First-5-Agents MVP¶

🧭 Overview¶

The First-5-Agents MVP defines the minimal functional loop of the ConnectSoft AI Software Factory, where intelligent agents collaboratively turn a software idea into a production-ready, observable microservice — autonomously.

This MVP proves the factory’s core hypothesis:

That autonomous agents, when coordinated through well-defined prompts, events, and templates, can generate production-grade software services with traceability, resilience, and consistency.

🎯 Objective¶

Enable a full agentic software generation pipeline starting from a user prompt:

“Build a BookingService using REST API, NHibernate, and MassTransit.”

...and autonomously perform:

Idea formalization (Vision Architect Agent)
Architecture modeling (Enterprise Architect Agent)
Codebase scaffolding (Microservice Generator Agent)
Git traceable commit (Code Committer Agent)
Pull request orchestration (Pull Request Creator Agent)

🔁 Core Agentic Loop¶

flowchart TD
    UserInput["🧠 Feature Idea"] --> VisionAgent["🎯 Vision Architect Agent"]
    VisionAgent --> ArchAgent["🏗 Enterprise Architect Agent"]
    ArchAgent --> GeneratorAgent["🧱 Microservice Generator Agent"]
    GeneratorAgent --> CommitterAgent["🧾 Code Committer Agent"]
    CommitterAgent --> PRAgent["📦 Pull Request Creator Agent"]
    PRAgent --> AzureDevOps["🚀 Azure DevOps CI/CD"]

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📦 Scope of the MVP¶

This MVP includes:

5 foundational agents with modular roles
1 generated microservice (e.g., BookingService)
CI/CD build and deployment
Traceability via trace_id, agent_id, blueprint_id
Manual or scripted activation of each agent (for now)

🔍 Not in Scope (Yet)¶

Project management UI or dashboards
Full telemetry/insights platform
Agent coordination engine (MCP server)
Agent retry or human escalation flows
Component registry or edition logic

🏗️ High-Level Architecture of First-5-Agent Workflow¶

The First-5-Agents MVP is structured as a modular, event-driven, traceable pipeline where each agent performs a single specialized function. The entire system is built using:

Clean Architecture principles
Domain-Driven Design (DDD)
Autonomous microservices generated from standardized templates
Azure DevOps CI/CD for code delivery
OpenTelemetry for traceability and observability

🧠 High-Level Agent Execution Stack¶

graph TD
    subgraph AgentWorkflow
        A1[Vision Architect Agent] --> A2[Enterprise Architect Agent]
        A2 --> A3[Microservice Generator Agent]
        A3 --> A4[Code Committer Agent]
        A4 --> A5[Pull Request Creator Agent]
    end

    subgraph CI_CD
        A5 --> DevOps[Azure DevOps Pipeline]
        DevOps --> Deployment[Docker / Azure Container App]
    end

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🧱 Layered Stack Model¶

Layer	Components
Agent Layer	5 autonomous agents (Vision, Arch, Generator, Commit, PR)
Template Layer	`connectsoft-microservice` (.NET template)
Infrastructure Layer	Azure DevOps, Git repo, Docker, optional Pulumi
Observability Layer	Serilog, OpenTelemetry, trace IDs
Artifact Layer	`BookingService` microservice (REST, NHibernate, MassTransit)

📋 Input → Output Chain¶

Phase	Input	Output
1️⃣ Vision	Idea	Structured Vision Document
2️⃣ Architecture	Vision Document	Blueprint + Domain Contracts
3️⃣ Generation	Blueprint + Contracts	Codebase (multi-project solution)
4️⃣ Commit	Codebase	Git commit + trace metadata
5️⃣ Pull Request	Git changes	PR with description + CI trigger

Each agent adds semantic trace metadata like trace_id, agent_name, blueprint_id, artifact_id into logs and outputs.

👥 List of the 5 Core Agents in Scope¶

The First-5-Agents MVP introduces a minimal yet complete pipeline by orchestrating 5 specialized autonomous agents. Each agent operates as a standalone microservice (or component) with clear boundaries, traceability, and defined roles.

