📘 Blueprints in the AI Software Factory¶

🧩 Overview of Blueprints¶

🧩 What Is a Blueprint?¶

In the ConnectSoft AI Software Factory, a blueprint is a structured, declarative plan that guides intelligent agents in generating, validating, and delivering autonomous software components. It is more than configuration — it is an execution contract between planning agents and generation agents.

🔍 Why Blueprints Matter¶

Most code generation tools stop at producing a file. In contrast, the AI Software Factory uses blueprints to:

Orchestrate modular service generation across microservices, infrastructure, tests, and UI.
Ensure traceability of every action with metadata like traceId, blueprintId, agentId.
Link agent decisions to outcomes, enabling retries, auditing, and debugging.
Structure multi-agent collaboration by defining shared inputs and validated outputs.

🧠 Key Properties of a Blueprint¶

Property	Description
Declarative	Describes intent and structure, not implementation
AI-Readable	Used by semantic agents and planners as structured input
Composable	Can embed or reference DSLs (Domain-Specific Language fragments)
Traceable	Every blueprint is tied to execution metadata (e.g., `traceId`, `version`)
Versioned	Stored as versioned files in memory and Git-compatible formats
Reusable	Indexed and semantically linked across services and tenants

🔁 How Blueprints Fit Into the Factory Flow¶

flowchart TD
    Idea["💡 Prompt / Feature Request"]
    VBP["🧩 Vision Blueprint"]
    SBP["🧩 Service Blueprint"]
    MBP["🧩 Module Blueprint"]
    ART["🛠️ Artifacts: Code, Tests, Infra, Docs"]

    Idea --> VBP
    VBP --> SBP
    SBP --> MBP
    MBP --> ART

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

This diagram represents the flow from idea to execution. Each blueprint refines the previous one and adds structure until artifacts are fully generated.

🎛️ Blueprint vs DSL vs Artifact¶

Layer	Role	Example
DSL	Atomic building block	`AppointmentBookedEvent`, `UserEntity`
Blueprint	Composite plan for generation	`booking-service-blueprint.yaml`
Artifact	Output from agent execution	`.cs`, `.bicep`, `.feature`, `.md`

🔗 Blueprints compose DSLs and produce artifacts.

🗂️ Format and Structure¶

Blueprints are typically stored as structured YAML files with consistent fields:

blueprintId: booking-service-v1
type: Service
traceId: 2b3471f9
version: 1.0.0
dsl:
  - ref: dsls/entities/Appointment.yaml
  - ref: dsls/events/AppointmentBooked.yaml
outputs:
  - service: BookingService
    handlers:
      - BookAppointmentCommand

🧭 Who Produces Blueprints?¶

Blueprints are emitted or consumed by agents during generation orchestration:

Agent Type	Blueprint Roles
Vision Architect Agent	Produces `VisionBlueprint`
Product Manager Agent	Produces `ServiceBlueprint`
DevOps Agent	Produces `PipelineBlueprint`
QA Agent	Consumes `ServiceBlueprint` and produces `TestBlueprint`

Each blueprint is traceable to its originating agent and skill.

✅ Summary¶

Blueprints are central knowledge artifacts in the AI Software Factory.
They serve as structured contracts between idea and implementation.
DSLs are reusable fragments used inside or referenced by blueprints.
Blueprints drive the generation of real artifacts through orchestrated agents.

🧩 DSL vs Blueprint vs Artifact¶

In the AI Software Factory, it's essential to clearly distinguish between:

DSLs (Domain-Specific Languages): fine-grained declarative building blocks
Blueprints: structured, traceable plans that orchestrate generation
Artifacts: tangible outputs like code files, diagrams, tests, and pipelines

🔣 1. What Is a DSL?¶

A DSL is a small, focused language designed to represent a narrow concept in a reusable, composable way.

Characteristic	Description
Atomic	Represents a single concern (e.g., `Entity`, `Event`, `Scope`)
Reusable	Can be referenced across multiple blueprints
Validatable	Has a schema, supports linting and safety checks
Versioned	Stored as `.yaml` or `.dsl.yaml`, often in `dsls/` folder

🧾 Example: EventDSL¶

type: Event
name: AppointmentBooked
source: BookingService
payload:
  - field: appointmentId
    type: Guid
  - field: time
    type: DateTime

🧩 2. What Is a Blueprint?¶

A blueprint is a composite, structured contract that defines how agents will generate or validate part of the system. Blueprints typically:

Reference or embed multiple DSLs
Define orchestration metadata (blueprintId, traceId)
Serve as inputs/outputs for agent workflows
Govern generation behavior, not just description

🧾 Example: Blueprint referencing DSLs¶

blueprintId: booking-service
type: Service
version: 1.0.0
agent: ProductManager
traceId: abc-123

dsl:
  - ref: dsls/entities/Appointment.yaml
  - ref: dsls/events/AppointmentBooked.yaml

outputs:
  - project: BookingService
    handlers:
      - name: BookAppointmentHandler

🛠️ 3. What Is an Artifact?¶

An artifact is the final output emitted by a code-generation agent or orchestration pipeline. It can be:

Artifact Type	Format	Examples
Code	`.cs`, `.ts`, `.py`	`BookAppointmentHandler.cs`
Test	`.feature`, `.spec.ts`	`appointment-booking.feature`
Infrastructure	`.bicep`, `.tf`, `.yaml`	`queue-provisioning.bicep`
Documentation	`.md`, `.svg`, `.png`	`HowItWorks.md`, `architecture.svg`
Configuration	`.json`, `.env`, `.yaml`	`pipeline.config.yaml`

These artifacts are always traceable back to the blueprint and DSLs used to generate them.

📊 4. DSL vs Blueprint vs Artifact: Side-by-Side Table¶

Category	DSL	Blueprint	Artifact
Role	Declarative fragment	Structured generation contract	Tangible result
Format	`*.dsl.yaml` or inline YAML	`*.blueprint.yaml`	`.cs`, `.bicep`, `.md`, etc.
Ownership	Domain architects, platform agents	Orchestration and planning agents	Generation agents
Versioning	Independent, reusable	Project/module scoped	Git-based or published pipeline
Stored In	DSL registry (Git/blob/memory)	Blueprint registry (Git/blob/memory)	Artifact repo or output storage
Used By	Multiple blueprints	Consumed by agents during execution	Used by DevOps, runtime systems
Example	`UserEntity`, `AppointmentEvent`	`booking-service-blueprint.yaml`	`BookingService.cs`, `bicep.yaml`

🧭 5. Blueprint as an Execution Contract¶

Agents treat a blueprint as a contract between planning and execution:

Input to the generation pipeline
Output of Vision, Product, Architect agents
Consumed by Engineering, QA, DevOps agents

flowchart LR
  subgraph Inputs
    A[📄 Prompt]
    B[🧾 DSLs]
  end

  subgraph Blueprint
    C[🧩 booking-service-blueprint.yaml]
  end

  subgraph Outputs
    D[🛠️ BookAppointmentHandler.cs]
    E[📑 booking.feature]
    F[🔧 provisioning.bicep]
  end

  A --> C
  B --> C
  C --> D
  C --> E
  C --> F

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

✅ Summary¶

DSLs are reusable fragments defining atomic elements like entities or events.
Blueprints are structured plans that compose DSLs and control agent generation.
Artifacts are the end products generated from a blueprint via agent orchestration.

Blueprints are the intelligent bridge between structured domain knowledge and working, observable software.

🧩 Blueprint Lifecycle Overview¶

The blueprint lifecycle defines how blueprints are created, validated, versioned, used by agents, and evolved over time in the AI Software Factory. Each blueprint is not just a static file — it's a living, traceable, versioned object at the heart of generation orchestration.

🔄 1. Blueprint Lifecycle Phases¶

Phase	Description
Created	Initial generation from Vision, Product, or Architect agents
Validated	Checked against schemas, DSLs, references, and required fields
Indexed	Embedded semantically and stored in memory/registry for reuse
Consumed	Used by downstream generation agents (e.g., Engineering, QA, DevOps)
Versioned	Tagged, updated, and optionally frozen for reproducibility
Regenerated	Modified in response to feedback, failed validation, or scope changes
Archived	Older versions retained for traceability or replay

🔧 2. Lifecycle Events¶

Blueprints emit system events at key stages:

Event Type	Emitted By	Purpose
`BlueprintCreated`	Vision/Product Agent	A new blueprint has been generated
`BlueprintValidated`	Validator Agent/Skill	All structural and reference checks passed
`BlueprintUsed`	Generation Agent	A blueprint has triggered artifact generation
`BlueprintVersioned`	Orchestrator/DevOps	A blueprint version is tagged and locked
`BlueprintInvalid`	Validator Agent/Skill	A validation failure occurred
`BlueprintUpdated`	Feedback Loop Agent	A newer revision was produced
`BlueprintArchived`	Blueprint Lifecycle Agent	Blueprint no longer active but retained for trace

Each event carries full trace metadata: traceId, blueprintId, agentId, runId, timestamp.

📦 3. Storage and Representation¶

Blueprints are persisted in the following places:

Layer	Storage Format	Purpose
Git Repo	`*.blueprint.yaml`	Auditable, version-controlled
Blob Storage	Structured YAML/JSON	Large-scale raw storage
Vector DB	Embedded text/vector	Semantic search and similarity
Execution Memory	Indexed object metadata	Runtime state and cross-agent access

🔐 All blueprints are traceable to their originating prompt, agent, and artifacts.

📘 4. Blueprint Lifecycle Diagram¶

flowchart TD
    A[🧠 Prompt or Requirement]
    B[🧩 Blueprint Created]
    C[✔️ Blueprint Validated]
    D[📚 Blueprint Indexed]
    E[🧠 Agent Consumes]
    F[🛠️ Artifacts Generated]
    G[📌 Blueprint Versioned]
    H[♻️ Blueprint Updated or Regenerated]
    I[📦 Blueprint Archived]

    A --> B --> C --> D --> E --> F --> G
    G --> H --> C
    G --> I

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🧠 5. Regeneration Scenarios¶

Blueprints may be automatically or manually updated when:

❌ Validation fails (e.g., missing DSL, invalid DSL version)
🔁 Scope expands (e.g., new feature added)
🧪 Tests fail and require new flows
🔎 Feedback loop suggests improved DSL coverage or structure

Agents like the Feedback Handler, Regression Tracker, or Blueprint Refiner are responsible for initiating these updates.

🧭 6. Agent Roles in the Lifecycle¶

Agent	Lifecycle Action
Vision Architect Agent	Emits initial `VisionBlueprint`
Product Manager Agent	Emits and links `ServiceBlueprint`
Validator Agent	Performs validation, emits events
DevOps Agent	Tags, versions, and stores blueprint
QA Agent	Consumes `TestBlueprint`
Documentation Agent	Uses blueprint metadata for docs
Feedback Loop Agent	Regenerates or evolves blueprint

All steps are coordinated through the Event Mesh, ensuring decoupled yet traceable interactions.

✅ Summary¶

Every blueprint follows a structured lifecycle — from creation to consumption and evolution.
Lifecycle events are emitted at every step for observability and traceability.
Multiple agents participate in the lifecycle, both as producers and consumers.
Regeneration and versioning are core to enabling adaptive, iterative generation.

🧩 Traceability and Metadata¶

Blueprints are not just generation plans — they are traceable, semantically indexed, and observable objects that encode provenance, purpose, and usage context throughout the AI Software Factory.

🛰️ 1. Why Traceability Matters¶

Every blueprint must answer:

🧠 Who generated it? (Agent identity, skill name)
🧭 Why was it generated? (Prompt, feature, requirement)
📎 Where is it used? (Linked microservices, pipelines, test cases)
🔁 What version? (Hash, timestamp, tags)
🧾 What artifacts resulted from it?

