days
`archived`	Moved to cold storage (e.g., Azure Blob Archive Tier)
`expired`	Eligible for deletion, marked by policy
`deleted`	Hard deleted (manually or via retention policy)

🔢 Expiry Criteria¶

Memory entries are eligible for expiry if:

Condition	Expiry Action
Not accessed in last 90 days	Mark as `stale`
Superseded by ≥ 2 newer versions	Archive oldest version
Has `status: failed` and `retry: success`	Mark failed version `expired`
`feedback.rating ≤ 2`	Mark for expiration
`promptVersion > 3` with no improvement	Expire unused intermediate
Manually flagged as obsolete	Immediate archive or delete

📁 Storage Tiering: Blob + Vector DB¶

Memory Type	GC Action	Storage Tier
`.cs`, `.feature`, `.yaml`	Archive old versions	Azure Blob Archive or Cold Tier
`.md`, `.json`, `.txt`	Compress and store diff-only	Standard Tier or zipped collection
Vectors (embeddings)	Delete expired entries	Purged from vector DB (e.g., Qdrant)
Metadata DB	Mark with `status = expired`	Soft-deleted for X days

🧠 Example: Prompt Expiry Chain¶

graph TD
  P1["Prompt v1 (Failed)"]
  P2["Prompt v2 (Low score)"]
  P3["Prompt v3 (Success)"]

  P1 --> P2 --> P3

Hold "Alt" / "Option" to enable pan & zoom

GC logic:

Keep v3 as active
Archive v2 (low reuse)
Expire and delete v1 (marked failed + redundant)

📊 GC Process Flow¶

Run GC job (daily or weekly)
Evaluate memory metadata:
- lastAccessed, version, status, feedback, reuseCount
Emit GC events:
- MemoryMarkedStale
- MemoryArchived
- MemoryMarkedForExpiry
Move blob files, delete vector entries, update metadata
Log GC actions in gc-logs/ folder

🔍 Agent & Studio Support¶

Agents are aware of expiration rules:
- Avoid using expired memory
- Can trigger regeneration of stale outputs
Studio allows:
- Manual flagging for archive/delete
- Viewing GC history per project/module
- Restore from archive (if within retention window)

🗃 Archive Retention Policies¶

Memory Type	Retention Period	Archive Action
Test results	60 days	Compress logs and features
Code versions	180 days	Move to cold storage
Prompt history	90 days	Retain top-rated only
Execution metadata	120 days	Archive to audit vault
Feedback entries	365 days	Persist unless redacted

🔐 Governance & Audit Trail¶

All GC actions are logged:

{
  "event": "MemoryMarkedForExpiry",
  "memoryId": "mem-test-1234",
  "reason": "Unaccessed + superseded",
  "timestamp": "2025-05-14T03:00:00Z"
}

Audit logs can be queried by:

memoryId
agentId
actionType
timestampRange

✅ Summary¶

Garbage collection and expiry ensures that ConnectSoft:

Maintains a lean and high-quality memory base
Preserves only useful, traceable, relevant artifacts
Keeps vector databases and blob storage optimized
Allows safe regeneration and replay from active memory
Offers clear audit trail and retention policies for all memory transitions

Memory that lives forever becomes noise. Memory that ages with governance becomes wisdom.

Future Extensions & Ontologies¶

The ConnectSoft memory architecture is designed for scale, modularity, and evolution. As the platform matures, the memory system will incorporate advanced capabilities, including domain-specific ontologies, multi-modal memory, knowledge graph enhancements, and AI-native features like RAG (Retrieval-Augmented Generation) and fine-tuned assistants.

This final section outlines the forward-looking vision for the Knowledge & Memory System and how it will empower a next-generation autonomous SaaS factory.

