🔄 Project Lifecycle¶

The ConnectSoft AI Software Factory transforms the way software is conceived, architected, and delivered by modeling the complete lifecycle of a project as a sequence of autonomous, agent-driven operations. This lifecycle reflects the journey of a SaaS solution from an initial concept to a production-ready, continuously evolving product — executed through intelligent orchestration and domain-aware automation.

At its core, the lifecycle embodies ConnectSoft’s foundational principles:

Clean Architecture and Domain-Driven Design to ensure modularity and clarity.
Event-Driven and Observable workflows for full transparency and traceability.
Security-First and Cloud-Native practices for safe and scalable operations.
Multi-Tenant SaaS support with tenant-aware customization and isolation.

Each project begins with minimal user input and evolves autonomously through clearly defined milestones. AI agents collaborate to define, generate, validate, deploy, and monitor all aspects of the solution — from the domain model to deployment infrastructure. The result is a fully-automated, enterprise-grade software system tailored to the user's intent, capable of rapid adaptation and intelligent improvement over time.

This lifecycle not only eliminates repetitive engineering work but also ensures consistency, quality, and strategic alignment across all projects built on the platform.

🧭 End-to-End Project Lifecycle Diagram¶

flowchart TD
  Start(["📌 Project Created"])

  subgraph PLAN [🧠 Planning & Design]
    C1[1️⃣ Project Metadata]
    C2[2️⃣ Domain Modeling]
    C3[3️⃣ Domain Validation]
    C4[4️⃣ Use Case Modeling]
    C5[5️⃣ UX Modeling]
    C6[6️⃣ System Architecture]
  end

  subgraph BUILD [⚙️ Generation & Implementation]
    C7[7️⃣ Microservice Templates]
    C8[8️⃣ API & Contracts]
    C9[9️⃣ Backend Logic]
    C10[🔟 Frontend Generation]
    C11[1️⃣1️⃣ Database Model]
    C12[1️⃣2️⃣ Messaging & Events]
    C13[1️⃣3️⃣ Tests & BDD]
    C14[1️⃣4️⃣ CI/CD Pipelines]
  end

  subgraph READY [🛡️ Readiness & Enablement]
    C15[1️⃣5️⃣ Security Hardening]
    C16[1️⃣6️⃣ Documentation]
    C17[1️⃣7️⃣ Integrations]
    C18[1️⃣8️⃣ Observability]
    C19[1️⃣9️⃣ Multi-Tenancy]
  end

  subgraph SHIP [🚀 Go-Live & Feedback]
    C20[2️⃣0️⃣ Simulation Playback]
    C21[2️⃣1️⃣ Release Packaging]
    C22[2️⃣2️⃣ Prod Readiness]
    C23[2️⃣3️⃣ Production Deployment]
    C24[2️⃣4️⃣ Feedback Loop]
  end

  Start --> C1
  C1 --> C2 --> C3 --> C4 --> C5 --> C6
  C6 --> C7 --> C8 --> C9 --> C10 --> C11 --> C12 --> C13 --> C14
  C14 --> C15 --> C16 --> C17 --> C18 --> C19
  C19 --> C20 --> C21 --> C22 --> C23 --> C24
  C24 -->|Triggers Next Cycle| C1

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🔵 Cycle 1 - Project Initialization¶

🧩 Overview¶

The Project Initialization cycle is the first entry point into the ConnectSoft AI Software Factory. It begins when a human user (or external integration) creates a new software project and provides high-level intent and constraints.

This cycle sets the foundation for orchestrated agent activation, automated traceability, and downstream planning.

🧑‍💼 Actors¶

Actor	Role
Human User	Initiates project creation, provides business context
Orchestration Layer	Validates input, starts agent session, emits `ProjectInitialized`
Studio UI / API Gateway	Interface for submitting inputs and selecting preferences
Vision Architect Agent	Awaits activation based on orchestration trigger in Cycle 3

📝 Inputs Collected from User¶

Users define the following project metadata:

Parameter	Description
Project Name	Unique title for traceability (e.g., `VetPracticeApp`)
Business Objective	One-line problem/opportunity description
Industry Domain	e.g., Healthcare, Legal, HR, Logistics
Initial Use Case Prompt	Freeform natural language (e.g., “Appointment scheduling for clinics”)
Template Type	Prebuilt blueprint to use (e.g., Microservice Template)
Target Scope	MVP, Full SaaS, Internal Tool, API-only, etc.
Preferred Tooling Stack	Currently: Azure DevOps only
Feature Flags (optional)	Enable/disable specific platform features (e.g., Observability, BFF, gRPC, Dapr)
Runtime Constraints	Deployment budget, time-to-market, scale considerations

🧠 System Behavior¶

1. Project Creation Trigger¶

User submits inputs via the Studio UI or ConnectSoft API.
Orchestration Layer receives CreateProjectCommand.