🧠 Agent 1: Vision Architect Agent¶

Role: Transforms a human prompt or idea into a structured, machine-consumable Vision Document
Outputs: Problem statement, opportunity, personas, feature list, software classification
Trigger: Receives raw idea or input message

🏗 Agent 2: Enterprise Architect Agent¶

Role: Converts the Vision Document into a formal System Architecture Blueprint
Outputs: Bounded contexts, aggregates, service boundaries, use cases, transport choices (e.g., REST, messaging)
Trigger: VisionDocumentCreated event

🧱 Agent 3: Microservice Generator Agent¶

Role: Generates a full Clean Architecture-based microservice using the connectsoft-microservice template
Outputs: Multi-project .NET solution for a domain-specific service
Trigger: SystemArchitectureBlueprintCreated event

🧾 Agent 4: Code Committer Agent¶

Role: Commits the generated codebase to a Git repository (Azure DevOps) with embedded traceability
Outputs: Git commit with trace_id, changelog, and commit metadata
Trigger: MicroserviceGenerated event

📦 Agent 5: Pull Request Creator Agent¶

Role: Creates a well-formed Pull Request from the committed code, linking to upstream trace ID, blueprint, and triggering CI
Outputs: Pull Request with structured title/body, trace metadata, and CI build trigger
Trigger: CodeCommitted event

🔁 Agent Collaboration Pattern¶

These agents follow the Sequential Handoff Pattern, where each output becomes the structured input of the next. The handoff is traceable, observable, and event-driven (not tightly coupled).

📌 Trace Metadata Fields (Required)¶

All agents must enrich their outputs with:

trace_id — unique lifecycle execution ID
agent_id — name or ID of the agent
blueprint_id — optional, links to the architectural artifact
created_at — UTC timestamp
artifact_uri — if applicable, where the output is stored

🧠 Vision Architect Agent — Purpose & Responsibilities¶

The Vision Architect Agent is the entry point of the ConnectSoft AI Software Factory pipeline. It converts human-level input (ideas, prompts, business goals) into structured, machine-ready artifacts called Vision Documents.

This agent establishes the foundation for all downstream agents by formalizing the intent behind a software solution.

🎯 Core Purpose¶

Transform freeform feature ideas into structured, traceable, multi-agent-compatible Vision Documents.

This ensures that architectural and engineering agents can reason over the system using shared terminology, objectives, and constraints.

📋 Responsibilities¶

Responsibility	Description
Idea Structuring	Parse raw input text and extract key goals, context, and pain points
Problem Framing	Define the core business or technical problem the system solves
Opportunity Mapping	Identify potential value, innovation vectors, or differentiators
Solution Conceptualization	Propose a high-level solution without implementation details
Software Classification	Classify the system: SaaS, API, Mobile, AI agent platform, etc.
Feature Decomposition	List initial modules, user goals, or features (non-technical)
Persona Definition	Identify target users, stakeholders, external systems
Success Criteria	Define measurable or outcome-based indicators of success
Vision Artifact Creation	Create a structured Vision Document with embedded metadata
Emit System Event	Publish `VisionDocumentCreated` with trace ID and payload for downstream agents

📦 Output: Vision Document¶

The output is a structured Markdown + JSON artifact containing:

Problem definition
Opportunity description
Solution summary
Software type
Features/modules
Personas
Success metrics
trace_id, agent_id, and semantic metadata

This document is consumed by the Enterprise Architect Agent to design the system blueprint.

🧾 Vision Architect Agent — Inputs, Outputs, and Prompt Patterns¶

The Vision Architect Agent operates by transforming a raw, often vague idea into a structured vision document that downstream agents can act on. This is achieved using semantic prompt orchestration, domain knowledge, and traceability metadata.

📥 Inputs¶

Input Type	Description	Example
Raw Idea (Text)	User-provided feature description or goal	“Build a BookingService with REST API and messaging.”
Context Tags	Optional tags like domain, priority, or constraints	`"domain": "Scheduling", "target": "SaaS"`
Session Metadata	Trace ID, blueprint group, originating user or agent	`trace_id: booking-core-v1`
Optional Artifacts	Existing documents, workflows, or diagrams (if available)	Markdown notes, PDFs, whiteboard export

📤 Outputs¶

Output Type	Description	Format
Vision Document (Markdown)	Structured narrative artifact for humans and agents	`.md`
Vision Metadata (JSON)	Compact semantic version for downstream systems	`.json`
System Event	`VisionDocumentCreated` event with links and trace info	Event Bus message (JSON payload)

Sample Output Fields:¶

{
  "trace_id": "booking-core-v1",
  "agent_id": "vision-architect",
  "softwareType": ["SaaS", "API"],
  "features": ["Appointment Scheduling", "Notifications"],
  "personas": ["Clinic Admin", "Customer"],
  "successMetrics": ["Reduce no-shows by 25%"]
}

🧠 Prompt Template Pattern¶

📥 Input Prompt Template (to the agent):¶

Human input:
"Build a BookingService with REST API and messaging."