This traceability ensures:

Benefit	Outcome
Debuggability	Trace bugs back to blueprint origin
Compliance & Auditing	Show why a change was made, by whom, and from what spec
Regeneration triggers	Change in DSL/requirement auto-updates blueprints
Semantic reuse	Discover similar blueprints via vector search

🧬 2. Core Metadata Fields¶

Every blueprint includes standard metadata fields in its header:

blueprintId: booking-service-v1
type: Service
agent: ProductManager
skill: GenerateServiceBlueprint
traceId: 9fae23ab-28a9-4cf2-a4ed-a839fa8e4cb1
runId: 20240601-BookingFlow
version: 1.2.0
status: Validated
createdAt: 2024-06-01T13:42:00Z
updatedAt: 2024-06-01T14:17:22Z

Field	Purpose
`blueprintId`	Unique identifier for this blueprint instance
`type`	Category: `Vision`, `Service`, `Test`, etc.
`agent`	Name of the emitting agent persona
`skill`	The specific skill/prompt/function that generated it
`traceId`	Trace context for tracking across all downstream actions
`runId`	Factory execution ID (batch or orchestrated run)
`version`	SemVer-compatible version
`status`	Enum: `Draft`, `Validated`, `Used`, `Invalid`, `Archived`
`createdAt`	UTC timestamp of creation
`updatedAt`	UTC timestamp of last modification

🔍 3. DSL and Artifact References¶

Blueprints maintain direct references to both inputs and outputs:

dsl:
  - ref: dsls/entities/Appointment.yaml
  - ref: dsls/events/AppointmentBooked.yaml

artifacts:
  - ref: artifacts/services/BookingService.cs
  - ref: artifacts/bicep/booking-queue.bicep
  - ref: artifacts/tests/booking.feature

These references support:

🔄 Artifact regeneration on DSL change
🧭 Full Prompt → DSL → Blueprint → Artifact lineage
🔎 Smart diffing and version navigation

🧠 4. Semantic Embedding and Memory Indexing¶

Every blueprint is embedded into the factory’s vector-based memory graph:

Memory Layer	What’s Stored	Purpose
Short-Term Memory	Contextual use during current run	Prompt-to-agent-to-output linkage
Long-Term Memory	Embedded semantic vectors	Similarity, reuse, memory recall
Persistent Memory	Versioned YAML and metadata	Audit, rollback, archiving

Blueprint trace IDs are also linked to events, logs, prompts, test results, and documentation.

📡 5. Traceability Diagram¶

flowchart TD
    P[📄 Prompt: "Add Booking Feature"]
    D1[🧾 DSL: AppointmentBookedEvent]
    D2[🧾 DSL: AppointmentEntity]
    B[🧩 booking-service-blueprint.yaml]
    A1[🛠️ BookingService.cs]
    A2[📑 booking.feature]
    A3[🔧 booking.bicep]

    P --> B
    D1 --> B
    D2 --> B
    B --> A1
    B --> A2
    B --> A3

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🧷 6. Events and Trace Hooks¶

Blueprints inject trace data into the observability layer via:

traceId propagation into:
- Logs (LogContext.TraceId)
- Events (EventGrid.Metadata)
- Test spans (OpenTelemetry.SpanId)
Event mesh metadata headers:
- X-Blueprint-Id
- X-Agent-Name
- X-DSL-Hash

This allows for full-stack traceability across platform telemetry.

✅ Summary¶

Every blueprint is tagged, versioned, and traceable across the factory.
DSLs and artifacts are explicitly linked for auditing and regeneration.
Trace IDs connect blueprints to events, logs, test runs, and final outputs.
Semantic memory enables intelligent reuse and similarity-based navigation.

🧩 Blueprint Memory Indexing and Retrieval¶

Every blueprint generated within the AI Software Factory is embedded and indexed in multiple memory layers — enabling retrieval, reuse, regeneration, and similarity-based orchestration.

This memory system ensures that blueprints aren't lost or static — they are continuously discoverable, learnable, and updatable based on historical traces.

🧠 1. Multi-Layer Memory Model¶

Blueprints are indexed in three coordinated memory layers:

Memory Layer	Scope	Storage	Purpose
Short-Term	Current agent run	In-context memory	Used to resolve prompts & generate output
Long-Term	Cross-agent semantic graph	Vector DB (e.g., Azure Cognitive Search, Redis, Qdrant)	Recall similar blueprints or DSLs
Persistent	Immutable file/archive	Blob storage + Git-compatible YAML	Versioned audit log, replay, governance

🔎 2. Semantic Embedding of Blueprints¶

Each blueprint is semantically embedded into vector memory using a transformer model (e.g., text-embedding-3-large or openai/azure-embedding) with fields like:

title, purpose, dsl.refs, outputs.project, agent, traceId, and artifact paths

These embeddings power:

🔁 Reuse detection ("Find similar service blueprint")
🧬 Merge suggestions ("Combine two booking flows")
🔍 Memory-driven prompting ("You already built a similar test for 'UserRegistered'")

🧭 3. Blueprint Retrieval Interfaces¶

Interface Type	Purpose	Used By
`MemorySkill.SearchBlueprints(query)`	Semantic similarity query	Planner, Vision, Refiner Agents
`BlueprintMemory.GetById(blueprintId)`	Exact blueprint fetch	Generator and DevOps Agents
`MemorySkill.ListByTag(tag: Booking)`	Return all tagged blueprints	Studio UI, AutoDocs Agent
`BlueprintHistory.FindByTrace(traceId)`	Find all versions linked to a trace run	Validator, QA Agent

📘 4. Example: Vectorized Blueprint¶

{
  "blueprintId": "booking-service-v1",
  "type": "Service",
  "agent": "ProductManager",
  "dsl.refs": [
    "dsls/entities/Appointment.yaml",
    "dsls/events/AppointmentBooked.yaml"
  ],
  "embedding": [0.013, -0.144, 0.251, ...]
}

This record is stored in a vector index alongside other structured fields.

🕸️ 5. Blueprint Memory Graph¶

Each blueprint is also linked into a traceable memory graph for reasoning and planning:

graph TD
    Prompt["🧠 Prompt: Add Booking Flow"]
    VisionBP["🧩 Vision Blueprint"]
    ServiceBP["🧩 Service Blueprint"]
    TestBP["🧩 Test Blueprint"]
    Artifact1["📄 booking.feature"]
    Artifact2["🛠️ BookAppointmentHandler.cs"]

    Prompt --> VisionBP --> ServiceBP --> TestBP
    TestBP --> Artifact1
    ServiceBP --> Artifact2

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This enables contextual reasoning for agents like:

🧪 "Did we test all flows from the booking blueprint?"
🔄 "Which blueprints were affected when Appointment DSL changed?"
🧠 "Have we built something like this before?"

🔁 6. Memory-Driven Blueprint Reuse¶

Agents can auto-suggest or copy patterns from similar blueprints:

“You’ve already built a blueprint for PrescriptionService. Do you want to reuse its event structure for ReminderService?”

This mechanism makes the factory progressively smarter over time.

✅ Summary¶

Blueprints are indexed in multi-layer memory for intelligent retrieval and orchestration.
Semantic vectors enable fuzzy matching, reuse, and similarity detection.
The memory system is fully trace-linked, powering traceability and observability.
Retrieval is available via agents, orchestration flows, and Studio UI.

🧩 Agents That Use and Produce Blueprints¶

In the AI Software Factory, blueprints are the core shared language between intelligent agents. They are:

🛠️ Produced by upstream planning agents (vision, product, architecture)
📥 Consumed by downstream generation agents (engineering, QA, DevOps, documentation)
🔁 Refined by agents in the feedback loop (test, runtime, observability)

🧠 1. Blueprint as the Core Artifact Exchange Format¶

Instead of relying on plain prompts or raw YAML snippets, ConnectSoft agents collaborate through structured, versioned blueprints.

Blueprints encapsulate:

DSL references (semantic)
Purpose and traceability
Input/output expectations
Agent/system context
Memory hooks and trace metadata

🤖 2. Which Agents Produce Blueprints?¶

Agent Type	Blueprint Types Produced
Vision Architect Agent	`VisionBlueprint` (modules, personas, flows)
Product Manager Agent	`ServiceBlueprint`, `EntityBlueprint`
Enterprise Architect	`InfrastructureBlueprint`, `SecurityBlueprint`
QA Engineer Agent	`TestBlueprint` (BDD, test matrix)
DevOps Agent	`PipelineBlueprint`, `DeploymentBlueprint`
Documentation Agent	`DocumentationBlueprint` from services/modules

These agents emit BlueprintCreated events and store generated YAML files into memory, blob, or Git.

🔁 3. Which Agents Consume Blueprints?¶

Agent Type	Blueprint Types Consumed
Software Engineer Agent	`ServiceBlueprint`, `ModuleBlueprint`
Test Automation Agent	`TestBlueprint`, `ServiceBlueprint`
Load & Performance Agent	`TestBlueprint`, `InfraBlueprint`
Resiliency & Chaos Agent	`SecurityBlueprint`, `PipelineBlueprint`
Feedback Loop Agent	All types (to refine or regenerate)
Code Reviewer Agent	Service blueprints to validate design vs code

These consumers often emit:

BlueprintUsed
BlueprintValidated
BlueprintUpdated

🔄 4. Agent-to-Agent Blueprint Flow¶

flowchart LR
    Prompt["📄 Prompt"]
    Vision["🧠 Vision Architect Agent"]
    Product["📋 Product Manager Agent"]
    Architect["🧱 Enterprise Architect Agent"]
    QA["🧪 QA Engineer Agent"]
    Engineer["🧑‍💻 Software Engineer Agent"]
    DevOps["🚀 DevOps Agent"]

    Prompt --> Vision --> Product --> Architect --> Engineer
    Product --> QA
    Architect --> DevOps

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📤 Each stage emits a blueprint
📥 Downstream agents consume, validate, or evolve the blueprint

🔂 5. Multi-Agent Blueprint Collaboration Patterns¶

Pattern	Description
Fan-out	One blueprint feeds multiple agents (`ServiceBlueprint → Engineer + QA`)
Cascade	Each blueprint evolves into the next (`Vision → Service → Infra`)
Feedback Loop	Agents suggest improvements or corrections (`TestAgent → ProductManager`)
Branch per Edition	Blueprints are forked and scoped to tenant or edition variants

🧬 6. Agent-Blueprint Registry¶

All agent/blueprint interactions are logged to a registry for:

🔍 Studio inspection
📊 Observability dashboards
🔁 Feedback loops and auto-repair
🧠 Meta-learning of agent effectiveness

Example registry row:

{
  "agent": "ProductManager",
  "blueprintId": "booking-service-v2",
  "producedAt": "2024-06-01T14:42:00Z",
  "traceId": "abc-123",
  "status": "Validated"
}

✅ Summary¶

Agents interact with each other through structured blueprints, not raw text.
Every blueprint has a producing and consuming agent, creating a traceable chain.
Blueprint creation, usage, and validation are observable and version-controlled.
Collaboration flows are event-driven, decoupled, and semantically rich.

🧩 Vision Blueprint¶

The Vision Blueprint is the foundational planning artifact produced by the Vision Architect Agent. It captures the high-level structure, purpose, and modular breakdown of the product or system to be generated.

This blueprint seeds the downstream generation of all other blueprints — service, test, infra, etc.

🧠 1. Purpose¶

The Vision Blueprint defines:

🧭 Product goals, business flows, and personas
🧩 System modularization into domains, services, and capabilities
🧵 Key event flows and integration boundaries
🧠 Semantic signals for AI agents (naming, scope, responsibility hints)

It answers the question:

“What should we build, for whom, and how should it be structured?”

🧬 2. Example Structure¶

blueprintId: vision-booking-system
type: Vision
agent: VisionArchitect
traceId: efd2a43b
version: 1.0.0
status: Validated

personas:
  - name: PetOwner
    goals:
      - Book appointments
      - Get reminders
  - name: ClinicAdmin
    goals:
      - Manage scheduling
      - Review patient history

modules:
  - name: BookingService
    purpose: Handle new appointment creation
    events:
      - emits: AppointmentBooked
      - listensTo: UserRegistered
  - name: ReminderService
    purpose: Send notifications
    events:
      - emits: ReminderSent
      - listensTo: AppointmentBooked
  - name: UserService
    purpose: Register and manage users

domain: VetClinic
businessGoal: Enable self-service appointment booking with reminders

🔎 3. DSLs Referenced or Embedded¶

DSL Type	Purpose
`ModuleDSL`	Defines high-level service modules
`EventDSL`	Key events in the system (`AppointmentBooked`)
`PersonaDSL`	End-user goals, journeys, and actors

Blueprints can either:

Embed small DSL fragments directly
Or reference them externally via ref: dsls/...

