Technology Stack¶

Overview & Stack Philosophy¶

The ConnectSoft AI Software Factory is designed to autonomously generate scalable, secure, cloud-native, and intelligent SaaS platforms across industries. Its technology stack reflects the platform’s core architecture principles, supporting a massive number of composable services, domain models, agent skills, and infrastructure components.

This section outlines the philosophy behind the stack, not just the tools.

🧱 Guiding Technology Principles¶

Principle	Description
🧠 AI-First	All generation flows are AI-agent driven, using prompt engineering and embedded memory
🧱 Modular & Composable	Every unit (template, microservice, orchestrator, test, adapter) is isolated and pluggable
🧭 DDD & Clean Architecture	The domain layer drives all design — with strict separation of concerns
🔁 Event-Driven	Internal communication is asynchronous, reactive, and observable
☁️ Cloud-Native	Designed for horizontal scaling, auto-recovery, and distributed operation
🔐 Security-First	Tenancy, RBAC, prompt access, memory isolation, and secure build pipelines
📊 Observability-First	Tracing, logs, metrics, and behavior graphs are first-class citizens

⚙️ Platform-Scale Alignment¶

The stack is designed to support:

✅ 3,000+ microservices and components
✅ 20+ agent types and hundreds of prompt flows
✅ Multi-edition, multi-tenant SaaS products
✅ Autonomous generation, feedback, and regeneration cycles
✅ Secure publishing pipelines, memory snapshots, and governance

🔗 Stack Layers¶

The system is separated into seven major technical layers:

Layer	Purpose
1. Agent Layer	SK + OpenAI + prompt pipelines and planners
2. Application Layer	.NET Core + DDD services, command/handler pipelines
3. Transport Layer	REST + gRPC + MassTransit + Azure Service Bus
4. Storage Layer	SQL + Cosmos DB + Blob + Vector DBs (semantic memory)
5. Infrastructure Layer	Azure AKS, Functions, App Gateway, Bicep
6. DevOps Layer	Azure DevOps, Git, NuGet, CI/CD pipelines, template registry
7. Observability Layer	Serilog, OTEL, Grafana, Prometheus

→ Each layer is independently swappable and extensible.

graph TB
  subgraph Agentic Layer
    SK(Semantic Kernel)
    LLM(OpenAI / Azure OpenAI)
    VectorDB(Qdrant / Azure AI Search)
    PromptEngine(Prompt Blueprint Engine)
    MemorySDK(Memory SDK)
  end

  subgraph Application Layer
    NET(.NET 8 / ASP.NET Core)
    MassTransit(MassTransit)
    NHibernate(NHibernate)
    API(API Versioning / REST / gRPC)
    Auth(OpenIddict)
  end

  subgraph Infrastructure Layer
    AKS(Azure Kubernetes Service)
    ServiceBus(Azure Service Bus)
    Blob(Azure Blob Storage)
    Gateway(Azure App Gateway)
    KeyVault(Azure Key Vault)
    Cosmos(Azure Cosmos DB)
    Functions(Azure Functions)
  end

  subgraph DevOps and CI/CD
    DevOps(Azure DevOps)
    Git(Git Repos)
    YAML(YAML Pipelines)
    Artifacts(Azure Artifacts)
    Bicep(Bicep / Terraform)
  end

  subgraph Observability and Security
    Serilog(Serilog Logging)
    OTEL(OpenTelemetry)
    AppInsights(Azure Application Insights)
    Prometheus(Prometheus Exporters)
    Grafana(Grafana Dashboards)
    RBAC(Role-Based Access Control)
    Audit(Audit Logging)
  end

  subgraph Frontend Layer
    Angular(Angular)
    Blazor(Blazor)
    Webflow(Webflow / CMS)
    Tailwind(Tailwind CSS)
    i18n(Localization / i18n Agent)
  end

  SK --> LLM
  SK --> VectorDB
  SK --> MemorySDK
  PromptEngine --> SK

  NET --> MassTransit
  NET --> NHibernate
  NET --> API
  NET --> Auth

  AKS --> ServiceBus
  AKS --> Blob
  AKS --> Gateway
  AKS --> Cosmos
  AKS --> KeyVault

  DevOps --> Git
  DevOps --> YAML
  DevOps --> Bicep
  DevOps --> Artifacts

  Serilog --> AppInsights
  OTEL --> AppInsights
  Prometheus --> Grafana
  RBAC --> Audit