You must respond with:
1. Problem Definition  
2. Opportunity  
3. Proposed Solution (non-technical)  
4. Software Type  
5. Feature List  
6. Stakeholders / Personas  
7. Success Metrics  
8. Vision Document (Markdown + JSON)

🧾 System Prompt:¶

You are a Vision Architect Agent. Convert freeform business ideas into structured software vision documents. Output must include classification, features, personas, and traceable metadata. Format output in Markdown + JSON. Maintain Clean Architecture compatibility.

🏗️ Enterprise Architect Agent — Scope & Domain Modeling¶

The Enterprise Architect Agent is responsible for transforming a high-level Vision Document into a formal, structured architecture blueprint. This blueprint becomes the foundation for system decomposition, service generation, messaging design, and clean architectural alignment.

🎯 Core Scope¶

Convert vision-level ideas into modular, domain-aligned, event-driven, and cloud-native architecture blueprints that can be implemented autonomously.

The agent operates across business, domain, and technical boundaries, enabling multi-agent collaboration and scalable service orchestration.

🧱 Primary Responsibilities¶

Area	Description
Domain Decomposition	Identify bounded contexts and model the core domain using DDD principles
Aggregate Identification	Define key aggregates/entities that reflect core business concepts
Use Case Design	Specify high-level use cases and operations that the service must support
Transport Selection	Decide whether communication should be via REST, messaging, gRPC, etc.
Service Boundary Definition	Establish microservice boundaries and team/agent ownership guidelines
Architecture Blueprint Creation	Output a structured artifact (`SystemArchitectureBlueprint`) with all core decisions
Emit Event	Publish `SystemArchitectureBlueprintCreated` event for generator agent

🧩 Example Domain Output (for BookingService)¶

bounded_context: Booking
aggregate_root: Appointment
value_objects:
  - TimeSlot
  - Location
  - ProviderId
use_cases:
  - CreateAppointment
  - CancelAppointment
  - GetAvailableSlots
service_model: RestApi
persistence_model: NHibernate
messaging_model: MassTransit

📦 Architectural Blueprint Includes¶

Domain model (aggregates, value objects)
Application use cases
Layer separation (Clean Architecture)
Service boundaries
Messaging and integration strategy
Traceability fields (trace_id, agent_id, blueprint_id)

👥 Collaboration¶

Consumes output from Vision Architect Agent
Produces structured inputs for:
- Microservice Generator Agent
- API Designer Agent (in later stages)
- Event-Driven Architect Agent (if needed)

📐 Enterprise Architect Agent — Outputs and Use Case Design¶

The Enterprise Architect Agent produces a System Architecture Blueprint that encapsulates the structure, behavior, and operational boundaries of the system to be built. This output serves as the formal contract for downstream engineering agents, particularly the Microservice Generator Agent.

📤 Outputs¶

Output	Description
SystemArchitectureBlueprint (YAML/JSON)	Describes domain, use cases, layers, persistence, and transport strategies
Event Emission	Emits `SystemArchitectureBlueprintCreated` event with trace ID and artifact reference
Domain Model Package	A structured model of entities, aggregates, and value objects
Use Case Definitions	Declared contracts for application-layer operations
Metadata	Includes `trace_id`, `agent_id`, `blueprint_id`, domain tags, ownership, timestamps

🧩 Example Output (YAML Blueprint)¶

trace_id: booking-core-v1
agent_id: enterprise-architect
blueprint_id: booking-service-blueprint
bounded_context: Booking
aggregate_root: Appointment
entities:
  - Appointment
  - Provider
value_objects:
  - TimeSlot
  - Location
use_cases:
  - CreateAppointment
  - CancelAppointment
  - GetAvailableSlots
service_model: RestApi
persistence_model: NHibernate
messaging_model: MassTransit
authentication_required: true

🧠 Use Case Design Philosophy¶

Each use case reflects a business operation modeled using Clean Architecture and DDD boundaries. It defines the application service contract, including:

Field	Description
`name`	Name of the use case (`CreateAppointment`)
`input_dto`	Input data structure
`output_dto`	Output/result
`preconditions`	Optional constraints or validation rules
`postconditions`	Optional success criteria or follow-up events
`events_emitted`	Domain or integration events published (e.g. `AppointmentCreated`)

📦 Use Case Example (CreateAppointment)¶

name: CreateAppointment
input_dto:
  - patient_id: string
  - provider_id: string
  - start_time: datetime
  - end_time: datetime
output_dto:
  - appointment_id: string
events_emitted:
  - AppointmentCreated

🔁 Downstream Consumption¶

The blueprint and use case definitions are consumed by:

✅ Microservice Generator Agent
✅ Code Committer Agent (as metadata for trace)
✅ CI pipeline scripts (optional code-gen or lint rules)

🧱 Microservice Generator Agent — Template Logic & CLI Parameters¶

The Microservice Generator Agent transforms a structured System Architecture Blueprint into a real, production-grade microservice using the ConnectSoft .NET template system.