🧠 4. Agent Usage¶

Agent	Usage
Product Manager Agent	Uses module definitions to generate `ServiceBlueprints`
Enterprise Architect	Uses event flow to model infrastructure
QA Agent	Uses `events` and `personas` to generate test coverage
Feedback Loop Agent	Links test feedback to originating goals or modules

🧭 5. Vision Blueprint Diagram¶

graph TD
    VA[🎯 Vision Architect Agent]
    VB[🧩 Vision Blueprint]
    PM[📋 Product Manager Agent]
    EA[🧱 Enterprise Architect Agent]
    QA[🧪 QA Agent]

    VA --> VB
    VB --> PM
    VB --> EA
    VB --> QA

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The Vision Blueprint may be refined when:

💬 Requirements change
✨ New modules are added
🔍 QA discovers missing scenarios
🧠 Similar blueprints are detected and merged

The updated blueprint emits BlueprintUpdated events, and downstream agents are re-triggered with new versioned inputs.

✅ Summary¶

The Vision Blueprint defines the high-level intent and modular scope of the product.
It contains personas, modules, and event flows that seed downstream generation.
It’s produced by the Vision Architect Agent and consumed by multiple downstream agents.
It supports embedding or referencing DSLs for persona, module, and event design.

🧩 Service Blueprint¶

The Service Blueprint defines the structure, responsibilities, and interaction surfaces of a specific microservice or backend module. It is produced by the Product Manager Agent, based on the upstream Vision Blueprint and DSL references.

This blueprint drives the generation of handler classes, commands, events, REST/gRPC APIs, contracts, and configuration layers.

🧠 1. Purpose¶

The Service Blueprint answers:

🛠️ What is this service responsible for?
📬 What commands and events does it handle?
📡 What APIs does it expose?
🧱 What entities does it manage internally?

It guides the creation of production-grade, modular, agent-compatible services.

🧬 2. Example Structure¶

blueprintId: booking-service-v1
type: Service
agent: ProductManager
version: 1.0.0
traceId: svc-9fae23ab

dsl:
  - ref: dsls/entities/Appointment.yaml
  - ref: dsls/events/AppointmentBooked.yaml
  - ref: dsls/commands/BookAppointment.yaml

responsibility: Handle all appointment scheduling
boundedContext: Scheduling
api:
  - POST /appointments → BookAppointment
  - GET /appointments/{id} → GetAppointment

handlers:
  - BookAppointmentCommand → AppointmentBooked
  - CancelAppointmentCommand → AppointmentCancelled

storage:
  entity: Appointment
  projections:
    - UpcomingAppointments
    - AppointmentsByUser

🔎 3. DSLs Referenced¶

DSL Type	Description
`EntityDSL`	Defines aggregates like `Appointment`
`CommandDSL`	Incoming actions (e.g., `BookAppointment`)
`EventDSL`	Outgoing domain events
`ApiDSL`	REST or gRPC interface definition

These DSLs allow the blueprint to remain lightweight while reusing validated domain logic.

🧠 4. Agent Usage¶

Agent	Action
Software Engineer Agent	Generates actual service code (handlers, contracts, infra)
Test Automation Agent	Generates endpoint and command tests
DevOps Agent	Uses handler map to emit health checks, routes, policies
Documentation Agent	Produces API reference pages and architecture docs

🔁 5. Collaboration Flow¶

graph TD
    PM[📋 Product Manager Agent]
    SB[🧩 Service Blueprint]
    ENG[👨‍💻 Software Engineer Agent]
    QA[🧪 Test Agent]
    DOC[📘 Doc Agent]
    OPS[🚀 DevOps Agent]

    PM --> SB
    SB --> ENG
    SB --> QA
    SB --> DOC
    SB --> OPS

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🧰 6. Output Examples (Generated Artifacts)¶

Output Type	Filename
Command handler	`BookAppointmentHandler.cs`
Domain entity	`Appointment.cs`
Event class	`AppointmentBooked.cs`
API controller	`AppointmentController.cs`
Tests	`appointment-booking.feature`, `AppointmentTests.cs`
OpenAPI Spec	`booking-api.yaml`
Infrastructure	`booking-service.bicep`

These artifacts inherit trace IDs and metadata from the blueprint for observability and traceability.

✅ Summary¶

The Service Blueprint defines a complete backend service module.
It references entities, commands, events, and APIs via DSLs.
It orchestrates handler logic, API surface, projections, and tests.
It’s generated by the Product Manager Agent and consumed by Engineers, QA, Docs, and DevOps agents.

🧩 Module Blueprint¶

The Module Blueprint defines a focused functional unit or adapter within a larger service or frontend application. It can represent:

Internal components (e.g., logging module, email adapter)
External integrations (e.g., Stripe adapter, Kafka publisher)
UI feature modules (e.g., BookingCalendar UI component)

It is typically emitted by agents like the Software Engineer Agent, Frontend Developer Agent, or Platform Adapter Agent.

🧠 1. Purpose¶

Module Blueprints allow for:

🧱 Explicit modeling of reusable units inside a service or app
🧩 Composability and isolation of logic (e.g., auth, payments)
🔁 Reuse across services (e.g., NotificationModule)
🧪 Targeted test generation and instrumentation

They serve as a fine-grained blueprint layer underneath or alongside Service Blueprints.

🧬 2. Example Structure¶

blueprintId: notification-module-v1
type: Module
agent: SoftwareEngineer
traceId: mod-41cf8b12
version: 1.0.1

dsl:
  - ref: dsls/integration/SendGridAdapter.yaml
  - ref: dsls/events/ReminderSent.yaml

purpose: Send email reminders for appointments
dependsOn:
  - AuthModule
  - EventBusClient

outputs:
  - EmailSender.cs
  - NotificationController.cs
  - NotificationSent event

runtime:
  language: C#
  mode: background worker
  retryPolicy: exponential backoff

🔎 3. DSLs Referenced¶

DSL Type	Description
`AdapterDSL`	Defines integration boundary (e.g., SMTP)
`EventDSL`	Emitted or consumed events
`RetryPolicyDSL`	Describes reliability semantics
`UIComponentDSL`	For frontend modules (optional)

🧠 4. Agent Usage¶

Agent	Action
Software Engineer Agent	Implements internal module logic
Adapter Generator Agent	Produces external connector logic
Frontend Developer Agent	Maps to UI widgets, reactive components
QA Agent	Generates unit/integration tests for the module
Observability Agent	Injects tracing, metrics, logs into module template

🧩 5. Relationship to Other Blueprints¶

Relation	How it connects
`ServiceBlueprint`	A module is a unit within a service
`InfrastructureBlueprint`	May require config (e.g., email provider setup)
`TestBlueprint`	Used to define test boundaries for specific unit
`PipelineBlueprint`	Can be deployed independently (e.g., worker)

🧪 6. Example Outputs¶

Output Artifact	File Name
Adapter Implementation	`SendGridAdapter.cs`
Handler or Worker	`ReminderEmailWorker.cs`
Event Contract	`ReminderSent.cs`
UI Component (if UI)	`BookingCalendarComponent.tsx`
Module Config	`notification.module.config.yaml`

🧭 7. Blueprint Fan-out Diagram¶

graph TD
    SB[🧩 Service Blueprint]
    MB1[🧩 Notification Module]
    MB2[🧩 Auth Module]
    MB3[🧩 Retry Policy Module]
    ENG[👨‍💻 Engineer Agent]

    SB --> MB1
    SB --> MB2
    SB --> MB3
    MB1 --> ENG
    MB2 --> ENG
    MB3 --> ENG

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

✅ Summary¶

Module Blueprints define scoped, reusable, testable software units.
They support both backend and frontend components (e.g., adapters, widgets).
DSLs used include adapters, events, UI components, retry policies, etc.
They are essential for micro-composability, reusability, and isolation within the factory.

🧩 Test Blueprint¶

The Test Blueprint defines structured, agent-readable test plans for verifying the correctness, coverage, and resiliency of generated components. It is typically produced by the QA Engineer Agent, Test Automation Agent, or indirectly derived from Service and Module Blueprints.

It ensures that all business logic, contracts, and flows are validatable, observable, and traceable via generated test cases.

🧠 1. Purpose¶

The Test Blueprint answers:

✅ What should we test?
🧪 How should we test it?
🎯 What are the acceptance criteria or edge cases?
🔄 What happens under load, failure, or retries?

It drives the generation of both automated tests and test monitoring hooks.

🧬 2. Example Structure¶

blueprintId: booking-service-tests-v1
type: Test
agent: QAEngineer
version: 1.0.0
traceId: test-93df8ea9

dsl:
  - ref: dsls/events/AppointmentBooked.yaml
  - ref: dsls/commands/BookAppointment.yaml
  - ref: dsls/entities/Appointment.yaml

scenarios:
  - title: Book appointment successfully
    type: happy-path
    steps:
      - Given user is authenticated
      - When user sends BookAppointment command
      - Then AppointmentBooked event is emitted
      - And appointment is persisted in DB

  - title: Double-booking prevention
    type: edge-case
    steps:
      - Given appointment already exists
      - When another BookAppointment is sent
      - Then a validation error is returned
      - And no duplicate event is emitted

coverageMatrix:
  - feature: Booking
    commands: [BookAppointment]
    events: [AppointmentBooked, AppointmentRejected]
    testCases: [happy, invalid, concurrency, load]

testFormats:
  - type: BDD
    output: `appointment-booking.feature`
  - type: Integration
    output: `BookingFlowTests.cs`

🔎 3. DSLs Referenced¶

DSL Type	Description
`CommandDSL`	Specifies what actions are being tested
`EventDSL`	Validates that correct outputs are emitted
`EntityDSL`	Used for setup, assertions, and projections
`ErrorDSL`	Maps expected exceptions or validation scenarios

🧠 4. Agent Usage¶

Agent	Action
Test Automation Agent	Generates BDD or integration test files
QA Engineer Agent	Asserts coverage and completeness
Load & Chaos Agents	Extend scenarios for performance or fault-injection
Code Reviewer Agent	Verifies that critical paths are covered

🧩 5. Collaboration Example¶

flowchart TD
    SB[🧩 Service Blueprint]
    TB[🧩 Test Blueprint]
    QA[🧪 QA Engineer Agent]
    TA[🤖 Test Automation Agent]
    LPA[🔥 Load Test Agent]

    SB --> TB
    TB --> QA
    TB --> TA
    TB --> LPA

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🧪 6. Example Outputs¶

Output Type	Filename
BDD Feature File	`appointment-booking.feature`
Integration Tests	`AppointmentTests.cs`
Test Runner Config	`xunit.runner.json`, `jest.config.js`
Load Test Plan	`locust_booking_test.py`
Resiliency Profile	`booking-chaos-profile.yaml`

📈 7. Coverage and Feedback Loops¶

Test Blueprints integrate with observability:

Export test results as spans with trace ID
Emit BlueprintValidated, TestPassed, or TestFailed events
Trigger blueprint regeneration on assertion or coverage gap
Store feedback for next test planning cycle

✅ Summary¶

Test Blueprints define structured, DSL-driven testing plans.
They support BDD, integration, chaos, and performance testing.
Generated from commands, events, and service responsibilities.
They ensure factory outputs are verifiable, traceable, and auditable.

🧩 Infrastructure Blueprint¶

The Infrastructure Blueprint defines the cloud-native, environment-specific resources and configurations required to support the deployment, communication, and operation of a service or system. It is produced by the Enterprise Architect Agent, and later consumed by DevOps, Security, and Resiliency Agents.

It ensures that infrastructure is declarative, reproducible, and event-aligned — and matches the design requirements in the Vision and Service Blueprints.

🧠 1. Purpose¶

The Infrastructure Blueprint answers:

☁️ What cloud resources are needed for this module or service?
🔐 What configurations and access rules must be enforced?
🔄 How do we wire event-driven components and messaging systems?
🏗️ Which environments and deployment modes are supported?

It drives the generation of IaC templates, deployment configs, and environment-specific overlays.