🚀 Key Expansion Areas¶

Area	Description
🧠 Ontology-Driven Reasoning	Introduce domain ontologies for semantic alignment and cross-agent understanding
🎥 Multi-Modal Memory	Support memory entries with images, diagrams, flows, videos, audio
🔁 Memory-Backed Assistants	Fine-tuned agent workflows grounded in project or module memory
📦 RAG for Prompt Execution	Use memory chunks as dynamic context for semantic kernel executions
📊 GraphQL Memory Query API	Flexible, composable memory query layer for humans and agents
🧩 Knowledge Modules DSL	Define memory blueprints using a high-level DSL for portability and reuse

🧠 Domain Ontologies¶

Introduce standardized domain concepts and relationships:
- Entities, actions, events, contracts, policies
Support across blueprints, prompts, event schemas, and tests
Example: define Appointment, BookingWindow, ReschedulePolicy
Used for:
- Prompt grounding
- Test generation
- Event-based agent reasoning
Ontologies stored in memory and linked to artifacts

Future memory types:

Type	Use Case
`.png`	Diagrams (e.g., architecture, flowcharts, UI maps)
`.svg`	Renderable graphs, event timelines
`.mp4`	UI flows, agent demo recordings
`.wav`	Voice feedback, conversation logs
`.drawio`	Auto-generated diagrams from service blueprints

→ All assets are indexed and stored as retrievable, embedded memory units.

🔁 Retrieval-Augmented Generation (RAG)¶

RAG will become standard for all SK-based agents:

Query memory for relevant context (text, blueprint, code, prompt)
Inject into LLM system prompt
Execute with context-aware precision

✅ Enables more intelligent generations, fewer retries, and richer behavior.

🧠 Memory-Aware Assistants¶

ConnectSoft will offer:

Fine-tuned task-specific agents (e.g., Blueprint Optimizer, Test Explainer)
Assistants trained on ConnectSoft memory patterns
Capable of:
- Explaining blueprint decisions
- Suggesting test improvements
- Answering “why this module looks like that”

→ Deep personalization per project + context.

🧾 GraphQL & Semantic Memory Query Layer¶

Expose a unified, expressive memory query language:

query {
  memory(
    filter: {
      type: "test"
      tags: ["reschedule"]
      projectId: "proj-vetclinic"
    }
  ) {
    memoryId
    filePath
    version
    status
    createdAt
  }
}

→ Enables intelligent apps, low-code memory tools, and third-party platform access.

📜 Knowledge Module DSL (Future)¶

A declarative DSL to define reusable knowledge modules:

module:
  name: AppointmentHandler
  type: handler
  inheritsFrom: booking-handler-base
  inputs:
    - entity: Appointment
    - event: AppointmentRequested
  outputs:
    - command: ScheduleAppointment
  blueprintRef: mem-blueprint-1234

✅ Enables blueprint-driven memory scaffolding and CI/CD-ready validation.

🧪 Intelligent Memory Validation Agents¶

Upcoming agent types will automatically:

Validate memory correctness against blueprint
Detect redundant memory
Detect drift between memory and Git repo
Suggest optimizations and doc corrections

📦 Memory Snapshots & Packaging¶

New formats to export/import memory:

memory.bundle.zip → includes docs, code, tests, metadata
memory.snapshot.json → diffable memory representation
Use cases:
- Release packaging
- Disaster recovery
- Edition migration
- Offline simulation

✅ Summary¶

The future of ConnectSoft’s memory system is:

Semantic (with ontologies)
Multi-modal (code, diagrams, audio, visual)
Interoperable (GraphQL, bundles, DSL)
AI-native (RAG-ready, explainable, proactive)
Continuous (learning through every cycle)

Memory is no longer just support for generation — it is the engine of platform intelligence.

Runtime & State¶

State & Memory — How run state and AI memory are integrated in the Factory runtime
Runtime & Control Plane Overview — Operational view of Factory runtime
Execution Engine — How runs and jobs use state during execution

Architecture & Design¶

Overall Platform Architecture — High-level Factory architecture
Orchestration Layer — How orchestration uses knowledge and memory
Knowledge Indices — Vector search and semantic retrieval
Knowledge Graph — Graph-based knowledge representation