This agent acts as a blueprint interpreter and template executor, ensuring consistent generation of modular, observable, and agent-compatible codebases.

🎯 Core Function¶

Parse the architectural blueprint and invoke the appropriate parameters of the connectsoft-microservice template → to scaffold a full Clean Architecture solution aligned to DDD, EDA, and factory-wide conventions.

🧩 Template Engine¶

Template: connectsoft-microservice
Language: C# (.NET 8)
Mechanism: Invokes dotnet new CLI with extracted blueprint metadata
Extensibility: Supports AI-based parameter mapping and dynamic toggling of modules (REST, gRPC, messaging, AI)

⚙️ Key CLI Parameters (mapped from blueprint)¶

CLI Parameter	Derived From	Example
`--AggregateRootName`	`aggregate_root`	`Appointment`
`--ServiceModelType`	`service_model`	`RestApi`
`--PersistenceModelType`	`persistence_model`	`NHibernate`
`--MessagingModelType`	`messaging_model`	`MassTransit`
`--UseSemanticKernel`	enabled by default	`true`
`--UseOpenAI`	optional	`false`
`--Swagger`, `--HealthCheck`, `--Docker`	always included	`true`
`--TraceId` (custom)	`trace_id`	`booking-core-v1`

🚀 Sample CLI Command (BookingService)¶

dotnet new connectsoft-microservice \
  --AggregateRootName Appointment \
  --ServiceModelType RestApi \
  --PersistenceModelType NHibernate \
  --MessagingModelType MassTransit \
  --UseSemanticKernel true \
  --UseOpenAI false \
  --Swagger true \
  --HealthCheck true \
  --Docker true

✅ Output: A fully structured, multi-project solution ready for commit, traceable by trace_id and blueprint_id.

📦 Output Structure (Example)¶

BookingService/
├── DomainModel/
├── EntityModel/
├── ApplicationModel/
├── ServiceModel.RestApi/
├── MessagingModel/
├── FlowModel.MassTransit/
├── PersistenceModel.NHibernate/
├── Application/
├── UnitTests/
├── DockerCompose/
├── InfrastructureModel/
└── SemanticKernel/

🧠 Intelligent Parameter Mapping (AI Agent)¶

The agent supports intelligent mapping:

Reads SystemArchitectureBlueprint file
Translates domain values into CLI switches
Auto-injects trace metadata and initial documentation

🧱 Microservice Generator Agent — Output Structure and Project Breakdown¶

Once the Microservice Generator Agent executes the connectsoft-microservice template, it produces a multi-project solution aligned with Clean Architecture and domain-driven design. Each project is modular, traceable, and ready for CI/CD and agent collaboration.

📦 Output: Multi-Project Microservice Structure¶

ConnectSoft.BookingService/
├── DomainModel/                    # Use case interfaces, domain services
├── EntityModel/                    # Aggregates, entities, value objects
├── DomainModel.Impl/              # Use case implementations
├── ApplicationModel/              # DI registration, middleware config
├── Application/                   # Host application (Main)
├── ServiceModel.RestApi/          # REST controllers, API endpoints
├── PersistenceModel.NHibernate/   # ORM mappings, repositories
├── MessagingModel/                # Event contracts (e.g., AppointmentCreated)
├── FlowModel.MassTransit/         # Consumers, sagas, messaging integration
├── SemanticKernel/                # AI skill registration (optional)
├── DockerCompose/                 # Dev/test container orchestration
├── InfrastructureModel/           # Pulumi / deployment config (optional)
├── UnitTests/                     # Unit testing layer
└── ArchitectureModel/             # Mermaid diagrams, service documentation

🧱 Clean Architecture Layer Mapping¶

graph LR
    UI[ServiceModel.RestApi] --> App[ApplicationModel]
    App --> Domain[DomainModel]
    Domain --> Entity[EntityModel]
    App --> Infra[PersistenceModel + MessagingModel]

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No inner layer references outer layers
DI bindings managed in ApplicationModel
Entry point (Main) resides in Application

🧪 Cross-Cutting Features (Generated)¶

Feature	Included?
OpenTelemetry Tracing	✅
Serilog Structured Logging	✅
ProblemDetails API Errors	✅
FluentValidation	✅
Health Checks (`/health/live`, etc.)	✅
Swagger UI (`/swagger`)	✅
Feature Flags Support	✅
Dockerfile + Docker Compose	✅
CI-ready Git Layout	✅

🧠 Traceability Hooks in Code¶

Every generated service embeds trace ID support via:

Logging enrichers (WithTraceId())
Correlation ID propagation
Metadata in Swagger and CI

This allows downstream agents (e.g., QA, observability) to connect artifacts to the trace_id and blueprint_id.