🧬 2. Example Structure¶

blueprintId: infra-booking-service-v1
type: Infrastructure
agent: EnterpriseArchitect
version: 1.0.0
traceId: infra-23fe88c2

dsl:
  - ref: dsls/queues/AppointmentQueue.yaml
  - ref: dsls/secrets/SendGridKey.yaml
  - ref: dsls/k8s/deployment-strategy.yaml

resources:
  - type: Queue
    name: appointment-queue
    provider: AzureServiceBus
    access: BookingService, ReminderService

  - type: Secret
    name: SENDGRID_API_KEY
    scope: BookingService
    rotate: true

  - type: Deployment
    target: Kubernetes
    replicas: 3
    autoscaling: enabled
    strategy: rolling
    probes:
      - readiness: /healthz
      - liveness: /healthz

environments:
  - name: dev
    suffix: -dev
    replicas: 1
  - name: prod
    suffix: ""
    replicas: 3

🔎 3. DSLs Referenced¶

DSL Type	Purpose
`QueueDSL`	Defines event queues and routing
`SecretDSL`	Defines secrets, credentials, rotation rules
`K8sStrategyDSL`	Describes deployment strategy and autoscaling
`EnvDSL`	Captures environment-specific config

🧠 4. Agent Usage¶

Agent	Usage
DevOps Agent	Generates `.bicep`, `.yaml`, `.tf`, or `arm.json`
Security Agent	Validates secrets, scopes, RBAC policies
Observability Agent	Adds dashboards, logging volumes, metrics exporters
Resiliency Agent	Extends strategy with chaos injection & retry configs

🧩 5. Cross-Blueprint Relationships¶

Source Blueprint	Usage in Infra Blueprint
`ServiceBlueprint`	Defines service to provision for
`EventDSL`	Mapped to queue and topic definitions
`PipelineBlueprint`	Uses infra resources as targets and outputs
`SecurityBlueprint`	Validates that proper network and secret boundaries exist

⚙️ 6. Sample Output Artifacts¶

Type	File
Azure Bicep	`booking-service.bicep`
Kubernetes YAML	`booking-deployment.yaml`
Terraform	`main.tf`
Helm Chart	`helm/booking-service/values.yaml`
Secret Manager Config	`sendgrid-key.config.json`

📡 7. Observability Integration¶

Infra Blueprints may include:

Probes (readiness, liveness)
Prometheus annotations
Distributed tracing injectors
Alert rule definitions
Cost visibility tags

These are used by Observability and DevOps agents for dashboards and alerting.

✅ Summary¶

Infrastructure Blueprints define all cloud-native resources needed for a service.
They reference secrets, queues, deployment strategies, and environments via DSLs.
They produce standardized IaC artifacts, Helm charts, and secrets configurations.
They are consumed by DevOps, Security, and Observability agents for full-lifecycle support.

🧩 Security Blueprint¶

The Security Blueprint defines the access controls, trust boundaries, scopes, secrets, and attack surfaces for a service or system. It is generated by the Enterprise Architect Agent or the Security Analyst Agent, based on inputs from Vision, Service, and Infra Blueprints.

It ensures that every blueprint-generated system follows Security-First design principles — with proactive controls, validations, and audits embedded by design.

🧠 1. Purpose¶

The Security Blueprint answers:

🔐 What data is sensitive?
🧭 What are the internal vs external boundaries?
🗝️ What secrets or credentials are needed?
🛡️ What authentication and authorization models are used?
🔍 What attack surfaces must be tested and monitored?

It drives security middleware, role definitions, token policies, and span-level observability.

🧬 2. Example Structure¶

blueprintId: security-booking-service-v1
type: Security
agent: SecurityAnalyst
version: 1.0.0
traceId: sec-71cbf13a

dsl:
  - ref: dsls/secrets/SendGridKey.yaml
  - ref: dsls/roles/User.yaml
  - ref: dsls/trust-boundaries/booking.yaml

trustBoundaries:
  - context: BookingService
    type: internal
  - context: AppointmentAPI
    type: external
    requires: AuthToken

authentication:
  provider: OpenIddict
  requiredScopes:
    - booking:read
    - booking:write

authorization:
  policies:
    - role: ClinicAdmin
      access: full
    - role: PetOwner
      access: own-appointments

secrets:
  - name: SENDGRID_API_KEY
    usage: email-sending
    rotation: monthly
    owner: BookingService

attackSurfaces:
  - path: /appointments
    type: public-api
    test: enabled

🔎 3. DSLs Referenced¶

DSL Type	Description
`TrustBoundaryDSL`	Defines internal/external service boundaries
`SecretDSL`	Specifies usage and lifecycle of credentials
`RoleDSL`	Role-based permissions and mappings
`ScopeDSL`	OAuth2 scopes and token design

🧠 4. Agent Usage¶

Agent	Action
Code Generator Agent	Injects `[Authorize]` filters, policies, and scopes
DevOps Agent	Ensures secrets are wired and scoped to correct service only
Observability Agent	Adds security spans to tracing and logs
QA & Test Agent	Generates attack surface penetration tests
Documentation Agent	Emits public trust boundary diagrams

🛡️ 5. Security Blueprint Diagrams¶

flowchart TD
    A[🌐 External Client]
    B[🔐 API Gateway]
    C[🔧 BookingService]
    D[🔑 OpenIddict]
    E[📦 Secret Store]

    A -->|AuthToken| B --> C
    C --> E
    B --> D

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This represents a trust boundary-aware path from a public request through secured service layers.

🔁 6. Compliance and Observability Integration¶

Security Blueprints automatically emit:

SecurityPolicyPublished events
SecurityViolationDetected hooks (optional)
Changelog.md if policies change over time
SpanTemplates for:
unauthorized_access
token_expired
invalid_scope

✅ Summary¶

Security Blueprints model trust boundaries, roles, scopes, secrets, and threats.
They are generated from and reinforce Vision, Service, and Infra Blueprints.
Outputs feed directly into middleware, policies, monitoring, and secrets wiring.
They provide traceable, auditable security overlays for every agent-generated service.

🧩 Pipeline Blueprint¶

The Pipeline Blueprint defines the end-to-end CI/CD automation, build strategy, and release workflows for a specific service, module, or entire product line. It is produced by the DevOps Agent, based on Service, Test, Infra, and Security Blueprints.

It ensures every component generated by the AI Factory is deployable, testable, and promotable via observable, idempotent pipelines.

🧠 1. Purpose¶

The Pipeline Blueprint answers:

🧪 How do we test and validate this component automatically?
🚀 What is the deployment path per environment?
🧩 What are the pipeline stages, triggers, and conditions?
🛡️ How do we integrate with secrets, approvals, and observability?

It drives YAML-based pipelines, Git hooks, and promotion flows across dev → staging → prod.

🧬 2. Example Structure¶

blueprintId: pipeline-booking-service-v1
type: Pipeline
agent: DevOpsAgent
version: 1.0.0
traceId: pipe-8f21cb77

dsl:
  - ref: dsls/pipeline-steps/build-test.yaml
  - ref: dsls/deployments/k8s-deploy.yaml
  - ref: dsls/env/dev.yaml
  - ref: dsls/env/prod.yaml

stages:
  - name: build
    runsOn: ubuntu-latest
    steps:
      - dotnet restore
      - dotnet build
      - dotnet test

  - name: quality-gate
    steps:
      - sonar scan
      - code coverage check

  - name: deploy-dev
    environment: dev
    approvals: none
    steps:
      - helm upgrade
      - run smoke tests

  - name: deploy-prod
    environment: prod
    approvals:
      - required: true
    steps:
      - helm upgrade --values=env/prod.yaml
      - notify ops team

artifacts:
  - build: `booking-service.zip`
  - publish: `container registry + Helm chart`

🔎 3. DSLs Referenced¶

DSL Type	Description
`PipelineStepDSL`	Defines individual reusable pipeline steps
`EnvironmentDSL`	Describes per-env differences
`DeploymentDSL`	Encapsulates platform-specific deploy logic
`ApprovalDSL`	Workflow gates and approvers (optional)

🧠 4. Agent Usage¶

Agent	Purpose
DevOps Agent	Generates `azure-pipelines.yaml`, GitHub Actions
QA/Test Agents	Inject test stages and coverage checks
Observability Agent	Adds logging, span IDs, dashboard hooks
Security Agent	Adds secrets, RBAC scopes, and approval workflows

🛠️ 5. Sample Output Files¶

Output Type	File Name
Azure Pipelines	`azure-pipelines.yaml`
GitHub Actions	`.github/workflows/build.yaml`
K8s Job YAML	`job-booking-deploy.yaml`
Helm Release Step	`helm-release-step.yaml`
Deployment Diagram	`pipeline-flow.mmd`

🔁 6. Traceability and Observability Integration¶

Each stage emits events or metrics:

Hook	Emitted To
`PipelineStarted`	Event Grid + Dashboard + Trace Store
`PipelineStepCompleted`	OpenTelemetry, traceId enriched logs
`PipelineFailed`	Notification mesh + retry orchestrator
`ArtifactPublished`	Registry metadata with blueprint ID

These ensure pipelines are observable and self-healing.

📡 7. CI/CD Flow Diagram¶

flowchart TD
    Commit[📝 Code Commit]
    Build[🏗️ Build Stage]
    Test[✅ Test Stage]
    Dev[🚀 Deploy Dev]
    Prod[🚀 Deploy Prod]

    Commit --> Build --> Test --> Dev --> Prod

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Each stage is blueprint-aware and can be traced via traceId, runId, and artifactId.

✅ Summary¶

Pipeline Blueprints define the full CI/CD automation lifecycle for generated services.
They reference reusable DSLs for steps, approvals, environments, and deployment logic.
They produce observable YAML pipelines and emit events for orchestration feedback loops.
They ensure that every artifact is testable, deployable, and traceable from source to prod.

🧩 Documentation Blueprint¶

The Documentation Blueprint defines the structured, contextual documentation required for a service, module, UI component, or entire system. It is generated by the Documentation Agent, often based on Service, Vision, and Test Blueprints.

This blueprint ensures that generated software is also self-explaining, navigable, and onboarding-friendly — supporting both internal developers and external users.

🧠 1. Purpose¶

The Documentation Blueprint answers:

📖 What should we explain and to whom?
🔎 Where are the entry points for understanding this system?
🧩 How do we organize API, domain, and architectural knowledge?
🧪 Can we explain decisions, variations, and system behaviors?

It creates navigable, structured markdown docs and links to diagrams, DSLs, and blueprints.

🧬 2. Example Structure¶

blueprintId: docs-booking-service-v1
type: Documentation
agent: DocumentationAgent
version: 1.0.0
traceId: doc-91ceabd3

dsl:
  - ref: dsls/entities/Appointment.yaml
  - ref: dsls/events/AppointmentBooked.yaml
  - ref: dsls/api/booking.yaml

sections:
  - title: Overview
    file: `docs/booking-service/index.md`
    includes:
      - businessGoal
      - personaMap
      - contextDiagram

  - title: Domain Model
    file: `docs/booking-service/domain.md`
    includes:
      - entityList
      - aggregateRoots
      - eventList

  - title: API Reference
    file: `docs/booking-service/api.md`
    source: OpenAPI
    format: Markdown + Mermaid

  - title: Test Scenarios
    file: `docs/booking-service/tests.md`
    source: TestBlueprint
    format: BDD Table

  - title: Deployment & Infra
    file: `docs/booking-service/infra.md`
    includes:
      - Helm chart overview
      - pipeline link
      - K8s deploy config

🔎 3. DSLs Referenced¶

DSL Type	Purpose
`EntityDSL`	Documents domain models
`EventDSL`	Explains event contracts and triggers
`ApiDSL`	Generates OpenAPI or gRPC docs
`PersonaDSL`	Embedded into overview and use cases
`BlueprintRef`	Cross-links Vision, Test, Infra blueprints

🧠 4. Agent Usage¶

Agent	Action
Documentation Agent	Generates docs, diagrams, and navigation structure
Product Owner Agent	Validates onboarding and user journey clarity
QA/Test Agent	Inserts test flow visualizations and outcome maps
Studio UI	Renders preview and cross-referenced doc portal

📄 5. Output Artifacts¶

Output File	Description
`docs/index.md`	Overview, scope, personas
`docs/domain.md`	Entity-event diagrams
`docs/api.md`	REST/gRPC reference
`docs/tests.md`	Scenario summaries and examples
`docs/infra.md`	Infra structure, endpoints, probes
`docs/images/context.mmd`	Mermaid diagram source
`mkdocs.yml`	Injected nav tree and metadata

🧭 6. Rendering Integration (Markdown to Portal)¶

Documentation Blueprints integrate with:

MkDocs with Material theme (internal developer portals)
Swagger/OpenAPI UIs (API surfacing)
Mermaid for sequence, flow, context diagrams
AI indexers (e.g., docs/ folder is embedded in vector memory)

🔄 7. Live Documentation Flow Diagram¶

flowchart TD
    SB[🧩 Service Blueprint]
    TB[🧩 Test Blueprint]
    AB[🧩 API DSL]
    DB[🧩 Documentation Blueprint]
    DOC[📄 Markdown Pages]

    SB --> DB
    TB --> DB
    AB --> DB
    DB --> DOC

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✅ Summary¶

Documentation Blueprints structure all human-readable outputs of the Factory.
They integrate DSLs, blueprints, and metadata into usable markdown or web outputs.
They support onboarding, auditing, collaboration, and platform discoverability.
They are automatically updated when upstream inputs change — always trace-linked.