2. Validation & Metadata Enrichment¶

Orchestration validates parameters and assigns:
- projectId (UUID)
- traceId and sessionId
- Timestamps and initiator metadata

3. Repository & Folder Setup¶

Azure DevOps Project is provisioned:
- Git Repos: /src, /tests, /infra, /docs
- Azure Boards Project: Epics, Features initialized
- Azure Wiki seeded with placeholder structure

4. Event Emission¶

Emits: ProjectInitialized → triggers Vision Architect Agent (Cycle 3)

📦 Artifacts Generated¶

Artifact	Format	Location
`project-metadata.json`	JSON	Azure DevOps `docs/metadata/`
Initial Wiki Home Page	Markdown	Azure Wiki
Azure Boards Feature Work Item	JSON	Azure DevOps Boards
Empty Git Repositories	Git	Azure DevOps Repos

🔁 Orchestration Flow¶

sequenceDiagram
  participant User
  participant StudioUI
  participant Orchestration
  participant AzureDevOps
  participant EventBus

  User->>StudioUI: Create New Project
  StudioUI->>Orchestration: Submit Project Metadata
  Orchestration->>AzureDevOps: Create Repos, Boards, Wiki
  Orchestration->>Orchestration: Assign traceId, sessionId
  Orchestration->>EventBus: Emit ProjectInitialized

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📘 Notes¶

Currently, only Azure DevOps is supported for:
- Git repositories
- Work item tracking
- CI/CD pipelines
- Wiki documentation
- Dashboards and reporting
Future iterations will support GitHub, GitLab, Bitbucket, and custom integrations.

🟢 Cycle 2 - Source Control Configuration & Template Provisioning¶

🧩 Overview¶

In this cycle, the system finalizes source control setup and provisions the selected solution template. These form the technical scaffolding upon which agents will generate microservices, APIs, CI/CD pipelines, and documentation.

🧑‍💼 Actors¶

Actor	Role
User	Confirms default source control and selects project template
Orchestration Layer	Persists user selections and dispatches provisioning events
Template Provisioner Agent	Applies selected solution template to source control
Azure DevOps Integrator	Creates project structure, repos, pipelines, boards

⚙️ Tooling & Stack Selection¶

For MVP, only Azure DevOps is supported. This includes:

🔹 Git Repositories (source, test, infra, docs)
🔹 Azure Boards for work items and planning
🔹 Azure Pipelines for CI/CD
🔹 Azure Artifacts for package management
🔹 Azure Wiki for documentation

🧠 User Choices¶

Field	Description
Source Control System	Auto-set to Azure DevOps (future: GitHub, GitLab)
Solution Template	User selects from pre-approved templates (see below)
Initial Modules	Optional: pre-select microservices or libraries to scaffold
Pipeline Setup	YAML pipeline structure: classic, stage-based, or parallelized
Infra-as-Code Mode	Choose Bicep, Terraform, or default

🧰 Supported Solution Templates¶

Template Name	Description
Microservice Template	Clean architecture, DDD, MassTransit, NHibernate
API Gateway Template	YARP-based reverse proxy with rate-limiting, auth
Auth Server Template	OpenIddict-based Identity Provider
Library Template	NuGet library with dependency injection, tests
Blazor WebApp Template	Razor-based front-end with authentication
(more to be added)	—

⚙️ System Behavior¶

1. Source Control Workspace Initialization¶

Orchestration Layer ensures Azure DevOps:
- Creates Git repos: src/, tests/, infra/, docs/
- Sets up branch protection & default main branch
- Adds README.md, .editorconfig, .gitignore

2. Template Provisioning¶

Template Provisioner Agent:
- Instantiates template from ConnectSoft catalog
- Customizes scaffold with project name and identifiers
- Commits initial structure to Azure DevOps repos

3. Work Item Seeding¶

Azure Boards initialized with:
- Epic: “Bootstrapping System”
- Feature: “Initialize Base Solution Template”
- Tasks: Create services, APIs, CI/CD, infra

4. Event Emission¶

Emits: TemplateProvisioned → triggers Vision Agent (Cycle 3)

📦 Artifacts Generated¶

Artifact	Format	Location
`ConnectSoft.Template.json`	JSON metadata	`docs/metadata/`
`.git/` structure	Git repo	Azure DevOps
`azure-pipelines.yml`	YAML	Root of repo
Work Items	JSON	Azure DevOps Boards
Wiki Home Page	Markdown	Azure DevOps Wiki

🔁 Orchestration Flow¶

sequenceDiagram
  participant User
  participant StudioUI
  participant Orchestration
  participant TemplateAgent
  participant AzureDevOps
  participant EventBus