🧾 Code Committer Agent — Git Actions, Metadata, CI Triggering¶

The Code Committer Agent is responsible for safely and traceably pushing generated code into version control (Azure DevOps Git). It ensures every commit is auditable, agent-attributed, and ready to trigger CI/CD workflows automatically.

🎯 Core Responsibilities¶

Responsibility	Description
Git Integration	Clone/pull the correct repo and branch
Stage & Commit	Add all generated files with proper trace metadata
Metadata Enrichment	Annotate commit with `trace_id`, `agent_id`, `blueprint_id`
Change Log Generation	Create or update `CHANGELOG.md` entry
Event Emission	Emit `CodeCommitted` event with commit ID and URI
Trigger CI/CD	Push commit to tracked branch to start Azure DevOps pipeline

🗃️ Example Git Commit Metadata¶

git commit -m "feat: initial BookingService microservice [trace_id: booking-core-v1] [agent: code-committer]"

Embedded Metadata (in commit message or Git notes):¶

Field	Value
`trace_id`	`booking-core-v1`
`agent_id`	`code-committer`
`blueprint_id`	`booking-service-blueprint`
`timestamp`	`UTC now`
`artifact_type`	`microservice-template-generated`

📄 Changelog Template (Auto-generated)¶

## [booking-core-v1] - 2025-05-11
### Added
- BookingService microservice scaffolded via connectsoft-microservice
- REST API for Appointment use cases
- NHibernate setup and MassTransit messaging
- OpenTelemetry, health checks, and Swagger

🔄 Event Emission¶

After committing:

{
  "event": "CodeCommitted",
  "trace_id": "booking-core-v1",
  "agent": "CodeCommitter",
  "commit_sha": "c9e2f84",
  "repo": "https://dev.azure.com/connectsoft/BookingService",
  "branch": "main",
  "changelog_uri": "CHANGELOG.md"
}

🚀 CI/CD Triggering¶

The agent pushes to a branch that has an Azure DevOps pipeline YAML configured (e.g., main or dev), ensuring:

Immediate build of the generated microservice
Deployment to Docker / Azure environment
Test and telemetry validation

📦 Pull Request Creator Agent — PR Title, Traceability, Collaboration¶

The Pull Request Creator Agent finalizes the delivery of generated code by creating a structured, traceable, and CI-integrated pull request. It ensures human and agent collaborators can review, approve, and merge changes into the production branch — closing the loop on agent-driven delivery.

🎯 Core Responsibilities¶

Responsibility	Description
PR Creation	Create a pull request on Azure DevOps for the generated commit(s)
Trace Metadata Injection	Include `trace_id`, `agent_id`, and `blueprint_id` in the title and body
Summary Description	Auto-generate a concise overview of what the PR includes
CI/CD Integration	Ensure PR triggers build + test pipelines
Linkage to Issue or Blueprint	Optionally reference a vision, ticket, or design doc

📝 Example PR Title¶

feat: BookingService initial scaffold [trace_id: booking-core-v1]

📄 Example PR Body¶

### 📦 Summary

This pull request adds the generated microservice `BookingService`, created by the Microservice Generator Agent from blueprint `booking-service-blueprint`.

---

### 🧠 Metadata

- `trace_id`: `booking-core-v1`
- `blueprint_id`: `booking-service-blueprint`
- `agent_id`: `pull-request-creator`
- `created_at`: `2025-05-11T08:45:00Z`

---

### ✅ Included Features

- REST API controllers for Appointment use cases
- NHibernate mappings and repository interfaces
- MassTransit consumers for domain events
- OpenTelemetry tracing, health checks, Swagger UI

---

### 📌 Notes

This PR is auto-generated and ready for CI build/test. Please review structure and trace alignment.

🔗 Optional Linkages¶

Linked Object	Example
Blueprint Doc	`/docs/booking/booking-service-blueprint.md`
Vision Doc	`/docs/booking/booking-service-vision.md`
Agent Execution Log	`/logs/booking-core-v1/execution.json`
Changelog	`/CHANGELOG.md`

🔁 Collaboration & Notification¶

The agent may also:

Assign reviewers (e.g. DevOps or QA agents in future)
Tag based on agent group (e.g. autogen, scheduling)
Add a comment webhook to post updates (e.g., build pass/fail)

🔄 Agent Interaction Flow — Event-Driven Handoff & Traceability Chain¶

The First-5-Agents MVP relies on an event-driven, loosely coupled architecture, where each agent reacts to structured events emitted by its predecessor. This allows agents to operate independently, maintain traceability, and scale horizontally — all while preserving a shared execution context via trace_id.