  Angular --> Tailwind
  Blazor --> Tailwind
  Webflow --> i18n

  style Agentic Layer fill:#dbeafe,stroke:#60a5fa,stroke-width:1.5px
  style Application Layer fill:#e0f2fe,stroke:#38bdf8,stroke-width:1.5px
  style Infrastructure Layer fill:#ede9fe,stroke:#8b5cf6,stroke-width:1.5px
  style DevOps and CI/CD fill:#ecfccb,stroke:#65a30d,stroke-width:1.5px
  style Observability and Security fill:#fef3c7,stroke:#f59e0b,stroke-width:1.5px
  style Frontend Layer fill:#f0fdf4,stroke:#4ade80,stroke-width:1.5px

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

🧠 AI-First Enablement¶

The stack is natively integrated with:

Semantic Kernel (agent runtime)
OpenAI GPT models via Azure or OpenAI endpoints
Qdrant / Azure AI Search for memory retrieval
Memory metadata schema + prompt tracing
Skill-based prompt orchestration with planner-driven agents

✅ This enables ConnectSoft to behave not just as a platform, but as an autonomous development system.

🔁 Extensibility and Template-Driven Architecture¶

ConnectSoft provides:

.NET-based service and library templates
Scaffolding CLI for new modules
DSLs for defining adapters, handlers, events
Auto-wiring for Serilog, tracing, health checks, and DI
Generator agents to produce new templates or specialize them per use case

✅ Templates, SDKs, and memory records are deeply linked to the tech stack, not separate.

✅ Summary¶

The ConnectSoft technology stack is:

Opinionated, but extensible
Secure and observable by default
Built to support AI-native, memory-driven software generation
Deeply aligned with modern .NET, Azure, SK, and DDD patterns

It’s not just what’s in the stack — it’s how it aligns with intelligent autonomy at scale.

Core Backend Technologies¶

The ConnectSoft AI Software Factory is powered by a robust, battle-tested, and modular .NET 8-based backend stack. It embraces clean architecture, domain-driven design (DDD), messaging, and resilience-first practices — allowing agents to generate production-grade SaaS modules with predictable structure, maintainability, and testability.

This section outlines the foundational backend technologies used across microservices, templates, orchestrators, and libraries.

🔧 Core Technologies¶

Technology	Purpose
.NET 8 (C#)	High-performance, cross-platform runtime for services and libraries
ASP.NET Core	Web API layer (REST & gRPC endpoints, minimal APIs, filters)
MassTransit	Distributed messaging and orchestrated workflows via Azure Service Bus/RabbitMQ
NHibernate	Domain-focused ORM for persistence with advanced mapping capabilities
FluentValidation	Validation layer for commands, DTOs, and request models
OpenIddict	Token issuance, authentication flows, integration with IdentityServer/OIDC
Serilog	Structured logging with sinks to Console, Application Insights, Seq
gRPC	Fast binary RPC layer for internal service communication
REST (OpenAPI)	External/public API exposure with Swagger documentation

🧱 Domain-Driven Design + Clean Architecture¶

All backend modules adhere to strict layering:

Layer	Responsibility
`Domain`	Entities, aggregates, value objects
`Application`	Commands, handlers, orchestrators, services
`Infrastructure`	Repositories, clients, database adapters
`API`	REST/gRPC controllers, DTOs, versioning

✅ Ensures every agent-generated service is testable, extensible, and cleanly separated.

🧪 Testing Stack¶

Tool	Use Case
MSTest	Unit and integration testing
SpecFlow	BDD-style `.feature` tests, step definitions
FluentAssertions	Expressive test assertions
Mocking libraries	Dependency mocking (e.g., Moq, NSubstitute)

✅ Agent-generated services come with tests, specs, and runners by default.

🔌 API Design Stack¶

✅ OpenAPI (Swagger) with versioning
✅ Auto-generated API docs from source
✅ API-first design via blueprint + SK-prompt + ApiDesignerAgent
✅ gRPC service interfaces auto-scaffolded with .proto generation

🧩 Integration Templates & Contracts¶

Every backend service comes with:

Event contract schemas (*.event.json)
Command and query DTOs
API request/response models (*.dto.cs)
Health check endpoints (/healthz, /readyz)
Auto-wired MassTransit consumers and publishers

✅ These are all stored and versioned in memory for reuse and agent understanding.