  User->>StudioUI: Select Azure DevOps, Microservice Template
  StudioUI->>Orchestration: Submit Source Config
  Orchestration->>AzureDevOps: Setup repos, boards, wiki
  Orchestration->>TemplateAgent: Instantiate and scaffold template
  TemplateAgent->>AzureDevOps: Push scaffolded code
  Orchestration->>EventBus: Emit TemplateProvisioned

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📘 Notes¶

Template customization supports tokenized replacement ({{ProjectName}}, {{Domain}}, etc.)
YAML pipelines follow ConnectSoft standards for:
- build, test, package, deploy stages
Template catalogs and blueprint versions are versioned and traceable via metadata

🔷 Cycle 3 - Vision Agent Activation¶

🧩 Overview¶

This cycle marks the activation of the first core intelligence agent: the Vision Architect Agent. It interprets the user’s intent, context, and constraints — and transforms them into a structured, traceable Vision Document, setting the foundation for all downstream agents.

The Vision Agent is activated by the orchestration layer upon receiving the TemplateProvisioned event from Cycle 2.

🧠 Objectives¶

Convert user input into a machine-readable, semantically enriched specification
Structure product scope into goals, roles, drivers, and risks
Identify strategic anchors: MVP focus, domain complexity, competitive differentiation

🧑‍💼 Actors¶

Actor	Role
Orchestration Layer	Emits `TemplateProvisioned` and triggers agent activation
Vision Architect Agent	Parses inputs into `VisionDocument.md` and `VisionDocument.json`
Semantic Kernel	Executes planning, summarization, extraction skills
User (Optional)	May provide clarifying answers via chat or form
Studio UI / Review Panel	Displays structured vision for human review and signoff

🧾 Inputs to the Agent¶

Source	Data
Project Metadata	Name, domain, business objective, scope
Use Case Prompt	Freeform prompt from Cycle 1
Template Info	Selected blueprint (e.g., Microservice Template)
Runtime Constraints	Budget, time-to-market, observability toggles
Industry Tags	e.g., `healthcare`, `multi-tenant`, `event-driven`

🧪 Agent Behavior¶

The Vision Agent:

Parses the prompt and metadata
Applies predefined prompt engineering templates
Leverages embedded semantic skills:
- 🧩 Problem Framing
- 🧭 Opportunity Discovery
- 📌 Stakeholder Role Extraction
- 🚦 Constraint Analysis
- 🧱 Initial Modularization Proposals
Outputs structured artifacts

📦 Artifacts Generated¶

Artifact	Format	Description
`VisionDocument.md`	Markdown	Human-readable description of goals, drivers, and outcomes
`VisionDocument.json`	JSON	Machine-readable version with tags, UUIDs, classifications
`agent-metadata.json`	JSON	Agent version, skillset used, traceability ID
`Event: VisionDocumentCreated`	EventBus	Triggers next orchestration step

Example markdown output (excerpt):

## Problem Statement
Clinics lack efficient, mobile-friendly appointment scheduling tools...

## Business Goals
- Automate 80% of appointment bookings
- Reduce admin time by 30%...

## Stakeholders
- Clinic Manager
- Pet Owner (Mobile-first User)
- Admin Staff

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant VisionAgent
  participant SemanticKernel
  participant EventBus
  participant AzureDevOps

  Orchestration->>VisionAgent: Trigger Agent (TemplateProvisioned)
  VisionAgent->>SemanticKernel: Run Skills
  SemanticKernel->>VisionAgent: Structured Output
  VisionAgent->>AzureDevOps: Store vision artifacts
  VisionAgent->>EventBus: Emit VisionDocumentCreated

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📘 Notes¶

Traceability: every vision output includes traceId, agentId, skillGraphId
This cycle can include optional user clarification via chat UI if the agent confidence score is low
Future support: multilingual prompts, prompt style selectors (e.g., “business plan,” “elevator pitch”)

🟣 Cycle 4: Product Planning¶

🧩 Overview¶

This cycle involves the Product Manager Agent, which consumes the VisionDocument generated in Cycle 3 and produces a detailed product plan that includes features, MVP boundaries, user goals, value streams, and release phases.

This structured plan acts as a contract of understanding between engineering agents and business stakeholders — ensuring traceability of goals to implementation.