🧭 Event-Driven Handoff Pattern¶

Each agent emits a domain event that acts as a handoff trigger for the next agent. These events include:

A reference to the produced artifact (e.g., Vision Document, Blueprint, Git commit)
The trace_id, which unifies all steps in the same generation flow
Agent identity and timestamp for audit and observability

📜 Sequential Event Chain¶

sequenceDiagram
    participant User as Human Prompt
    participant Vision as Vision Architect Agent
    participant Arch as Enterprise Architect Agent
    participant Gen as Microservice Generator Agent
    participant Commit as Code Committer Agent
    participant PR as Pull Request Creator Agent

    User->>Vision: "Build BookingService"
    Vision->>Vision: Generate Vision Document
    Vision-->>Arch: Emit `VisionDocumentCreated`

    Arch->>Arch: Generate Architecture Blueprint
    Arch-->>Gen: Emit `SystemArchitectureBlueprintCreated`

    Gen->>Gen: Run CLI, Generate Codebase
    Gen-->>Commit: Emit `MicroserviceGenerated`

    Commit->>Commit: Git commit with trace metadata
    Commit-->>PR: Emit `CodeCommitted`

    PR->>PR: Create Pull Request
    PR-->>CI: Azure DevOps Build Triggered

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🧩 Traceability Graph¶

Each artifact (document, blueprint, commit, PR) includes the following minimum trace metadata:

Field	Description
`trace_id`	Unifies all artifacts and events in one logical execution
`agent_id`	Indicates which agent produced the artifact
`blueprint_id`	Links to the architectural specification
`artifact_type`	Vision, Architecture, Service Code, Git Commit, PR, etc.
`timestamp`	UTC timestamp of creation
`artifact_uri`	Pointer to file, repo, or URL

🧠 Trace ID = the connective tissue All agents, artifacts, commits, and events in one generation flow are traceable through a single trace_id.

📂 Event Payload Example¶

{
  "event": "MicroserviceGenerated",
  "trace_id": "booking-core-v1",
  "agent_id": "microservice-generator",
  "blueprint_id": "booking-service-blueprint",
  "output_path": "/services/BookingService",
  "timestamp": "2025-05-11T08:33:21Z"
}

🛡️ Benefits of Event-Driven Agent Interaction¶

✅ Loose coupling – Agents don’t call each other directly
✅ Parallelization Ready – Multiple services can generate in parallel
✅ Auditable – Every step is logged and trace-linked
✅ Recoverable – Any failed step can be retried independently
✅ Composable – Future agents (QA, Security, Observability) can subscribe to the same events

📊 Observability, Tracing, and Metadata Enrichment per Agent¶

The First-5-Agents MVP is designed with observability-first principles, ensuring that every agent’s execution can be traced, measured, audited, and debugged — both in real time and retrospectively.

Each agent enriches its outputs and actions with standardized metadata and emits OpenTelemetry (OTEL)-compliant spans, logs, and metrics.

🧭 Core Observability Features¶

Feature	Description
Trace ID Propagation	Each agent execution carries a `trace_id` linking all artifacts together
OpenTelemetry Traces	Each major action is wrapped in a traceable span
Structured Logging (Serilog)	Logs include `trace_id`, `agent_id`, and `artifact_id` in every message
Metrics Exposure	Agents expose Prometheus-compatible metrics (e.g., execution time, failure count)
Validation & Retry Logs	Failures and recovery attempts are logged with full input/output diffs

📋 Common Metadata Fields¶

Every output and log line from an agent includes:

Field	Example
`trace_id`	`booking-core-v1`
`agent_id`	`microservice-generator`
`execution_id`	`msgen-8734-x`
`artifact_type`	`ArchitectureBlueprint`, `MicroserviceCode`
`created_at`	`2025-05-11T08:47:00Z`
`duration_ms`	`1750`
`status`	`success`, `fail`, `retry`

📈 OpenTelemetry Tracing Example¶

→ [trace_id=booking-core-v1] [agent=enterprise-architect]
   span: GenerateBlueprint [✔ 248ms]
→ [trace_id=booking-core-v1] [agent=microservice-generator]
   span: ExecuteCLI 'dotnet new connectsoft-microservice ...' [✔ 3.2s]
→ [trace_id=booking-core-v1] [agent=code-committer]
   span: CommitToGit 'initial scaffold' [✔ 612ms]