🧩 Event Flow Blueprint¶

The Event Flow Blueprint defines the end-to-end message-driven behavior across services, modules, and external systems. It is created by the Enterprise Architect Agent or Solution Architect Agent, based on the Vision, Service, and Module Blueprints.

This blueprint is essential for enabling event-driven, decoupled systems and aligns with the platform’s Event-Driven Mindset.

🧠 1. Purpose¶

The Event Flow Blueprint answers:

📡 Which events are emitted, listened to, or transformed?
🔄 What is the event lifecycle across the system?
🔗 Which services or modules are coupled via events?
🚨 Where do retries, dead-letter queues, or compensations apply?

It provides a formal map of the semantic event backbone of a product or domain.

🧬 2. Example Structure¶

blueprintId: event-flow-booking-v1
type: EventFlow
agent: EnterpriseArchitect
version: 1.0.0
traceId: evtflow-7a1b9b3a

events:
  - name: AppointmentBooked
    publisher: BookingService
    subscribers:
      - ReminderService
      - AnalyticsService

  - name: ReminderSent
    publisher: ReminderService
    subscribers:
      - LoggingService

  - name: AppointmentCancelled
    publisher: BookingService
    subscribers:
      - NotificationService
      - ReminderService

bus:
  provider: AzureServiceBus
  retryPolicy: exponential
  deadLetterQueue: enabled

topologies:
  - type: topic
    name: appointments
    partitioning: by location
    routes:
      - event: AppointmentBooked
        path: `appointments/{region}`

🔎 3. DSLs Referenced¶

DSL Type	Description
`EventDSL`	Defines schema and metadata for each event
`TopicDSL`	Specifies messaging patterns and bus topology
`RetryPolicyDSL`	Describes event delivery behavior
`DeadLetterDSL`	Optional dead-letter queue definitions

🧠 4. Agent Usage¶

Agent	Action
Service Engineer Agent	Uses to wire publisher/subscriber handlers
Infra/DevOps Agent	Creates queue/topic infrastructure
QA/Test Agent	Builds event flow test cases
Observability Agent	Configures distributed tracing across event hops
Chaos/Resiliency Agent	Validates message loss, retry, duplicate handling

🔁 5. Traceability and Replayability¶

Each event includes:
traceId
correlationId
spanId
originService
Enables distributed tracing of cross-service event flow
Blueprint can emit EventFlowViolationDetected if expected subscriber is missing

🔀 6. Event Flow Diagram¶

graph TD
    Booking[📦 BookingService]
    Reminder[🔔 ReminderService]
    Analytics[📊 AnalyticsService]
    Notification[📧 NotificationService]

    Booking -->|AppointmentBooked| Reminder
    Booking -->|AppointmentBooked| Analytics
    Booking -->|AppointmentCancelled| Notification
    Booking -->|AppointmentCancelled| Reminder
    Reminder -->|ReminderSent| Logging

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📄 7. Output Artifacts¶

Type	File
Topic Definition	`appointments.topic.yaml`
Subscription Config	`booking-subscribers.yaml`
Retry Policy Config	`retry-policies.yaml`
Dead Letter Strategy	`deadletter-config.yaml`
Event Flow Diagram	`event-flow-booking.mmd`

✅ Summary¶

Event Flow Blueprints define the system-wide message choreography across microservices.
They power decoupled, observable architectures and enable event tracing and debugging.
They are generated by architectural agents and consumed by engineering, QA, and DevOps.
Outputs include routing rules, topic maps, retry policies, and visualization diagrams.

🧩 SaaS Product Blueprint¶

The SaaS Product Blueprint defines a productized, multi-tenant-aware configuration of a system composed of services, modules, capabilities, and constraints. It is produced by the Product Owner Agent and optionally extended by the Growth Strategist Agent or Tenant Modeling Agent.

This blueprint allows the factory to generate, customize, and evolve SaaS variants with edition-specific logic, entitlements, feature toggles, and business models — while reusing core components.

🧠 1. Purpose¶

The SaaS Product Blueprint answers:

🧱 Which services, features, and flows are part of this SaaS edition?
💰 What business model applies (subscription, freemium, usage-based)?
👥 Which tenants or user groups does this apply to?
🔀 What overrides or constraints differ from the default blueprint?

It formalizes product configuration-as-code for SaaS delivery at scale.

🧬 2. Example Structure¶

blueprintId: saas-booking-lite-v1
type: SaaSProduct
agent: ProductOwner
version: 1.0.0
traceId: edition-94e0cd45

baseProduct: booking-platform
edition: Lite

enabledModules:
  - BookingService
  - UserService
  - ReminderService

disabledModules:
  - AnalyticsService
  - IntegrationHub

featureToggles:
  - name: EnableManualConfirmation
    default: false

businessModel:
  type: subscription
  billingCycle: monthly
  pricePerSeat: 12.99
  freeTier: true
  usageLimits:
    appointmentsPerMonth: 50

tenantScopes:
  - name: vet-clinics-limited
    locationGroup: "Tier-2 Cities"
    branding:
      theme: "blue-silver"
      logo: "vet-lite.png"

🔎 3. DSLs Referenced¶

DSL Type	Description
`EditionDSL`	Declares which modules/features are enabled
`PricingModelDSL`	Defines billing and entitlement model
`FeatureToggleDSL`	Flags and rollout configurations
`TenantProfileDSL`	Multitenant-specific customizations

🧠 4. Agent Usage¶

Agent	Purpose
Product Owner Agent	Creates and evolves the blueprint
Business Analyst Agent	Maps edition to feature set, tenant profiles
Pricing & Growth Agents	Suggest pricing models and usage limits
Docs & Studio Agent	Renders edition documentation and matrix comparisons
Engineering Agents	Use the blueprint to inject `if edition == Lite` logic

🧩 5. Blueprint Relationships¶

Blueprint	Integration
`VisionBlueprint`	SaaS editions originate from the product’s vision
`ServiceBlueprint`	Selects which services to include per edition
`FeatureBlueprint` (TBD)	Controls fine-grained feature availability and overrides
`PricingBlueprint` (TBD)	Optional future split for pricing schema and invoice flows

🌐 6. Edition Modeling Diagram¶

flowchart TD
    P[🧭 Vision Blueprint]
    E[🧩 SaaS Product Blueprint]
    F[⚙️ Feature Toggles]
    T[👥 Tenant Profiles]
    B[💰 Billing Model]

    P --> E
    E --> F
    E --> T
    E --> B

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📄 7. Output Artifacts¶

File	Description
`edition-config-lite.yaml`	Full SaaS product blueprint
`feature-flags.json`	Toggle settings for SDK
`pricing-rules.yaml`	Monthly cost model and seat limits
`tenant-scope.json`	Tenant targeting, branding, and overrides
`edition-docs.md`	Rendered comparison table and docs

✅ Summary¶

SaaS Product Blueprints define edition-aware configurations for productized delivery.
They formalize which modules, features, and business models apply to each variant.
They enable agents to create logic and flows scoped to editions or tenants.
They drive customization, monetization, and rollout — all from a central artifact.

🧩 Cross-Blueprint Traceability Blueprint¶

The Cross-Blueprint Traceability Blueprint defines the explicit links, lineage, and references between multiple blueprints, DSLs, and generated artifacts. It is produced by the Knowledge Orchestration Agent or automatically inferred by the Studio Memory Graph Engine.

It creates a trace map for auditability, observability, explainability, and regeneration across all stages of the AI Software Factory.

🧠 1. Purpose¶

The blueprint answers:

🧬 Which blueprints or DSLs does this blueprint depend on?
🔁 What was the generating prompt, agent, or source artifact?
🔗 Which outputs (files, APIs, diagrams) did this blueprint create?
🧠 Can I trace an event, test, or class back to its design origin?

It powers studio navigation, AI memory embedding, and impact analysis.

🧬 2. Example Structure¶

blueprintId: trace-map-booking-service-v1
type: Traceability
agent: KnowledgeOrchestrator
version: 1.0.0
traceId: trace-cc84bafe

sources:
  - blueprint: vision-booking-system
  - blueprint: service-booking-service-v1
  - dsl: dsls/events/AppointmentBooked.yaml
  - input: prompt://create-booking-service

outputs:
  - file: src/BookingService/Handlers/BookAppointmentHandler.cs
  - file: tests/BookingTests.cs
  - file: docs/booking-service/api.md
  - diagram: mermaid://booking-flow.mmd
  - testCase: appointment-booking.feature

lineage:
  - from: vision-booking-system → module:BookingService
  - to: service-booking-service-v1 → handler:BookAppointment
  - to: test-blueprint-booking → scenario:DoubleBooking

🔎 3. DSLs and Inputs Referenced¶

Type	Description
`BlueprintRef`	Pointers to other blueprints (by ID or version)
`DSLRef`	DSL dependencies (commands, events, etc.)
`InputPrompt`	Prompt or seed input that led to this blueprint
`ArtifactRef`	Output artifacts linked to trace IDs

🧠 4. Agent Usage¶

Agent	Usage
Studio UI Agent	Renders graph of all trace relationships
Test & QA Agents	Trace failed test → blueprint → logic source
Observability Agent	Correlates logs → spans → blueprint context
Feedback Loop Agent	Suggests regeneration or improvement targets

🕸️ 5. Traceability Graph Diagram¶

graph TD
    Prompt["📨 Prompt"]
    Vision["📘 Vision Blueprint"]
    Service["🧩 Service Blueprint"]
    Test["🧪 Test Blueprint"]
    Docs["📄 Documentation"]
    Code["📦 Handler.cs"]
    TestFile["✅ Tests.cs"]

    Prompt --> Vision --> Service --> Code
    Service --> Test --> TestFile
    Service --> Docs

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📊 6. Observability Integration¶

Emits TraceMapCreated, ArtifactLinked, BlueprintLineageDiscovered
Powers:
Search by trace ID
Backtrace from file to blueprint
Audit logs per generated cluster

📄 7. Output Artifacts¶

Artifact	Description
`trace-map.yaml`	Full cross-blueprint reference map
`trace-index.json`	Indexed view for Studio navigation
`span-links.yaml`	Trace/Span correlation for Observability agents
`lineage-diagram.mmd`	Generated flow of origin-to-output

✅ Summary¶

The Traceability Blueprint connects all blueprints, DSLs, inputs, and artifacts.
It powers explainability, navigation, observability, and trace-based regeneration.
It is automatically built and maintained by orchestration and memory agents.
It is central to how the factory learns, audits, and evolves from past outputs.

🧩 Blueprint Observability Metadata & Span Templates¶

The Blueprint Observability Metadata defines structured telemetry annotations, span templates, metrics hooks, and trace enrichments that agents must inject into their generated components and outputs. This blueprint metadata is auto-attached to each blueprint via the Observability Agent and serves the foundation for tracing, debugging, SLO compliance, and cost visibility.