🧠 Objectives¶

Decompose vision into epics, features, goals, and user outcomes
Identify MVP feature set and release roadmap
Define personas, scenarios, and value streams
Establish traceability between business intent and service boundaries

🧑‍💼 Actors¶

Actor	Role
Product Manager Agent	Translates vision into executable features and outcomes
Orchestration Layer	Activates product planning step after `VisionDocumentCreated`
Semantic Kernel	Uses planning, prioritization, summarization, and roadmap generation skills
Azure DevOps Integrator	Syncs features and epics to Boards
User (Optional)	Can review or adjust initial product plan before execution

📝 Inputs to the Agent¶

Input	Source
`VisionDocument.json`	Output of Cycle 3
Domain Tags	Extracted during initialization
Stakeholder Roles	From Vision Document
Selected Template & Constraints	From Cycles 1–2

🧪 Agent Behavior¶

The Product Manager Agent performs the following:

Reads Vision: Extracts problem, opportunity, constraints
Identifies User Personas: Maps to value-providing actors
Decomposes Product Scope:
- Defines Epics, Features, User Stories
- Assigns each to a Persona, Goal, and Outcome
Prioritizes MVP:
- Groups features into MVP, vNext, Stretch Goals
Outputs Planning Artifacts:
- Product Plan, Feature List, Roadmap Phases
- Azure Boards Epics/Features/Tasks (JSON format)

📦 Artifacts Generated¶

Artifact	Format	Description
`ProductPlan.md`	Markdown	Hierarchical outline of features, releases, goals
`Features.json`	JSON	Structured feature set (used by Engineering Agents)
Azure Boards Items	Epics, Features, Stories	Pushed to Azure DevOps
`Event: ProductPlanCreated`	EventBus	Triggers next agent phase (Enterprise Architecture)

Sample structure excerpt:

{
  "epic": "Appointment Booking",
  "features": [
    {
      "title": "Book Appointment (Mobile)",
      "persona": "Pet Owner",
      "outcome": "Quickly book without account",
      "priority": "MVP"
    },
    {
      "title": "Admin View Calendar",
      "persona": "Clinic Admin",
      "priority": "vNext"
    }
  ]
}

🗂 Sample Output in Markdown¶

## 🧭 Product Plan

### 🎯 Goal 1: Simplify Booking for Pet Owners
- ✅ Feature: Mobile Booking Without Login [MVP]
- ✅ Feature: Date-Based Availability Search [MVP]
- 🔄 Feature: Appointment Reminders [vNext]

### 👤 Stakeholder: Clinic Admin
- ✅ Feature: Staff Calendar View [vNext]
- 🔄 Feature: Manual Rescheduling Tool [Stretch]

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant ProductAgent
  participant AzureDevOps
  participant EventBus

  Orchestration->>ProductAgent: Trigger Agent (VisionDocumentCreated)
  ProductAgent->>ProductAgent: Analyze vision, personas, scope
  ProductAgent->>AzureDevOps: Push epics/features/stories
  ProductAgent->>EventBus: Emit ProductPlanCreated

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📘 Notes¶

Features tagged with MVP, vNext, or Stretch
All features maintain a backward link to original user goals (traceable)
Azure DevOps Boards integration supports:
Automatic linking of work items to repos
Area paths and iterations for planning
Future: support Lean Canvas and Business Model Canvas auto-generation

🟠 Cycle 5: Enterprise Architecture¶

🧩 Overview¶

This cycle introduces the Enterprise Architect Agent, which structures the project into bounded contexts, defines domain boundaries, aligns with DDD principles, and connects strategic business goals to technical service boundaries.

This foundational modeling phase ensures modularity, scalability, and extensibility of the resulting SaaS platform.

🧠 Objectives¶

Model domain-driven architecture based on the Product Plan
Define bounded contexts, aggregates, and entity relationships
Align epics/features to contextual service ownership
Create a context map for downstream service generation

🧑‍💼 Actors¶

Actor	Role
Enterprise Architect Agent	Analyzes product features and domains to model bounded contexts
Orchestration Layer	Triggers this cycle after `ProductPlanCreated`
Semantic Kernel	Applies DDD modeling, value object detection, context extraction
User (Optional)	May review proposed models and aggregates before code generation

📥 Inputs to the Agent¶

Input	Source
`ProductPlan.json`	From Product Manager Agent
`VisionDocument.json`	Original goals, personas
Features-to-Context Suggestions	Seeded by templates or previous agents
Industry Tags	Used for contextual hints (e.g., healthcare, education, logistics)

🧪 Agent Behavior¶

The Enterprise Architect Agent performs:

Contextual Decomposition:
- Groups features by domain boundaries and responsibilities
- Identifies bounded contexts
Domain Modeling:
- Detects key entities, aggregates, value objects
- Maps commands/events per service
Service Responsibilities:
- Aligns responsibilities to autonomous services
- Defines input/output ports and integrations
Event Topology:
- Suggests events for cross-context communication
- Identifies eventual consistency boundaries
Artifact Emission:
- Context map
- Domain model diagrams
- Service definition metadata