All spans are tagged with:

trace_id
agent_id
blueprint_id
artifact_uri
span_status

✅ Agents emit spans and logs via built-in exporters: OTLP → Application Insights / Prometheus / Jaeger

🧪 Failure Recovery Visibility¶

Failures are logged with full input → output diff, retry markers, and escalation hints:

{
  "trace_id": "booking-core-v1",
  "agent_id": "microservice-generator",
  "status": "fail",
  "error": "Template execution failed: invalid parameter 'PersistenceModelType'",
  "attempt": 1,
  "next_action": "retry_with_corrected_parameter"
}

🛰️ Observability Pipeline¶

graph TD
  AgentExec --> OTELCollector --> ApplicationInsights
  AgentExec --> MetricsEndpoint --> Prometheus
  AgentExec --> StructuredLogs --> AzureLogAnalytics

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📌 Optional Enhancements (Post-MVP)¶

Correlate Git commits and PRs in dashboards
Visualize agent spans with Gantt-style execution timelines
Integrate logs into a searchable trace explorer
Include agent resource usage (CPU/memory) per task

🚀 Deployment Environment — Azure DevOps Pipelines, Docker, Pulumi¶

The deployment phase of the First-5-Agents MVP is handled via a cloud-native CI/CD pipeline, configured in Azure DevOps, and powered by Docker and optionally Pulumi for infrastructure-as-code.

This environment ensures that every agent-generated service is:

Built reproducibly
Deployed consistently
Observable in production or dev
Traceable back to agent actions and commits

🛠️ CI/CD Pipeline – Azure DevOps¶

Each generated microservice includes a preconfigured azure-pipelines.yml file, which handles:

Stage	Tasks
Build	Restore NuGet, compile all projects
Test	Run unit tests, generate coverage
Package	Build Docker image
Push	Push image to Azure Container Registry (or local registry)
Deploy	Use Docker or Pulumi to deploy to Azure App Service, Container Apps, or Kubernetes
Trace Injection	Annotate logs and environment with `trace_id`, `artifact_type`, `agent_id`

🧪 Sample `azure-pipelines.yml`¶

trigger:
  branches:
    include: [main]

variables:
  trace_id: '$(Build.SourceBranchName)-$(Build.BuildId)'

stages:
  - stage: BuildAndTest
    jobs:
      - job: Build
        steps:
          - task: UseDotNet@2
            inputs:
              packageType: sdk
              version: '8.x'
          - script: dotnet build --configuration Release
          - script: dotnet test --no-build --logger trx

  - stage: Dockerize
    jobs:
      - job: DockerBuild
        steps:
          - task: Docker@2
            inputs:
              command: buildAndPush
              repository: connectsoft/booking-service
              tags: |
                latest
                $(Build.BuildId)

🐳 Docker-Based Runtime (Default)¶

Each microservice is packaged as a Docker container, with:

Health checks (/health/live, /health/ready)
Trace ID injection via environment variables
Logging via Serilog to console or file
OpenTelemetry exporter preconfigured

✅ This makes it deployable locally, on AKS, Azure Container Apps, or Docker Compose

🌐 Optional: Pulumi-Based IaC¶

If --UsePulumi is enabled during generation:

Resource	Pulumi Module
Azure App Service	`azure-native:web`
Azure Key Vault	`azure-native:keyvault`
Azure Container Registry	`azure-native:containerregistry`
Service Bus	`azure-native:servicebus`
DNS/SSL	`azure-native:network`

This allows agents or infrastructure scripts to deploy environments via Pulumi CLI, using typesafe C#.

🔁 Traceability in Deployment Logs¶

Deployment logs automatically include:

{
  "trace_id": "booking-core-v1",
  "stage": "Deploy",
  "agent": "pull-request-creator",
  "container_tag": "booking-service:20250511.1",
  "status": "Success",
  "region": "East US"
}

🧩 Future Enhancements (Post-MVP)¶

Blue/green deployments with rollback
Deployment agent with observability feedback loop
Canary testing support
GitOps sync integration (e.g., FluxCD, ArgoCD)

🔮 Future Enhancements — Agent Insights, Validation, Feedback Loops¶

While the First-5-Agents MVP delivers a complete autonomous generation loop, the long-term evolution of the ConnectSoft AI Software Factory depends on layering in insight, self-correction, validation, and feedback-driven intelligence.

These enhancements will move the system from automated code generation to a resilient, adaptive software production ecosystem.

📊 1. Agent Insights Service (Telemetry and Audit)¶

A lightweight microservice (AgentInsightsService) can be introduced to:

Collect and store agent span data (start/stop, duration, output status)
Track failures, retries, and human interventions
Enable dashboards for:
- Most active agents
- Generation time per service
- Failed executions by cause
Correlate agent output with test and deploy metrics

💡 Powered by OpenTelemetry + MongoDB or SQL + SignalR for live updates.