It standardizes the observability contract of every blueprint.

🧠 1. Purpose¶

It answers:

🔍 How do we observe and monitor this blueprint’s output?
📈 Which metrics, spans, logs, and traces are emitted?
🔄 How do we correlate blueprint inputs with production behavior?
🚨 What hooks exist for alerting and auto-regeneration?

This metadata makes each blueprint self-traceable, self-monitoring, and studio-indexed.

🧬 2. Example Structure¶

blueprintId: service-booking-service-v1
observability:
  traceId: svc-9fae23ab
  spanTemplates:
    - id: BookAppointment
      type: command
      tags:
        blueprintType: service
        module: BookingService
        commandName: BookAppointment
      logs:
        - onStart: "Booking started"
        - onError: "Booking failed"
        - onSuccess: "Booking completed"
      metrics:
        - name: booking_duration
          type: histogram
          unit: ms

    - id: AppointmentBooked
      type: event
      emitSpan: true
      tags:
        eventName: AppointmentBooked
        originatingService: BookingService
      links:
        - fromCommand: BookAppointment
        - toHandler: SendReminder

🔎 3. Metadata Components¶

Component	Description
`traceId`	Global trace identifier per blueprint
`spanTemplates`	Templates for OpenTelemetry spans
`tags`	Key/value pairs for span indexing and filtering
`logs`	Lifecycle log messages (start, error, success, retry)
`metrics`	Custom metrics auto-registered with blueprint context
`links`	Cross-span links to represent causality

🧠 4. Agent Usage¶

Agent	Action
Observability Agent	Injects span templates and exports to tracing systems
QA/Test Agent	Uses spans for test coverage and failure correlation
Feedback Loop Agent	Triggers retraining/regeneration based on failures
Code Review Agent	Validates missing observability contract

📈 5. Example Emitted Artifacts¶

File	Purpose
`observability-span-map.yaml`	All span templates and log templates
`otel-tags.json`	Key-value label definitions per blueprint
`metrics.yaml`	Metric definitions (latency, error rate, etc.)
`logs-template.yaml`	Structured log messages with blueprint metadata
`traceability-overlay.yaml`	Trace links across multiple blueprints

📡 6. Distributed Span View¶

sequenceDiagram
    participant Client
    participant API
    participant BookingService
    participant ReminderService

    Client->>API: BookAppointment
    activate API
    API->>BookingService: Command → BookAppointment (traceId: svc-9fae23ab)
    activate BookingService
    BookingService-->>ReminderService: Emit → AppointmentBooked (span linked)
    deactivate BookingService
    deactivate API

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🧪 7. Studio Integration¶

This metadata:

Is auto-indexed into vector memory
Enables prompt-to-span correlation
Supports backtrace from span to blueprint
Visualized in the Blueprint Inspector UI with metrics, spans, test traces

✅ Summary¶

Every blueprint contains built-in observability metadata and span templates.
This enables full traceability from input prompt → blueprint → span → log → metric.
Enforced and injected by Observability Agents with links to test and feedback agents.
Forms the backbone of observability-first architecture in the ConnectSoft AI Factory.

🧩 Blueprint Feedback Loop Blueprint¶

The Blueprint Feedback Loop Blueprint defines how each blueprint integrates with runtime feedback, observability signals, test failures, user input, or design drift to trigger updates, corrections, or full regenerations. It is coordinated by the Feedback Loop Agent and relies on observability, memory, and test metadata.

This blueprint creates a closed-loop system where live behavior improves design artifacts — aligning the factory with continuous learning.

🧠 1. Purpose¶

It answers:

🔁 What kinds of runtime feedback can affect this blueprint?
📉 What errors or failures should trigger blueprint updates?
🧠 How are memory, traces, and feedback ingested into the blueprint pipeline?
✅ Can human and AI review both contribute to blueprint improvement?

It links production insights back into the AI software generation process.

🧬 2. Example Structure¶

blueprintId: service-booking-service-v1
type: FeedbackLoop
agent: FeedbackLoopAgent
version: 1.0.0
traceId: fb-loop-b74c23ed

monitoredOutputs:
  - artifact: BookingService.cs
  - artifact: BookAppointmentHandler.cs
  - spanId: booking_duration
  - test: appointment-booking.feature

feedbackTriggers:
  - type: test-failure
    source: BookingTests.cs
    action: regenerate TestBlueprint

  - type: span-error
    match: "booking failed"
    action: notify ObservabilityAgent

  - type: metrics-degradation
    metric: booking_duration
    threshold: p95 > 1000ms
    action: reroute to PerformanceTunerAgent

  - type: human-feedback
    source: GitHub PR comment
    tag: "incorrect-appointment-type"
    action: suggest blueprint refinement

🔎 3. Signal Types Referenced¶

Signal Source	Description
`test-failure`	Automated test or QA result deviation
`span-error`	Log/error emitted during span execution
`metric-anomaly`	Latency, error rate, or usage pattern deviation
`human-feedback`	Comments, issue reports, inline feedback
`drift-detection`	Code diverges from blueprint (via trace map mismatch)

🧠 4. Agent Usage¶

Agent	Role in the Feedback Loop
Feedback Loop Agent	Listens to signals, decides when to regenerate
Observability Agent	Emits actionable metrics and logs
QA/Test Agent	Adds test failure hooks into feedback stream
Engineering Agents	Re-ingest refined blueprint or correct outputs
Knowledge Agent	Updates memory + traceability with feedback results

🔄 5. Trigger-Action Mapping¶

Trigger	Action Taken
`TestFailure: E2E`	Rerun generator for Service + Test
`Metric Spike`	Insert tracing + retry suggestions
`PR Comment: “edge case not covered”`	Link to feature DSL + regenerate tests
`Missing Event: ReminderSent`	Link back to Service/Event Blueprint

🔁 6. Closed Feedback Diagram¶

flowchart TD
    Blueprint -->|Generates| Artifact
    Artifact -->|Runs| Observability
    Observability -->|Emits| Signal
    Signal -->|Triggers| FeedbackLoopAgent
    FeedbackLoopAgent -->|Refines| Blueprint

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📄 7. Output Artifacts¶

File	Description
`feedback-loop.yaml`	Full trigger-action-feedback mapping
`failure-insights.json`	Linked feedback + test + trace analysis
`feedback-events.log`	Runtime events consumed and resolved
`blueprint-regeneration-report.md`	Summary of changes per feedback cycle

✅ Summary¶

Feedback Loop Blueprints create a self-correcting, runtime-aware generation system.
They integrate metrics, tests, logs, and user input into blueprint regeneration logic.
Enable continuous improvement and blueprint evolution based on real-world behavior.
Close the loop between AI-generated software and production reality.

🧩 Blueprint Composition & Inheritance¶

The Blueprint Composition & Inheritance pattern enables blueprints to be modular, reusable, and hierarchical, supporting shared logic, layered overrides, and context-specific variants. It is orchestrated by the Blueprint Resolver Engine and supported by the Knowledge Graph.

This capability allows large systems to be modeled as composable blueprint trees, where common parts (like logging, error handling, or cross-cutting concerns) are inherited or reused across multiple blueprints.

🧠 1. Purpose¶

It enables:

🧱 Modularization of repeated patterns across services or editions
🪜 Hierarchical layering (e.g., base product → SaaS edition → custom tenant)
🔁 Reuse of DSLs, sections, or test cases across teams and services
🧬 Override and patch support for edition-based or tenant-specific mutations

It transforms blueprints from static specs into scalable building blocks.

🧬 2. Example Structure¶

blueprintId: service-booking-lite-v1
extends: service-booking-base-v1
type: Service
version: 1.1.0
agent: ServiceEngineer
traceId: comp-bf23ddc1

overrides:
  endpoints:
    - name: BookAppointment
      policies:
        authorization: disabled
        featureFlag: EnableManualConfirmation

merge:
  - from: shared/error-handling-snippets.yaml
  - from: shared/audit-logging-blocks.yaml

🧩 3. Composition Mechanisms¶

Mechanism	Description
`extends:`	Inherits from another blueprint (same type or a base-type)
`merge:`	Includes external YAML/DSL/snippets into the blueprint body
`override:`	Replaces specific sections or fields of the parent blueprint
`ref:`	Soft-link to other blueprint parts without full inclusion

🔎 4. Use Cases¶

Use Case	Composition Strategy
Common Error Handling	`merge:` common handlers into all services
SaaS Edition Variants	`extends:` a base edition with overrides
Multitenancy	`override:` branding, feature flags
Environment-Specific Infra	`merge:` env files per deployment stage
Shared Pipeline Segments (e.g. test gate)	`merge:` pipeline DSLs into all blueprints

🧠 5. Agent Usage¶

Agent	Role
Blueprint Resolver	Merges, overrides, validates blueprint composition
DevOps Agent	Reuses composed pipeline and infrastructure blocks
Product Owner Agent	Composes SaaS editions with inherited defaults
Studio Knowledge Agent	Visualizes and indexes blueprint hierarchies

🧬 6. Composition Diagram¶

graph TD
    A[🧩 Base Booking Service Blueprint]
    B[🧩 Booking Lite SaaS Edition]
    C[🧩 Custom Vet Tenant Blueprint]
    D[🧱 Error Handling Block]
    E[🧱 Audit Logging Block]

    B --> A
    C --> B
    A --> D
    A --> E

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📄 7. Output Artifacts¶

File	Purpose
`booking-service.yaml`	Final merged blueprint
`edition-booking-lite.yaml`	Overlayed SaaS variant
`custom-vet-tenant.yaml`	Final output with tenant-specific branding
`blueprint-inheritance.graph.yaml`	Hierarchical graph of all blueprint relations

✅ Summary¶

Blueprint Composition enables modular reuse, inheritance, and override across artifacts.
It is essential for scaling the factory across editions, tenants, and shared logic layers.
Used by Product, Infra, DevOps, and Documentation agents alike.
Powers maintainability, consistency, and evolution of platform-wide blueprint systems.

🧩 Blueprint Validation, Linting & Policy Enforcement¶

The Validation & Linting System ensures that all blueprints across the ConnectSoft AI Software Factory are syntactically valid, semantically consistent, and policy-compliant before being consumed by downstream agents or generation pipelines. It is enforced by the Validation Engine embedded within the Orchestration Kernel.

This guarantees that each blueprint is safe to generate, traceable, and aligned with platform-wide standards and enterprise rules.

🧠 1. Purpose¶

Validation answers:

✔️ Is this blueprint structurally correct and complete?
📚 Are all referenced DSLs, artifacts, and IDs resolvable and versioned?
🛡️ Does this blueprint comply with security, privacy, and naming policies?
🔁 Is this blueprint compatible with other referenced blueprints (composition graph)?

It prevents invalid or misconfigured blueprints from reaching generation or deployment stages.

🧬 2. Example Validation Rule Set¶

validationProfile: service-default
blueprintType: Service

rules:
  - id: must-have-traceId
    condition: blueprint.traceId != null
    severity: error

  - id: must-reference-entity-dsl
    condition: blueprint.dsl contains "entity/"
    severity: warning

  - id: blueprint-id-format
    condition: blueprint.blueprintId matches /^[a-z0-9\-]+$/
    severity: error

  - id: forbidden-hardcoded-secret
    condition: no value matches /^API_KEY=.+/
    severity: critical

  - id: max-pipeline-steps
    condition: count(pipeline.stages.steps) <= 20
    severity: warning

✅ 3. Validator Outputs¶

Output Type	File/Format
Errors & Warnings	`blueprint-lint-report.json`
Policy Violations	`compliance-failures.yaml`
Auto-Fixes (optional)	`blueprint-patched.yaml`
Studio UI Integration	Rendered inline in visual inspectors

🧠 4. Agent Participation¶

Agent	Responsibility
Validation Engine	Core validator across all blueprint types
Compliance Agent	Registers global policies and enforcement rules
Editor Agent	Lints during authoring or blueprint merge operations
Feedback Agent	Suggests regeneration if validation fails persistently

🔐 5. Enforced Policy Types¶

Category	Sample Policy Description
Naming	All blueprint IDs must follow kebab-case
Secrets	No hardcoded secrets or credentials in blueprint bodies
Limits	Max steps per pipeline, max retries per handler
Compliance	Must include data classification for all entities
Versioning	Must lock DSL versions before promotion to staging or prod

🛠️ 6. Validation Flow Diagram¶

flowchart TD
    Draft[📝 Draft Blueprint]
    Validator[🔍 Validation Engine]
    Report[📄 Lint Report]
    Generator[⚙️ Agent Generator]

    Draft --> Validator --> Report
    Validator -->|valid| Generator
    Validator -->|invalid| halt

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🧪 7. Studio and Automation Integration¶

Lint checks run in:
CI pipelines
Studio preview environment
Agent authoring lifecycle
Auto-fix support:
Inject missing traceId
Suggest valid enum values for blueprint fields
Flag outdated DSL references for upgrade

✅ Summary¶

Validation and linting ensure all blueprints are production-safe, standardized, and secure.
Driven by policy definitions, schema rules, and trace resolution checks.
Integrated across authoring, generation, promotion, and CI flows.
Protects against errors, inconsistencies, and misalignment with factory standards.