🛠 Optional Tooling Support¶

Depending on configuration flags and templates, the stack may include:

Rebus or Raw Azure Service Bus SDK for low-level messaging
EF Core (optionally, for basic persistence needs)
FastEndpoints or Carter for minimal API patterns
MediatR for CQRS-style communication between app layers
AutoMapper or map extensions (limited use)

📦 Library Reuse¶

Services rely on reusable ConnectSoft libraries:

ConnectSoft.Extensions.* (DI, telemetry, tracing, validation, auth, etc.)
ConnectSoft.Abstractions.* for shared contracts and policies
ConnectSoft.Integration.* for third-party adapter shells

→ These libraries are injected into services via templates and can be memory-discovered by agents.

✅ Summary¶

ConnectSoft’s backend stack is:

Clean, composable, and scalable
Template-friendly for code generation
Highly testable and observable
Built on enterprise-grade .NET 8 foundations
Aligned with memory-aware, skill-driven agent behaviors

The stack is not just a framework — it's a generation blueprint for autonomous backend production.

Frontend & UX Stack¶

While the ConnectSoft platform is backend-first and AI-driven, it also supports the autonomous generation and evolution of frontend modules — from admin dashboards to public SaaS portals — using a multi-framework frontend stack.

This section covers the technologies used for UI development, CMS integration, mobile/web support, localization, and agent-driven UI documentation and testing.

🎨 Supported UI Frameworks¶

Framework	Use Case
Angular	Complex, modular frontend apps (admin portals, dashboards)
Blazor (WASM/Server)	.NET-native frontend apps for internal tools, dynamic forms
JavaScript/TS	Lightweight widgets, SDKs, dynamic UX behavior
Tailwind CSS	Design utility classes used across all frameworks
Webflow	Landing pages, marketing flows, no-code UI customization

✅ Agents can generate UIs using any of these frameworks depending on the blueprint and module type.

🧱 UI Architecture Principles¶

Modular: Components, forms, flows are versioned per feature/module
Agent-Writable: UI templates and specs are auto-generated
CMS-Integrated: Webflow + markdown content rendered dynamically
Internationalized: Supports i18n/l10n via config + translation agents
Responsive: UI blueprints define layout behavior for mobile/tablet

🖼️ UI Artifact Types in Memory¶

Type	Stored As Memory?	Use Case
`.component.ts/.cs`	✅ Yes	Angular/Blazor components
`.html` / `.razor`	✅ Yes	Template logic (views, bindings)
`.scss` / `.css`	✅ Yes	Styling with Tailwind or scoped CSS
`form.blueprint.yaml`	✅ Yes	Form field specs used by the UI Generator Agent
`ui-test.feature`	✅ Yes	BDD scenarios for user interactions

🌐 Localization and Internationalization¶

Memory-aware localization engine:
- Extracts UI strings from markup/components
- Stores per-language versions
- Injected by LocalizationAgent at build time
Supported file formats:
- .resx, .json, .po, .xliff
UI memory is tagged by locale, language, region
Studio enables:
- Translation feedback loop
- Preview in selected locale

✍️ UI Documentation & Demo Memory¶

UI Storybook-style docs (auto-generated Markdown)
README.md per component/module
Visual regression snapshots (future extension)
Testable demo apps generated via UI Showcase Agent

✅ These artifacts are stored in memory and linked to component metadata.

🧪 UI Testing Stack¶

Tool	Purpose
SpecFlow	BDD test specs for frontend flows
Playwright (planned)	End-to-end browser automation
Jest/Angular TestBed	Angular unit/component testing
bUnit (Blazor)	Unit testing for .razor components

Tests are stored alongside the generated UI and linked in memory.

💡 Studio UI Viewer¶

Preview UI memory entries (HTML, Razor, TS)
Visualize forms defined via YAML
Compare UI changes across memory versions
Explore UI modules by tag, moduleId, projectId

🔧 Agent Use Cases for UI Generation¶

Agent	Behavior
`UI Designer Agent`	Generates layout and component blueprints from UX intent
`Frontend Developer Agent`	Implements UI from blueprint in Angular/Blazor/JS
`Localization Agent`	Injects translations and i18n bundles
`Test Generator Agent`	Builds UI `.feature` tests for flows and edge cases
`Documentation Agent`	Writes UI usage and integration docs (`README.md`)

All UI outputs are memory-versioned and replayable.