✅ 2. Output Validation Agent (Structure, Semantics, Security)¶

Introduce a new agent to review the outputs from any previous agent:

Validation Area	Check
Structure	Does the vision/blueprint/code follow ConnectSoft schemas?
Semantics	Are domain concepts and use cases coherent?
Security	Are sensitive data fields encrypted, protected, and role-restricted?
Traceability	Does the output carry `trace_id`, `agent_id`, and versioning?

This agent can act before PR creation or deployment, enabling a guardrail layer.

🔁 3. Feedback Loop Integration (Autonomous Improvement)¶

Agents can evolve through internal feedback signals, such as:

✅ Automated test failures → trigger bug resolver agent
✅ Architecture validation failures → trigger blueprint correction
✅ Human review flags → post feedback to root trace_id thread

🧠 These patterns allow agents to self-heal, ask for clarification, or adapt prompt strategies.

🧪 4. Metrics + Anomaly Detection¶

The platform can detect patterns in:

Repeated failures from the same agent type
Latency spikes in specific blueprint configurations
Commit→deploy cycles that exceed expected durations

This paves the way for runtime optimizers, cost controllers, and auto-scaling logic.

📌 Future Agents Enabled by These Enhancements¶

Agent	Role
`AgentInsightsCollector`	Real-time trace and span logger
`OutputValidatorAgent`	Pre-PR compliance and contract enforcer
`FeedbackRouterAgent`	Aggregates error, QA, and human feedback into agent prompts
`BlueprintCorrectionAgent`	Refines system architecture from failed attempts

🧭 Summary, Roadmap, and Next Steps¶

The First-5-Agents MVP lays the foundational loop of the ConnectSoft AI Software Factory, enabling the platform to autonomously turn a high-level idea into a production-ready microservice — with traceability, observability, and CI/CD integration.

This MVP proves the feasibility of agentic software creation through modular, event-driven workflows and AI-enhanced execution.

✅ What We’ve Built¶

Phase	Outcome
🧠 Vision Input →	Structured Vision Document
🏗 Architecture →	Clean, modular System Architecture Blueprint
🧱 Generation →	Full microservice scaffold using `connectsoft-microservice`
🧾 Commit →	Git commit with full trace metadata
📦 Pull Request →	CI-enabled PR linked to upstream artifacts
🚀 Deploy →	Service deployed via Azure DevOps & Docker/Pulumi
📊 Trace →	OTEL spans and structured logs per agent execution

🔁 MVP Agent Lifecycle Summary¶

flowchart TD
    Input["Human Prompt"] --> Vision["Vision Architect Agent"]
    Vision --> Blueprint["Enterprise Architect Agent"]
    Blueprint --> Code["Microservice Generator Agent"]
    Code --> Commit["Code Committer Agent"]
    Commit --> PR["Pull Request Creator Agent"]
    PR --> Azure["Azure DevOps CI/CD"]
    Azure --> Deploy["Microservice Live"]

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

🧠 Core Agent Roles Recap¶

Agent	Function
Vision Architect	Converts ideas into structured software visions
Enterprise Architect	Creates the domain, use case, and transport blueprint
Microservice Generator	Scaffolds full service based on Clean Architecture
Code Committer	Pushes commits with `trace_id`, version info
Pull Request Creator	Publishes PRs and triggers CI/CD workflows

🔮 Roadmap: What Comes Next¶

Stage	Focus
🔁 Agent Insights Service	Real-time telemetry collection and visualization
🔒 Output Validation Agent	Pre-PR contract enforcement
👥 Human-in-the-loop Feedback	Review triggers for failed agent paths
🧠 Feedback-Enriched Agents	Enable retry, improvement, and correction
🌍 Component Registry Service	Catalog generated services and their metadata
📚 Knowledge Memory Integration	Long-term blueprint and vision recall via vector DB
🎛 MCP-Based Orchestration	Introduce formal agent planner + skill router (MCP server)

🚀 Next Steps¶

✅ Finish .NET new generation and commit BookingService
✅ Finalize Azure DevOps CI/CD pipeline and deployment
✅ Log all agent events with trace_id
✅ Create an execution transcript for this MVP
🔁 Begin design of AgentInsightsService and OutputValidatorAgent
🧠 Expand agent catalog with additional engineering and orchestration roles

🎯 The MVP demonstrates that ConnectSoft can autonomously generate, trace, and ship software components. From here, we begin the journey toward fully adaptive, insight-driven software factories.