📦 Artifacts Generated¶

Artifact	Format	Description
`ContextMap.md`	Markdown	High-level domain map and responsibilities
`ContextMap.mmd`	Mermaid	Visual context map
`DomainModel.json`	JSON	Aggregates, entities, value objects, events
`Services.yml`	YAML	Bounded context to service mapping
`Event: ContextMapCreated`	EventBus	Triggers next architectural steps

📉 Sample Context Map (Mermaid)¶

graph TD
  Booking --> Notifications
  Booking --> Calendar
  Calendar --> Staff
  Notifications --> EmailService
  Staff --> Identity

  subgraph Booking Context
    B1[BookAppointment]
    B2[CancelAppointment]
  end

  subgraph Notifications Context
    N1[SendReminder]
    N2[NotifyReschedule]
  end

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📑 Sample Domain Modeling Snippet (JSON)¶

{
  "contexts": [
    {
      "name": "Booking",
      "aggregates": ["Appointment", "Schedule"],
      "commands": ["Book", "Cancel", "Reschedule"],
      "events": ["AppointmentBooked", "AppointmentCanceled"]
    }
  ]
}

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant EnterpriseArchitect
  participant EventBus
  participant StudioUI

  Orchestration->>EnterpriseArchitect: Trigger Agent (ProductPlanCreated)
  EnterpriseArchitect->>EnterpriseArchitect: Decompose features into contexts
  EnterpriseArchitect->>StudioUI: Store Context Map / Domain Model
  EnterpriseArchitect->>EventBus: Emit ContextMapCreated

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📘 Notes¶

Each context is tagged with:
- Owner (e.g., “BookingService”)
- Dependencies (inbound, outbound)
- Integration type (REST, event, async)
Supports eventual consistency, DDD tactical patterns, and service isolation
Aggregates are defined for future NHibernate generation

🟡 Cycle 6: Solution Blueprinting¶

🧩 Overview¶

In this cycle, the Solution Architect Agent transforms bounded contexts and domain models into modular solution blueprints based on Clean Architecture principles. This includes defining layers, interfaces, adapter strategies, and the physical project structure to support independent deployment, testing, and evolution.

This blueprint ensures code generated in upcoming cycles aligns with ConnectSoft’s architectural standards.

🧠 Objectives¶

Map bounded contexts to solution-level modules
Define Clean Architecture layers (Domain, Application, Infrastructure, EntryPoints)
Describe port/adapter patterns, dependency flow, and abstractions
Define inter-service relationships and integration boundaries
Prepare scaffolding instructions for code generation agents

🧑‍💼 Actors¶

Actor	Role
Solution Architect Agent	Creates high-level modular system design using Clean Architecture
Orchestration Layer	Triggers blueprint generation after `ContextMapCreated`
Template Engine	Uses ConnectSoft base templates to prepare project layout
Studio UI	Renders diagrams and documentation for review

📝 Inputs to the Agent¶

Input	Source
`ContextMap.json`	From Enterprise Architecture
`DomainModel.json`	Aggregates, entities, commands, events
`ProductPlan.json`	Features-to-context mapping
`ServiceTemplate.yaml`	Microservice structure definition (from template catalog)

🧪 Agent Behavior¶

The Solution Architect Agent performs:

Layer Mapping:
- Applies Clean Architecture pattern across each context
- Defines Domain, Application, Infrastructure, API projects
Adapter Identification:
- Detects needed adapters: HTTP, gRPC, MassTransit, Redis, etc.
- Maps input/output ports and boundaries
Service Composition:
- Creates blueprint per service:
  - Controllers / Consumers (Entry)
  - Use Cases (App Layer)
  - Repositories / Events (Infra Layer)
Integration Matrix:
- Defines how services communicate (REST, Events, etc.)
- Generates service-to-service dependency graph
Blueprint Emission:
- Instructions for scaffolding microservice folders and projects
- Architecture overview for documentation and validation

📦 Artifacts Generated¶

Artifact	Format	Description
`SolutionBlueprint.md`	Markdown	High-level layout and module description
`ServiceBlueprints/*.yaml`	YAML	One per service; structure, ports, dependencies
`CleanArchitectureDiagram.mmd`	Mermaid	Layered architecture view per service
`Event: BlueprintCreated`	EventBus	Triggers Engineering Scaffolding phase

📂 Sample Microservice Blueprint (`BookingService.yaml`)¶

name: BookingService
architecture: CleanArchitecture
layers:
  - domain: Appointment, Schedule
  - application: BookAppointmentHandler, CancelAppointmentHandler
  - infrastructure: NHibernateRepository, AzureServiceBusPublisher
  - entrypoints: REST API Controller
adapters:
  - input: REST (OpenAPI)
  - output: Event (AppointmentBooked)
dependencies:
  - NotificationService (Event: SendReminder)