✅ Summary¶

ConnectSoft’s UI stack is:

Framework-agnostic but strongly supported with Angular, Blazor, and Tailwind
Agent-ready with full support for UI generation, testing, and documentation
Memory-captured to allow reusability, localization, and regeneration
Cross-platform — supporting internal tools, public portals, and mobile-ready flows

The frontend is not handcrafted — it’s planned, generated, localized, and testable by design.

Agentic System Stack¶

The core innovation of the ConnectSoft platform is its Agentic Architecture — where intelligent agents use memory, prompt engineering, and domain-specific planning to generate, test, deploy, and document SaaS modules. This is made possible by a powerful stack centered around Semantic Kernel (SK), large language models (LLMs), vector databases, and a memory orchestration layer.

This section details the technologies powering AI agents, prompt execution, memory retrieval, and orchestration.

🤖 Core Agent Technologies¶

Component	Description
Semantic Kernel	Microsoft’s SDK for orchestrating AI skills, prompts, planners, and memory
OpenAI / Azure OpenAI	Primary LLM provider (GPT-4, GPT-3.5-turbo, `text-embedding-ada-002`)
Qdrant / Azure AI Search	Vector DB for storing prompt/code/doc/test embeddings
Memory SDK	Unified internal API for accessing all memory layers (vector, metadata, blob)
Prompt Blueprint Engine	Template + parameter + instruction pipeline for structured prompt creation

🧠 Semantic Kernel Stack¶

SK Concept	Used For
`KernelFunction`	Each agent skill (e.g. GenerateTest, CreateHandler)
`KernelPlugin`	Logical grouping of agent responsibilities
`Planner`	Task breakdown (Sequential, Stepwise, Semantic)
`Semantic Memory`	Embedded context from prior executions
`ExecutionContext`	Agent inputs, goals, skills, and memory bindings

✅ All agents are built as modular SK plugins, reusable across orchestration flows.

🔄 Memory Retrieval Flow¶

Agent receives goal from Planner
Memory layer queried:
- Vector DB for similarity
- Structured DB for metadata
Relevant artifacts injected into SK context
Prompt assembled from:
- Blueprint + memory + tags + instructions
Prompt executed via LLM
Output stored as memory with traceId, agentId, skillId

🧠 Prompt Execution Infrastructure¶

Multi-step prompt builders:
- Blueprint prompts
- Skill prompts
- Refinement prompts
Prompt history stored in memory (embedded + structured)
Semantic diffing and memory version control
Skill libraries for:
- Handler generation
- API design
- Test generation
- Adapter synthesis
- Markdown documentation

📦 Vector DB Support¶

Technology	Purpose
Qdrant	Fast, high-volume vector search
Azure AI Search	Built-in enterprise vector support
FAISS (Dev)	Local vector dev/test indexing

Memory entries (prompts, blueprints, tests, docs) are embedded and indexed with OpenAI embeddings or open models (bge-large, e5-mistral).

🔁 Agents & Planner Coordination¶

Planners use:

Skill discovery (/skills registry)
Memory inspection via trace/milestone
Context window optimizers (chunking, summarization)
Feedback loops from:
- Failed tests
- Low-quality outputs
- Human feedback

Planners coordinate 3–10 agents per module or milestone.