🧩 Blueprint Promotion Lifecycle (Draft → Review → Published)¶

The Blueprint Promotion Lifecycle defines the state machine, governance, and traceable transitions each blueprint goes through — from initial generation to review, publishing, and deprecation. This process is managed by the Blueprint Registry, supervised by the Blueprint Librarian Agent and optionally validated by human reviewers or external policy agents.

It ensures every blueprint follows a transparent, controlled, and versioned lifecycle aligned with enterprise standards.

🧠 1. Purpose¶

It answers:

🧪 Is this blueprint in a usable and trusted state?
🛠️ Has it passed validation, feedback, and policy reviews?
📅 When was it last promoted or deprecated?
🧬 Which version is stable, draft, or deprecated for this domain or edition?

This lifecycle enables safe collaboration, continuous improvement, and release readiness.

🔁 2. Promotion States¶

State	Description
`draft`	Initial state; editable by agents or humans
`validated`	Passed linting, schema checks, and DSL linking
`under-review`	Marked for peer/agent approval, possibly includes CI/CD dry runs
`published`	Locked; referenced by downstream generation pipelines or other blueprints
`deprecated`	No longer used; may have a successor or be removed in next generation

🧬 3. Example Promotion Metadata¶

promotion:
  currentState: published
  promotedBy: BlueprintLibrarianAgent
  version: 1.2.0
  promotedOn: 2025-06-01T12:40:23Z
  replaces: version 1.1.3
  changelog:
    - Updated retry policies for booking event
    - Fixed traceId injection missing for POST /book
  reviewStatus:
    reviewers:
      - name: ProductOwnerAgent
        approval: approved
        time: 2025-05-31T16:23:00Z
      - name: QAEngineerAgent
        approval: approved
        time: 2025-06-01T09:18:55Z

🧠 4. Agent Roles¶

Agent	Action
Blueprint Librarian Agent	Coordinates promotion transitions and changelogs
Review Agents (QA, Arch)	Validate for completeness, observability, traceability
Product Owner Agent	Marks feature-complete and usable for release
Feedback Agent	May trigger rollback or reversion to draft on failure

🔄 5. Promotion Workflow¶

stateDiagram-v2
    [*] --> Draft
    Draft --> Validated : schema + lint pass
    Validated --> UnderReview : manual or agent trigger
    UnderReview --> Published : approvals complete
    Published --> Deprecated : superseded or retired
    Deprecated --> Draft : optional resurrection

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📚 6. Registry and Audit Integration¶

Each blueprint is stored in the Blueprint Registry (backed by blob storage, versioned Git, and search index) with:

promotion-metadata.json
changelog.md
promotion-events.log
semantic version tagging

All transitions are auditable and traceable via the traceId + event log chain.

📅 7. Studio UI Integration¶

Blueprint status indicators (🟡 draft, 🟢 published, 🔴 deprecated)
Promotion UI with changelog, diffs, reviewers
Button to rollback or republish previous version
Cross-linked to all consuming artifacts (pipelines, APIs, services)

✅ Summary¶

Every blueprint follows a promotion lifecycle from draft to published or deprecated.
Each state is validated, versioned, and auditable by both agents and humans.
Promoted blueprints become stable contract points across the AI Software Factory.
The registry and Studio provide visibility, rollback, and governance interfaces.

🧩 DSL Referencing in Blueprints (vs. Embedding)¶

A key design principle of the ConnectSoft AI Software Factory is that blueprints do not embed full DSL definitions, but instead reference versioned DSL documents. This ensures reusability, consistency, traceability, and version control across all generated artifacts and blueprints.

🧠 1. Purpose¶

This design pattern answers:

📚 How do blueprints reuse DSL definitions without duplicating content?
🔗 How do agents know which DSLs were used to generate this blueprint?
📌 How is version consistency ensured across DSL consumers (e.g., services, tests, pipelines)?
🧠 How does the factory trace which part of the DSL influenced the output?

Blueprints become semantic scaffolds that reference DSLs, not contain them.

📂 2. Referencing Syntax¶

dsl:
  - ref: dsls/entities/Appointment.yaml@v1.2.0
  - ref: dsls/events/AppointmentBooked.yaml@latest
  - ref: dsls/api/booking-api.yaml
  - ref: dsls/policies/rate-limits.yaml

ref: points to a semantic DSL URI
Optional version suffix (@vX.Y.Z) ensures traceable reproducibility
Supports wildcards, semantic tags, or pinned commits

🔎 3. Comparison: Referencing vs. Embedding¶

Aspect	Referencing DSLs	Embedding DSLs
🔁 Reusability	High – many blueprints can reference	Low – content duplicated
📚 Traceability	Full – ref tracked by knowledge graph	Weak – embedded blocks hard to trace
📦 Output size	Compact – no bloated blueprint files	Larger – DSLs repeated across blueprints
🧬 Evolvability	Easier – DSLs can evolve independently	Risky – embedded versions go stale
🔐 Validation support	DSL can be linted independently	Blueprint validation harder

🧠 4. Agent Behavior When Referencing DSLs¶

Agent Type	Behavior When Encountering `ref:`
Blueprint Generator	Resolves the referenced DSL from registry
Validator Agent	Ensures referenced DSL exists and is valid
Documentation Agent	Includes `ref` headers or tooltips in output
Feedback Agent	Links test failures to exact DSL ref line

📂 5. Example DSL Registry Organization¶

dsls/
  └── entities/
      ├── Appointment.yaml
      └── User.yaml
  └── events/
      └── AppointmentBooked.yaml
  └── api/
      └── booking-api.yaml
  └── pipelines/
      └── build-release-default.yaml

DSLs are stored in registry-backed folders, with semantic versioning
Registry supports:
Tags (@latest, @approved)
Hashes (commit-based pinning)
Dependencies (DSL importing other DSLs)

🔁 6. DSL Usage in Multi-Agent Pipelines¶

flowchart TD
    DSL["🧬 DSL: Appointment.yaml"]
    ServiceBP["🧩 Service Blueprint"]
    TestBP["🧪 Test Blueprint"]
    DocBP["📄 Docs Blueprint"]

    DSL --> ServiceBP
    DSL --> TestBP
    DSL --> DocBP

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One DSL → many blueprints → many artifacts All traceable back to one canonical definition

📄 7. Blueprint Snippet Example (Referencing DSLs)¶

blueprintId: booking-service-v2
type: Service
dsl:
  - ref: dsls/entities/Appointment.yaml@v1.0.3
  - ref: dsls/commands/BookAppointment.yaml@latest

Output files (e.g., Appointment.cs, BookAppointmentHandler.cs) embed generated logic but point back to source DSL via metadata and traceId.

✅ Summary¶

Blueprints reference DSLs, never embed them — ensuring modularity, traceability, and reusability.
DSLs are stored and versioned independently in the DSL Registry.
All agents are equipped to resolve, validate, and trace these references automatically.
This principle powers the semantic contract layer of the entire AI Software Factory.

🧩 Blueprint Diagrams and Visualizations¶

To enhance understanding, explainability, and collaboration, every blueprint in the ConnectSoft AI Software Factory supports automated visual representations. These visualizations are generated by the Diagram Renderer Agent and embedded in both markdown docs and the Studio UI.

Blueprint diagrams help users and agents quickly grasp structure, flow, dependencies, events, and system behavior.

🧠 1. Purpose¶

Blueprint diagrams answer:

🧩 What is the structural layout or flow of this blueprint?
🔄 How do agents, services, modules, or events interact?
👀 Can a human or AI review this quickly without parsing YAML?
🔗 What dependencies and references does this blueprint include?

Visuals serve as both documentation and runtime-explainable models.

🧬 2. Diagram Types by Blueprint¶

Blueprint Type	Diagram Type
Service Blueprint	Component and handler flow diagrams
SaaS Product Blueprint	Edition matrix and scope graphs
Event Flow Blueprint	Event choreography and causality maps
Documentation Blueprint	Site tree and source inclusion maps
Feedback Blueprint	Signal-response-feedback chains
Composition Graphs	Hierarchical inheritance tree

🎨 3. Diagram Technologies Used¶

Type	Description
`Mermaid`	Default for flows, sequences, graphs
`PlantUML`	Optional for class, component diagrams
`Graphviz`	Complex dependency graphs
`Studio UI`	Interactive blueprint canvas

🖼️ 4. Example Mermaid Diagrams¶

📦 Service Blueprint: Booking Flow¶

graph TD
    UI[UI Layer]
    API[API Gateway]
    Book[BookAppointment Handler]
    Event[AppointmentBooked Event]
    Reminder[ReminderService Subscriber]

    UI --> API --> Book --> Event --> Reminder

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🔄 Event Flow Blueprint¶

sequenceDiagram
    participant BookingService
    participant ReminderService
    participant LoggingService

    BookingService->>ReminderService: Emit → AppointmentBooked
    ReminderService->>LoggingService: Emit → ReminderSent

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🧬 Composition Hierarchy¶

graph TD
    Base[service-base.yaml]
    Lite[service-booking-lite.yaml]
    Custom[service-booking-custom-vet.yaml]

    Lite --> Base
    Custom --> Lite

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🧠 5. Agent Usage¶

Agent	Action
Diagram Renderer Agent	Converts blueprint → visual → markdown embed
Documentation Agent	Inserts visual into markdown files and doc sites
Studio UI Agent	Shows visual overlays for blueprint navigation
Review Agents	Validate structure visually during promotion review

📁 6. Output Artifacts¶

File Name	Description
`booking-service.mmd`	Raw Mermaid syntax
`booking-service.svg/png`	Rendered visual used in docs
`blueprint-composition.graph.yaml`	Graph source for Studio rendering
`docs/booking-flow-diagram.md`	Rendered + captioned explanation section

🔁 7. Dynamic Visual Updates¶

Diagrams are automatically:

Regenerated when the blueprint content changes
Linked via traceId to source blueprint sections
Embedded into vector memory for explainable AI responses
Enhanced with click-to-navigate references in Studio

✅ Summary¶

All blueprints are rendered with consistent, rich diagrams to explain their structure and behavior.
Diagrams are automatically generated and versioned alongside blueprint files.
They are used by agents, reviewers, and end users across documentation and Studio interfaces.
This visual layer is essential for explainability, onboarding, auditing, and traceability.

🧩 Blueprint-Based Generation vs. Template-Based Generation¶

One of the foundational innovations in the ConnectSoft AI Software Factory is the shift from template-based code generation to blueprint-driven, semantic generation. While templates still play a role, they are no longer the top-level driver — blueprints are.

This cycle defines the distinction, trade-offs, and orchestration strategy between high-level blueprints and low-level templates.

🧠 1. Purpose¶

This contrast answers:

🧠 Why are blueprints the primary orchestration layer in the AI factory?
📦 What is the role of templates — when are they used and how?
🧬 How are blueprints, templates, DSLs, and prompts connected?
🔄 Can templates evolve independently from the product design intent?

It enables full separation of intent (blueprint) and form (template).