🖼 Mermaid Diagram (Clean Architecture Example)¶

graph TD
  UI[API Controller]
  APP[Use Case: BookAppointment]
  DOM[Aggregate: Appointment]
  INF[Infra: NHibernate Repo]
  EVT[Infra: Event Publisher]

  UI --> APP
  APP --> DOM
  APP --> INF
  APP --> EVT

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🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant SolutionArchitect
  participant StudioUI
  participant EventBus

  Orchestration->>SolutionArchitect: Trigger Agent (ContextMapCreated)
  SolutionArchitect->>StudioUI: Upload blueprint, diagrams
  SolutionArchitect->>EventBus: Emit BlueprintCreated

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📘 Notes¶

Each service has:
- A blueprint file
- Folder structure instructions
- Interface naming conventions (e.g., IAppointmentRepository)
Aligns with ConnectSoft’s Microservice Template repo structure
Future: blueprint diffing for upgrades/refactors

🟤 Cycle 7: Event-Driven Design¶

🧩 Overview¶

In this cycle, the Event-Driven Architect Agent defines the asynchronous communication fabric that binds services together using events, sagas, and event contracts. This enables loose coupling, event sourcing, and scalable orchestration across microservices.

The agent consumes service blueprints and domain models to define domain events, integration events, and event flows across bounded contexts.

🧠 Objectives¶

Identify and structure domain events and integration events
Define event contracts and schemas (JSON Schema or Avro)
Specify saga patterns, compensation logic, and long-running workflows
Generate an event topology with producers, consumers, and buses
Ensure full traceability from aggregates to emitted/consumed events

🧑‍💼 Actors¶

Actor	Role
Event-Driven Architect Agent	Builds the event-driven interaction model across contexts
Orchestration Layer	Activates agent after `BlueprintCreated`
MassTransit Integrator (default)	Generates bus configuration for publish/subscribe patterns
Studio UI	Visualizes event flows and schemas

📥 Inputs to the Agent¶

Input	Source
`ServiceBlueprints/*.yaml`	From Cycle 6
`DomainModel.json`	Entity lifecycle and command mappings
`ContextMap.json`	Bounded context responsibilities
`TemplateSettings.yaml`	Messaging framework (default: MassTransit + Azure Service Bus)

🧪 Agent Behavior¶

Event Emitter Mapping:
- Extracts domain events from aggregate actions (e.g., AppointmentBooked)
- Maps them to services that produce/consume those events
Event Schema Generation:
- Defines structured event contracts (e.g., JSON Schema, Avro)
- Adds metadata: event version, type, emitter, correlation ID
Saga Orchestration Design:
- Detects long-running operations (e.g., onboarding, payments)
- Proposes saga flows and compensation handlers
Bus Topology Definition:
- Assigns event routing rules, topic names, and handler mappings
- Defines retry, delay, deduplication, and dead-letter strategies

📦 Artifacts Generated¶

Artifact	Format	Description
`Events/*.json`	JSON Schema	Event contracts per service
`EventTopology.yaml`	YAML	Producer-consumer map, bus definitions
`Sagas/*.md`	Markdown	Long-running workflow patterns
`MermaidEventGraph.mmd`	Mermaid	Visual producer/consumer relationships
`Event: EventsDefined`	EventBus	Triggers API and schema generation agents

🧾 Sample Event Schema¶

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "AppointmentBooked",
  "type": "object",
  "properties": {
    "appointmentId": { "type": "string" },
    "startTime": { "type": "string", "format": "date-time" },
    "customerId": { "type": "string" }
  },
  "required": ["appointmentId", "startTime", "customerId"]
}

🗺 Sample Event Topology (Mermaid)¶

graph TD
  BookingService -->|publishes| AppointmentBooked
  NotificationService -->|consumes| AppointmentBooked
  NotificationService -->|publishes| ReminderScheduled
  AuditService -->|subscribes| AppointmentBooked

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🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant EventArchitect
  participant StudioUI
  participant EventBus

  Orchestration->>EventArchitect: Trigger Agent (BlueprintCreated)
  EventArchitect->>EventArchitect: Generate events, sagas, schemas
  EventArchitect->>StudioUI: Store diagrams and specs
  EventArchitect->>EventBus: Emit EventsDefined

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📘 Notes¶

Events are categorized:
- Domain Events (within a service boundary)
- Integration Events (across contexts)
Supports:
- Outbox Pattern for reliable publishing
- Saga Pattern for distributed workflows
Contracts are versioned and stored in Git under /contracts/

🔵 Cycle 8: API Design¶

🧩 Overview¶

This cycle involves the API Designer Agent, responsible for defining public and internal APIs for each microservice using OpenAPI (REST) and optionally gRPC. It ensures contract-first development, proper versioning, and consistent API gateway routing across the system.

The APIs serve as the formal interface for service-to-service communication, frontends, and third-party integration.