🧩 Sample Agent Configuration¶

{
  "agentId": "test-generator",
  "skills": ["ComposeFeatureTest", "GenerateEdgeCases"],
  "memoryScope": "module",
  "planner": "stepwise",
  "retryOnFail": true,
  "embeddingThreshold": 0.91
}
````

---

### 📎 Prompt Blueprint Engine

* `PromptTemplate.yaml` per agent skill
* Dynamic parameter binding
* Memory chunk injection
* Versioned and diffable prompt strategy
* Prompt examples stored as memory for reinforcement

→ Ensures **repeatable and traceable prompt generation** across agents and editions.

---

### 📊 Observability of Agent Execution

* `execution-metadata.json` includes:
    * Prompt
    * Output
    * Agent + Skill
    * Trace + Project
    * Duration
    * Embedding similarity scores
* Studio shows:
    * Input-output chain
    * Semantic diffs
    * Memory scorecards
    * RAG usage context (future extension)

---

### ✅ Summary

The Agentic System Stack combines:

* ✅ Semantic Kernel and structured AI skills
* ✅ Embedded memory retrieval and RAG flows
* ✅ Vector DBs + metadata for deep context
* ✅ Prompt engineering templates with traceability
* ✅ Planner-based skill orchestration and recovery

> This stack allows agents to generate, reason, adapt, and collaborate autonomously — with **memory as their operating system**.

---

## Cloud-Native & Infrastructure Stack

ConnectSoft is a **cloud-native, multi-tenant, infrastructure-as-code** platform — designed to scale horizontally, self-heal, integrate deeply with Azure, and enable per-edition customization, secure deployments, and autonomous provisioning.

This section outlines the infrastructure technologies, Azure services, and patterns used to support ConnectSoft’s AI-powered software factory.

---

### ☁️ Cloud Provider Strategy

- **Azure-First Deployment**
- Compatible with other providers via abstraction layers
- All agents and orchestrators treat infrastructure as modular memory

---

### 🧱 Infrastructure Foundations

| Technology             | Purpose                                                    |
|------------------------|------------------------------------------------------------|
| **Azure Kubernetes Service (AKS)** | Container orchestration for backend services and agents      |
| **Azure Service Bus**  | Message-driven coordination across services and agents     |
| **Azure Blob Storage** | Store memory artifacts, binaries, diagrams, docs           |
| **Azure App Gateway**  | Routing, SSL, API Gateway support                          |
| **Azure Key Vault**    | Secrets, credentials, and sensitive configuration          |
| **Azure Cosmos DB**    | Metadata, traceability, multi-tenant indexing              |
| **Azure Application Insights** | Observability: metrics, tracing, performance              |
| **Azure Functions**    | Lightweight, serverless agents and event handlers          |
| **Azure Container Registry (ACR)** | Host Docker images for services and build agents       |

---

### 📦 Infrastructure as Code (IaC)

| Tool            | Purpose                                       |
|------------------|-----------------------------------------------|
| **Bicep**         | Primary declarative IaC language for Azure   |
| **Terraform (optional)** | Cross-cloud, policy-aware provisioning         |
| **YAML**          | Used in pipelines for deploy/test orchestration |

Each module includes a generated `.bicep` file linked to:

- Storage
- Queue/topic setup
- Container app or AKS deployment
- Secrets injection from Key Vault
- Health probes

✅ IaC files are stored in memory and versioned per trace.

---

### 🔐 DevSecOps-Ready Foundation

- Security scanning on pipeline stage
- RBAC for services via Azure AD + token scopes
- Secrets and certs never stored in repos
- Memory agents use scoped identity to access storage, vectors, metadata
- Logs and metrics feed security dashboards in Grafana/App Insights

---

### 🧪 Infrastructure Observability

| Tool                | Purpose                                           |
|---------------------|---------------------------------------------------|
| **App Insights**     | Logs, traces, custom telemetry (OpenTelemetry-compatible) |
| **Grafana Dashboards** | Platform metrics, agent throughput, test pass rates |
| **Prometheus Exporters** | For .NET services and infrastructure components   |

✅ Dashboards are stored and versioned in memory per module or edition.

---

### 📤 Deployment Architecture

| Layer                     | Description                                              |
|---------------------------|----------------------------------------------------------|
| `Ingress (App Gateway)`   | TLS termination, routing, rate limiting                  |