🔁 2. Comparison Table¶

Feature	Blueprint-Based	Template-Based
Level of Abstraction	High-level, semantic	Low-level, structural
Origin	AI agents, planning prompts	Code generators, prewritten stubs
Structure	YAML-based, modular, composable	File/folder skeletons with tokens
Traceability	Fully traceable via traceId + DSLs	Limited trace, harder to backtrack
Reusability	Blueprint logic applies across templates	Templates reused per project
Evolvability	Change vision → regenerate multiple outputs	Change template → need manual sync
Studio Visibility	Full visual canvas & DSL graph	Hidden logic behind token injection

🧩 3. Architecture View¶

graph TD
    Vision --> Blueprint
    Blueprint --> DSLs
    Blueprint --> Templates
    Templates --> Artifacts
    DSLs --> Artifacts
    Artifacts --> Observability

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

Blueprints orchestrate templates
Templates render structure, based on DSL + blueprint context

🔧 4. Template Invocation from Blueprints¶

Example blueprint section:

templates:
  - use: template/service-handler
    input:
      command: BookAppointment
      entity: Appointment
      featureToggle: EnableManualConfirmation
      output: src/BookingService/BookAppointmentHandler.cs

The template/service-handler is reused across services — input fields come from blueprint context and DSL resolution.

🧠 5. Agent Participation¶

Agent	Role
Blueprint Orchestrator Agent	Chooses which templates to use, with which inputs
Code Generator Agent	Executes template render with blueprint + DSL context
Validator Agent	Verifies template injection matches blueprint intent
Editor Agent	Lets human override or inspect rendered template output

🧬 6. Summary Rules¶

✅ Always define what you want in a blueprint
✅ Templates define how that intent is materialized
✅ DSLs define vocabulary and contracts reused by both
🔁 Templates can be updated across all consuming blueprints if versioned
📎 Templates are linked via semantic URIs like template://service/handler@v2

🧪 7. Output Files¶

File	Description
`booking-service.yaml`	Blueprint input
`BookAppointmentHandler.cs`	Rendered artifact from template
`.factory/trace-map.yaml`	Links blueprint → template → DSL → output
`template-usage.log`	Diagnostic record of used templates

✅ Summary¶

Blueprints orchestrate generation via high-level design intent.
Templates are implementation mechanisms scoped by blueprint context.
The shift from template-first to blueprint-first powers automation, reuse, and consistency.
The factory uses this layered model for maximum scalability and control.

🧩 Blueprint-Aware Testing and Coverage Mapping¶

In the AI Software Factory, tests are not just code artifacts — they are tightly coupled with blueprint definitions, DSL references, and system intents. This allows agents to generate, trace, validate, and regenerate tests based on blueprint scope, ensuring semantic coverage and behavioral fidelity.

This blueprint-aware model unlocks full test traceability, coverage visualization, and AI-driven validation feedback loops.

🧠 1. Purpose¶

This approach answers:

🧪 Are all blueprint-defined behaviors covered by tests?
🧬 Which commands, events, or features are untested?
🔁 Can we regenerate tests from blueprint or DSL changes?
📊 What’s the test coverage across services, agents, and editions?

It powers automated QA pipelines with end-to-end semantic test scaffolding.

🧬 2. Example Blueprint Test Mapping¶

blueprintId: booking-service-v1
testCoverage:
  commands:
    - name: BookAppointment
      tests:
        - scenario: should prevent double booking
          file: tests/booking/double-booking.feature
        - scenario: should allow urgent slots
          file: tests/booking/urgent.feature

  events:
    - name: AppointmentBooked
      tests:
        - scenario: should emit event
          file: tests/booking/event-emission.feature

  errors:
    - name: InvalidSlot
      tests:
        - scenario: should reject past appointment
          file: tests/booking/invalid-past.feature

  uncovered:
    - command: CancelAppointment
    - event: AppointmentRescheduled

📊 3. Coverage Report Output¶

Item	Status	Coverage Source
`BookAppointment`	✅ Covered	2 BDD scenarios
`CancelAppointment`	❌ Missing	DSL referenced, test not found
`AppointmentBooked`	✅ Covered	Event span + test + log
`InvalidSlot`	✅ Covered	Test and span validated
`AppointmentRescheduled`	❌ Missing	Event defined, not tested

🧠 4. Agent Participation¶

Agent	Role
Test Generator Agent	Generates BDD/specflow tests from DSL + blueprint
QA Coverage Agent	Maps existing tests to blueprint structure
Feedback Agent	Suggests regeneration if test fails or is missing
Studio Inspector Agent	Renders coverage maps and test linkage graphs

📈 5. Coverage Mapping Diagram¶

graph TD
    DSL["📘 DSL: BookAppointment"]
    BP["🧩 Service Blueprint"]
    T1["✅ Test: should prevent double booking"]
    T2["✅ Test: should allow urgent slots"]
    T3["❌ Missing: CancelAppointment test"]

    DSL --> BP --> T1
    BP --> T2
    BP --> T3

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

This visual helps track which blueprint segments are covered or missing in tests.

📁 6. Output Files¶

File	Purpose
`test-coverage-map.yaml`	Links between blueprint commands/events/tests
`coverage-report.md`	Human-readable summary of test coverage
`uncovered-commands.log`	List of missing test coverage
`studio-coverage-view.json`	Used by Studio UI for rendering test graphs

✅ Summary¶

Blueprint-aware testing ensures all generated logic is validated against its intent.
Semantic mapping connects DSLs → blueprints → tests → runtime behavior.
Missing test coverage is automatically detected and flagged for regeneration.
Enables high-trust generation with continuous, explainable test feedback loops.

🧩 Final Summary and Blueprinting Principles Recap¶

The Blueprint System is the semantic heart of the ConnectSoft AI Software Factory. It captures intent, structure, traceability, and coordination of everything the platform generates — from microservices to documentation, from pipelines to UI, from SaaS editions to observability.

This cycle consolidates all principles, best practices, and strategic value of blueprinting into a clear, actionable reference for both human architects and AI agents.

🎯 Blueprinting Principles¶

Principle	Description
Intent-First	Blueprints encode what to build, not how to build it
Composability	Blueprints are modular, hierarchical, and can extend or override others
Traceability by Design	Every artifact maps to its blueprint, DSL, agent, and originating intent
Separation of Concerns	DSLs define semantics, templates define structure, blueprints orchestrate
Observability-First	Each blueprint emits spans, logs, metrics, and feedback hooks
Policy-Aware	Validated, linted, and compliant with global platform constraints
Multi-Agent Ready	Blueprints are designed for collaborative AI agent workflows
Reusable Across Domains	Blueprints work across microservices, pipelines, tests, docs, and editions
Memory-Integrated	Blueprint traceIds and lineage power memory systems and regeneration loops
Promotion-Governed	Draft → Validated → Reviewed → Published → Deprecated lifecycle enforced

🧱 Types of Blueprints (Recap)¶

Category	Examples
Structural	Microservice, API, Auth Server, SaaS Product
Behavioral	Events, Commands, Handlers, Features, Workflows
Operational	Pipelines, Observability, Runtime Configuration
Documentation	Markdown artifacts, diagrams, help docs
Testing & QA	SpecFlow files, test scenarios, feedback triggers
System Coordination	Traceability, Feedback Loops, Promotion metadata

🛠️ Core Outputs Produced from Blueprints¶

Output Type	Generated By
Source Code	Templates parameterized via blueprint + DSL context
Tests & Features	Derived from command/event/handler sections
Diagrams	Mermaid/PlantUML rendered from structural metadata
Docs & Markdown	Enriched markdown rendered from blueprint hierarchy
CI/CD Pipelines	Assembled via orchestration pipeline blueprints
Observability	Span maps, metric hooks, and traceId propagation
Feedback Triggers	Auto-correction inputs to retrain or regenerate flows

📌 Blueprint System Mindmap¶

graph TD
    Vision --> Blueprints
    Blueprints --> DSLs
    Blueprints --> Templates
    Blueprints --> Artifacts
    Blueprints --> Observability
    Blueprints --> Testing
    Blueprints --> Promotion
    Blueprints --> FeedbackLoop
    Blueprints --> Composition
    Blueprints --> Traceability

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

🧠 Strategic Value¶

✅ Scalable across 3,000+ services and SaaS variants
🔄 Regenerable from source intent at any time
🔍 Auditable across all generated outputs
🤝 Collaborative between agents and humans
🔗 Composable and traceable across the entire platform

✅ Final Summary¶

The Blueprint System is the semantic contract layer that powers the entire ConnectSoft AI Software Factory:

“Templates render, DSLs define, but blueprints decide what gets built, how it's structured, and why it exists.”

From vision to runtime, from planning to regeneration — every action, artifact, and agent in the factory revolves around the Blueprint System.

📘 Blueprints in the AI Software Factory¶

🧩 Overview of Blueprints¶

🧩 What Is a Blueprint?¶

🔍 Why Blueprints Matter¶

🧠 Key Properties of a Blueprint¶

🔁 How Blueprints Fit Into the Factory Flow¶

🎛️ Blueprint vs DSL vs Artifact¶

🗂️ Format and Structure¶

🧭 Who Produces Blueprints?¶

✅ Summary¶

🧩 DSL vs Blueprint vs Artifact¶

🔣 1. What Is a DSL?¶

🧾 Example: EventDSL¶

🧩 2. What Is a Blueprint?¶

🧾 Example: Blueprint referencing DSLs¶

🛠️ 3. What Is an Artifact?¶

📊 4. DSL vs Blueprint vs Artifact: Side-by-Side Table¶

🧭 5. Blueprint as an Execution Contract¶

✅ Summary¶

🧩 Blueprint Lifecycle Overview¶

🔄 1. Blueprint Lifecycle Phases¶

🔧 2. Lifecycle Events¶

📦 3. Storage and Representation¶

📘 4. Blueprint Lifecycle Diagram¶

🧠 5. Regeneration Scenarios¶

🧭 6. Agent Roles in the Lifecycle¶

✅ Summary¶

🧩 Traceability and Metadata¶

🛰️ 1. Why Traceability Matters¶

🧬 2. Core Metadata Fields¶

🔍 3. DSL and Artifact References¶

🧠 4. Semantic Embedding and Memory Indexing¶

📡 5. Traceability Diagram¶

🧷 6. Events and Trace Hooks¶

✅ Summary¶

🧩 Blueprint Memory Indexing and Retrieval¶

🧠 1. Multi-Layer Memory Model¶

🔎 2. Semantic Embedding of Blueprints¶

🧭 3. Blueprint Retrieval Interfaces¶

📘 4. Example: Vectorized Blueprint¶

🕸️ 5. Blueprint Memory Graph¶

🔁 6. Memory-Driven Blueprint Reuse¶

✅ Summary¶

🧩 Agents That Use and Produce Blueprints¶

🧠 1. Blueprint as the Core Artifact Exchange Format¶

🤖 2. Which Agents Produce Blueprints?¶

🔁 3. Which Agents Consume Blueprints?¶

🔄 4. Agent-to-Agent Blueprint Flow¶

🔂 5. Multi-Agent Blueprint Collaboration Patterns¶

🧬 6. Agent-Blueprint Registry¶

✅ Summary¶

🧩 Vision Blueprint¶

🧠 1. Purpose¶

🧬 2. Example Structure¶

🔎 3. DSLs Referenced or Embedded¶

🧠 4. Agent Usage¶

🧭 5. Vision Blueprint Diagram¶

🔁 6. Evolution and Refinement¶

✅ Summary¶

🧩 Service Blueprint¶

🧠 1. Purpose¶

🧬 2. Example Structure¶

🔎 3. DSLs Referenced¶

🧠 4. Agent Usage¶

🔁 5. Collaboration Flow¶

🧰 6. Output Examples (Generated Artifacts)¶

✅ Summary¶

🧩 Module Blueprint¶

🧠 1. Purpose¶

🧬 2. Example Structure¶

🔎 3. DSLs Referenced¶

🧠 4. Agent Usage¶

🧩 5. Relationship to Other Blueprints¶

🧪 6. Example Outputs¶

🧭 7. Blueprint Fan-out Diagram¶

✅ Summary¶

🧩 Test Blueprint¶

🧠 1. Purpose¶

🧬 2. Example Structure¶

🔎 3. DSLs Referenced¶