🧠 Objectives¶

Generate OpenAPI specifications for each service
Define command endpoints, query models, and resource URIs
Validate API consistency with domain models and events
Configure routing and security models for API Gateway
Produce testable, versioned, and documented API contracts

🧑‍💼 Actors¶

Actor	Role
API Designer Agent	Generates OpenAPI and/or gRPC schemas and endpoint structure
Orchestration Layer	Triggers after `EventsDefined`
API Gateway Integrator	Maps routes, rate limits, and versioning in YARP (or other gateway)
Studio UI	Visualizes and documents API structure
User (Optional)	May approve/reject specific endpoint naming or input/output structure

📝 Inputs to the Agent¶

Input	Source
`DomainModel.json`	For command and query structure
`Events/*.json`	Event payloads used in APIs
`ServiceBlueprints/*.yaml`	Adapter definitions and controller types
`EventTopology.yaml`	Determines which events have corresponding API commands

🧪 Agent Behavior¶

Endpoint Generation:
- Maps commands to POST endpoints
- Queries to GET, SEARCH, FILTER
- REST patterns: /appointments, /users/{id}, etc.
Schema Definition:
- Builds OpenAPI 3.1 specs
- Applies field-level annotations, constraints, enums
Versioning & Grouping:
- Adds API version headers and path grouping (e.g., /v1/appointments)
- Tags endpoints by context (e.g., Booking, Notifications)
API Gateway Mapping:
- Defines route config for YARP (default)
- Maps scopes, rate limits, identity claims
Contract Emission:
- Adds Swagger docs to service repo
- Exposes OpenAPI via /swagger/v1/swagger.json

📦 Artifacts Generated¶

Artifact	Format	Description
`openapi/booking-service.yaml`	YAML	REST API contract
`gateway/booking-route.yaml`	YAML	API Gateway route config
`api-metadata.json`	JSON	Version, context, producer metadata
`MermaidApiMap.mmd`	Mermaid	API-to-service diagram
`Event: ApiContractGenerated`	EventBus	Signals that API contracts are ready

🗂 Sample OpenAPI Excerpt¶

paths:
  /appointments:
    post:
      summary: "Book a new appointment"
      requestBody:
        content:
          application/json:
            schema:
              $ref: "#/components/schemas/BookAppointmentRequest"
      responses:
        200:
          description: "Appointment successfully booked"

🛡 Sample API Gateway Mapping¶

routes:
  - routeId: booking-api
    clusterId: booking-service
    match:
      path: /api/v1/appointments/{**catchAll}
    transforms:
      - PathRemovePrefix: /api/v1
    authorizationPolicy: require-user
    rateLimit:
      permitsPerSecond: 10

📉 Sample API Map (Mermaid)¶

graph TD
  Client --> API_GW
  API_GW --> BookingAPI
  API_GW --> NotificationAPI
  BookingAPI --> BookingService
  NotificationAPI --> NotificationService

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

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant ApiDesigner
  participant ApiGateway
  participant StudioUI
  participant EventBus

  Orchestration->>ApiDesigner: Trigger Agent (EventsDefined)
  ApiDesigner->>ApiGateway: Generate route mappings
  ApiDesigner->>StudioUI: Upload OpenAPI and visualizations
  ApiDesigner->>EventBus: Emit ApiContractGenerated

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

📘 Notes¶

APIs are documented and served via /swagger endpoints
Supports:
- JWT scopes and claim-based access control
- API key validation and rate limiting
- Request/response schema validation
Future: GraphQL API support, API mocking, and SDK generation

🟢 Cycle 9: Infrastructure Planning¶

🧩 Overview¶

In this cycle, the Infrastructure Planner Agent defines the cloud infrastructure required to support the solution. It provisions IaC (Infrastructure as Code) blueprints aligned with the architecture and API design from previous cycles.

The infrastructure includes compute, storage, identity, messaging, networking, and monitoring components, all prepared for CI/CD automation and deployment.

🧠 Objectives¶

Define Azure infrastructure layout per service blueprint and context
Generate Bicep (or Terraform) scripts for reproducible provisioning
Configure networking, environments (Dev, Staging, Prod), and secrets
Align resource structure to service boundaries and observability practices
Prepare IaC for integration into deployment pipelines

🧑‍💼 Actors¶

Actor	Role
Infrastructure Planner Agent	Maps services to infrastructure resources and outputs IaC templates
Orchestration Layer	Activates the agent after `ApiContractGenerated`
Cloud Provider Interface	Currently targets Azure (future: AWS, GCP)
Studio UI	Displays generated infra diagram and IaC preview
User (Optional)	May review cost estimates or override default configurations