| `Gateway Services`        | REST/gRPC aggregation per tenant/module edition          |
| `Microservices (AKS)`     | Isolated containers with health probes and scaling rules |
| `Event/Infra Layer`       | Azure Service Bus + Bicep-defined queues, storage, etc.  |
| `Memory + Observability`  | Cosmos DB + Blob + Vector + Logs                         |

Each deployment is traceable by `traceId` + `moduleId` and can be replayed or upgraded via agent-triggered workflows.

---

### 🧠 Infrastructure + Agents

Agents like `CloudProvisionerAgent`, `DeploymentOrchestratorAgent`, and `ReleaseCoordinator`:

- Use generated `.bicep` or `.yaml` files
- Trigger deployments via Azure DevOps pipeline APIs
- Read deployment memory from trace context
- Report success/failure in `execution-metadata.json`
- Support rollback and delta-based updates (planned)

---

### 🧩 Multi-Tenant & Edition-Aware Deployment

- Tenants mapped to isolated namespaces / resource groups
- Editions apply different feature toggles + infra memory overlays
- Deployment flows are edition-aware:
    - `Edition: Premium` → extra microservices, logging, autoscaling
- Memory modules tagged with:
    - `editionId`
    - `environment` (dev/staging/prod)

---

### ✅ Summary

The ConnectSoft infrastructure stack is:

- **Cloud-native**, secure, and modular
- **Azure-first**, but abstracted where needed
- **Declarative and versioned**, aligned with memory architecture
- **Governed and observable**, ready for scale and audit
- Fully **agent-operable and traceable**

> Infrastructure is not an afterthought — it's a **core memory domain** in the AI software factory.

---

## Observability, Security, and Compliance Stack

ConnectSoft was designed to generate and operate thousands of modular SaaS services, agents, and pipelines — which demands an architecture that is **observable by design**, **secure by default**, and **compliant by architecture**.

This section outlines the technologies and practices used to ensure **deep observability**, **fine-grained access control**, and **regulatory alignment** across the platform’s memory, services, agents, and orchestration.

---

### 📊 Observability: Logs, Traces, and Metrics

| Component                  | Purpose                                               |
|----------------------------|--------------------------------------------------------|
| **Serilog**                 | Structured logging with sinks (Console, Seq, App Insights) |
| **OpenTelemetry (OTEL)**   | Unified tracing and metrics collection across agents, services, SK |
| **Azure Application Insights** | Distributed tracing, telemetry aggregation, live metrics |
| **Prometheus Exporters**   | Metrics collection for .NET, AKS, and infrastructure components |
| **Grafana Dashboards**     | Visualizations for memory, agents, CI/CD, test runs, usage |

✅ Every agent execution, prompt response, memory mutation, and orchestrator state is observable and recorded.

---

### 🔁 Traceability Across Systems

Every generated artifact includes:

- `traceId`, `agentId`, `skillId`, `projectId`, `moduleId`
- Logged execution metadata
- Optional correlationId for distributed flows
- Links to:
    - Prompt that generated it
    - Memory output and version
    - Agent retry and feedback loop (if any)

✅ Enables Studio users and auditors to **reconstruct any generation, failure, or retry event**.

---

### 🔐 Security Foundation

| Mechanism                | Implementation                                          |
|--------------------------|----------------------------------------------------------|
| **RBAC**                  | Memory access is governed by `role`, `tenant`, `project`, `module`, and `type` |
| **Azure AD Token Scope** | User/agent tokens for Studio/API access                 |
| **Key Vault**            | All secrets, tokens, and connection strings secured     |
| **Per-agent identity**   | Execution context includes scoped identity/token        |
| **Audit Logging**        | All memory and pipeline operations are logged and queryable |

---

### 📦 Memory Security & Privacy

- Per-memory access metadata:
    - `accessScope`, `allowedRoles`, `tenantId`, `sensitive: true`
- Optional **redaction** or **token masking** for private prompts or feedback
- Studio enforces:
    - Read-only views
    - Role-aware masking of prompt input/output
- Memory encryption at rest (Blob + Cosmos DB)

---

### 🧪 Compliance and Governance

| Standard                  | Coverage Area                                         |
|---------------------------|--------------------------------------------------------|
| **GDPR/PII Readiness**     | Memory tagging, redaction, retention policies         |
| **Auditability**          | Execution logs + memory lineage trace                 |
| **CI/CD Policy Enforcement** | Branch protection + template checks + artifact signing |
| **SOC 2 / ISO 27001 alignment** | RBAC, secrets handling, trace access controls       |

✅ All compliance concerns are handled as part of the **memory and orchestration system**, not retrofitted post-generation.

---

### 🧠 Security Agents and Features

| Agent/Feature                   | Responsibility                                     |
|----------------------------------|----------------------------------------------------|
| `SecurityEngineerAgent`          | Validates blueprint and service architecture for common risks |
| `PenetrationTestAgent`          | Generates OWASP-style test cases and security assertions |
| `RBACPlannerAgent`               | Suggests secure access boundaries in memory and templates |
| `ComplianceOrchestrator` (planned) | Validates edition-specific compliance rules (e.g., HIPAA, GDPR) |

---

### 📊 Studio Observability Features

- 📈 Dashboards per project, agent, sprint
- 🔁 View full trace replay
- 🧠 View memory health (redundant, outdated, failed, reused)
- 🧪 Test coverage + outcome history
- 🔐 Access logs for memory entries
- 🧾 Security profile per edition or tenant

---

### 🧾 Alerts, Monitors, and Logging Patterns

- Serilog enrichers add:
    - `agentId`, `traceId`, `promptId`, `retryAttempt`
- Alerts on:
    - Agent failure rate
    - Memory ingestion issues
    - Test flakiness
    - Prompt execution lag
- Logging sinks:
    - App Insights (prod)
    - Seq (local/dev)
    - JSON log files (per agent, per trace)

---

### ✅ Summary

The Observability + Security + Compliance Stack ensures:

- Full visibility into every agent and module
- Traceable, auditable generations with memory lineage
- Role-governed access to every knowledge asset
- Enterprise-grade security and regulatory readiness
- Studio tools to **see, debug, and govern** the AI-powered factory

> Autonomy is only possible when you can **observe, secure, and trust every action.**

---

## DevOps & CI/CD Tooling

ConnectSoft implements a fully automated, **memory-aware DevOps pipeline** for every service, agent, library, and orchestration. Its DevOps layer is designed to support both human-in-the-loop and autonomous workflows, driven by agent coordination, memory versioning, and infrastructure as code.

This section documents the technologies and practices used for **CI/CD, GitOps, testing, publishing, artifact management, and pipeline generation**.

---

### 🔧 Core DevOps Stack

| Tool / Service           | Purpose                                                        |
|---------------------------|----------------------------------------------------------------|
| **Azure DevOps**           | Source control, pipelines, work items, release orchestration   |
| **Git Repositories**       | One repo per service/module; trace-annotated branches          |
| **YAML Pipelines**         | Build, test, publish, deploy — templated and reusable          |
| **Azure Artifacts**        | Internal NuGet registry for ConnectSoft libraries              |
| **Bicep / IaC validation** | Static validation and linting for deployment readiness         |
| **Dotnet CLI**             | Template packaging, unit testing, NuGet push, linting          |

---

### 🧱 Repository Structure

| Component           | Description                                            |
|---------------------|--------------------------------------------------------|
| `src/`              | Generated code: domain, application, API, tests        |
| `.azure-pipelines/` | YAML pipeline definitions (build/test/deploy)          |
| `templates/`        | Shared steps and pipeline fragments                    |
| `metadata/`         | `execution-metadata.json`, `trace.json`, logs          |
| `.nuspec`, `.csproj`| NuGet configuration for library modules                |

✅ Every module, microservice, or template has its own **dedicated repo** with agent-linked memory and trace history.

---

### 🔄 CI/CD Pipeline Phases

1. **Trigger**
     - Commit or PR created (by agent or human)
1. **Build**
     - Restore, compile, run analyzers, generate artifacts
1. **Test**
     - Run unit and `.feature` tests, collect results
1. **Package**
     - NuGet, zip, or docker image created
1. **Publish**
     - Push to Azure Artifacts or ACR
1. **Deploy**
     - Via Bicep + release pipeline (per edition/env)

✅ Each pipeline run links to memory: traceId, agentId, projectId, etc.

---

### 📦 Agent-Aware Pipelines

Agents emit YAML pipeline fragments like:

```yaml
jobs:
- job: Build
  steps:
    - script: dotnet build
    - script: dotnet test
    - task: PublishTestResults@2
````

And add memory annotations:

```json
{
  "generatedBy": "ci-generator-agent",
  "projectId": "proj-102",
  "traceId": "trace-ab99",
  "pipelineFile": ".azure-pipelines/build-and-test.yml"
}

✅ All pipeline artifacts are linked back to the generation trace and version.

🔁 Template Versioning & Validation¶

.NET templates (e.g., ConnectSoft.MicroserviceTemplate) are versioned and published
Pipeline includes:
- Template linting
- dotnet new --install test
- CI test of generated output
Templates published to:
- Azure Artifacts (internal)
- CLI-consumable via dotnet new

🔍 Traceability in DevOps¶

Each generated output includes:

traceId in commit footer or branch name
execution-metadata.json checked into repo
Agent name + skill in pipeline logs
Memory entries for:
- PRs
- Builds
- Unit test results
- Release summary

→ Enables memory-to-pipeline and pipeline-to-memory navigation.

📊 Studio DevOps Panels¶

In Studio:

Panel	Purpose
💡 CI/CD Status View	See pipeline runs per trace/module/project
🧪 Test Summary	Show test pass/fail per module and per agent
🔁 Memory → PR Mapping	Which prompt/code/test generated which PR
📦 Artifact Browser	View NuGet packages, zipped builds, images, linked memory

🔐 DevOps + Security Checks¶

YAML linting before commit
NuGet publishing includes:
- License metadata
- Semantic version enforcement
Security scan step for code and binaries
Memory logs updated with:
- Pipeline outcome
- Artifact versions
- Feedback from failed jobs

✅ Summary¶

The DevOps and CI/CD stack is:

🧠 Agent-generated, trace-aware, and memory-linked
🔁 Fully automated — from PR to package to deploy
🧪 Enforces test-first, versioned, and observable software
🔐 Secured, policy-aligned, and extensible

CI/CD is not just automation — it’s the execution backbone of the autonomous factory.

Extensibility & Internal Tooling¶

ConnectSoft is not a closed monolith — it's a modular, memory-driven software factory with a deeply extensible architecture. Every template, skill, orchestrator, or agent can be extended, replaced, or reused through a rich set of internal tools, SDKs, and modular libraries.

This section documents the core extensibility mechanisms and tooling infrastructure that power autonomous generation and developer integration.

🔧 Key Extensibility Concepts¶

Concept	Description
Scaffoldable Templates	`.NET`-based templates for microservices, libraries, gateways
Memory-Aware Libraries	Shared packages that agents can discover, reuse, or version-check
DSLs & YAML Blueprints	Define services, forms, APIs, events, or prompts declaratively
Internal CLI & SDKs	Tools for scaffolding, validating, packaging, and replaying memory
Plugin-Ready Skills	SK skills pluggable into agents via skill registry and prompt logic

📦 ConnectSoft Library Ecosystem¶

Library	Purpose
`ConnectSoft.Extensions.*`	Logging, tracing, DI, options, memory access
`ConnectSoft.Abstractions.*`	DTOs, shared contracts, DDD interfaces
`ConnectSoft.Integration.*`	Twilio, Stripe, S3, Braze, Auth0 adapters
`ConnectSoft.Sdk.*`	Studio + memory SDKs (planned)

✅ All are NuGet-packaged, versioned, and linked to memory entries.

🧱 Template System¶

Microservice, Library, Gateway, Test Suite templates
Driven by:
- dotnet new
- Project scaffolder agents
- Memory metadata (templateId, templateVersion)
Validate via:
- Build/test pipeline
- NuGet package linter
- dotnet new --install smoke tests

✅ Used by all agents to scaffold infrastructure-compatible outputs.

🧪 Prompt + Blueprint DSLs¶

Planned extensions include:

```yaml service: name: AppointmentHandler inputs: - command: CancelAppointment outputs: - event: AppointmentCancelled prompts: - GenerateHandler - GenerateTest - WriteDocs ````

→ Will allow blueprint-to-service conversion without full prompt authoring.

🧠 SK Skill Plugins¶

SK agents can register new skills via:
- .plugin.json or .skill.cs
- Config-driven plugin loader
Skills are hot-reloadable and memory-traceable
Skill metadata stored:
- skillId, agentId, sourceRepo, version, description

✅ Enables plug-and-play agent upgrades or domain-specific skills.

🛠️ Internal Tooling¶

Tool	Function
`ConnectSoft.AgentScaffolder`	CLI for generating new agent blueprints and memory files
`memory-export`	Export memory by `traceId`, `projectId`, or `type`
`blueprint-validate`	Validate blueprint YAMLs for structure and consistency
`csf-dev` (planned)	Dev helper for local testing, linting, and scaffolding

🔄 Studio & Developer UX¶

Developers can:
- View library usage per service
- Inject new templates into the CLI/SDK catalog
- Extend skill sets from Studio plugin registry
Studio integration:
- Create project from template
- Fork and modify blueprint YAMLs
- Publish new skill/plugin for reuse

🔁 Extending Memory Behavior¶

Memory storage, filtering, and reasoning are also extensible:

Define new MemoryTypes (e.g., uml, infra-plan, usage-guide)
Attach custom ingestion strategies (e.g., markdown chunkers)
Link memory to custom plugin behavior
Override retrieval strategies per agent or project

✅ Summary¶

The ConnectSoft extensibility layer includes:

🧱 Templates for every kind of service or asset
🧠 Skills and plugins for prompt execution and reasoning
🧰 CLI and SDK tools for development, debugging, packaging
📦 Libraries for consistent and composable services
🔁 DSLs and YAML blueprints for high-level code definition

Extensibility is not a side channel — it's the fabric of modular automation in ConnectSoft.