📝 Inputs to the Agent¶

Input	Source
`ServiceBlueprints/*.yaml`	From Cycle 6
`openapi/*.yaml`	API access requirements
`EventTopology.yaml`	Messaging infrastructure
`TemplateSettings.yaml`	Default resource definitions per stack

🧪 Agent Behavior¶

Resource Mapping:
- Maps each service to compute unit (App Service, AKS, Function App)
- Allocates storage (Blob, SQL, CosmosDB, etc.)
- Assigns messaging resources (Azure Service Bus, Event Grid)
Environment Scaffolding:
- Creates isolated setups for Dev, Staging, and Prod
- Applies resource tagging and naming conventions
Networking & Identity:
- Provisions virtual networks, private endpoints, app gateway
- Sets up Key Vaults, managed identities, and role assignments
Monitoring Setup:
- Configures Log Analytics, Application Insights, and alerts
IaC Generation:
- Outputs parameterized Bicep scripts (default) for full deployment
- Includes main.bicep, parameters.json, outputs.json

📦 Artifacts Generated¶

Artifact	Format	Description
`infrastructure/main.bicep`	Bicep	Root IaC file for entire solution
`infra/parameters.dev.json`	JSON	Dev environment values
`infra/azure-resources.yaml`	YAML	Logical resource map
`MermaidInfraDiagram.mmd`	Mermaid	Infra layout per environment
`Event: InfraPlanCreated`	EventBus	Triggers CI/CD Pipeline Setup

🛠 Sample Infra YAML (`azure-resources.yaml`)¶

booking-service:
  appServicePlan: S1
  appService: booking-api
  database: sql-booking-db
  storage: blob-booking-storage
  messaging: service-bus-booking
  keyVault: kv-booking

🖼 Mermaid Infra Diagram¶

graph TD
  subgraph Azure
    A[App Service: booking-api]
    B[SQL DB: booking-db]
    C[Service Bus: booking-events]
    D[Blob Storage: files]
    E[Key Vault]
    F[Application Insights]
  end

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

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🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant InfraAgent
  participant StudioUI
  participant EventBus

  Orchestration->>InfraAgent: Trigger Agent (ApiContractGenerated)
  InfraAgent->>InfraAgent: Generate IaC and resource map
  InfraAgent->>StudioUI: Show infra diagram and scripts
  InfraAgent->>EventBus: Emit InfraPlanCreated

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

📘 Notes¶

Resource configurations follow the ConnectSoft Cloud-Native Standards:
- Secure by default
- Observability enabled
- Least-privilege identity
- Multi-environment separation
All resource definitions are parameterized and versioned
Future: cost estimation report, cloud provider selection

🔴 Cycle 10: DevOps Bootstrap¶

🧩 Overview¶

This cycle activates the DevOps Agent, which prepares the complete CI/CD infrastructure required to continuously build, test, package, and deploy the solution. The pipelines are generated in YAML format and integrated with the previously defined infrastructure and repositories.

This step also initializes deployment environments and connects them to Azure DevOps for traceable, automated deployments across Dev, Staging, and Production.

🧠 Objectives¶

Generate YAML pipelines for each service and shared library
Configure Azure DevOps pipelines, agents, and environments
Integrate with IaC scripts and deployment manifests
Setup environment variables, secrets, and gates
Enable build validations, test runs, and artifact publishing

🧑‍💼 Actors¶

Actor	Role
DevOps Agent	Creates and configures CI/CD workflows across environments
Orchestration Layer	Triggers after `InfraPlanCreated`
Azure DevOps Integrator	Registers environments, pipelines, agents
Template Engine	Uses standardized pipeline templates for ConnectSoft projects

📝 Inputs to the Agent¶

Input	Source
`main.bicep`	From Infrastructure Planning
`ServiceBlueprints/*.yaml`	Defines which services require deployment flows
`api/openapi.yaml`	Determines when to validate contracts
`TemplateSettings.yaml`	Default pipeline structure and tasks
`EnvironmentConfig.yaml`	Maps `dev`, `staging`, `prod` to Azure resources

🧪 Agent Behavior¶

Pipeline Scaffolding:
- Generates azure-pipelines.yml for each service
- Adds stages: build, test, package, deploy
Environment Setup:
- Creates Azure DevOps environments: Dev, Staging, Prod
- Configures approvals, checks, and gates (e.g., manual signoff in Prod)
Secret & Config Wiring:
- Connects Key Vault references for secrets and tokens
- Adds variables files per environment (e.g., dev.env, prod.env)
CI Validations:
- Triggers test execution: MSTest, SpecFlow, static analysis
- Optionally publishes NuGet packages and container images
Pipeline Registration:
- Registers pipeline YAML in repo root
- Links Azure DevOps to Git repo and triggers builds

📦 Artifacts Generated¶

Artifact	Format	Description
`azure-pipelines.yml`	YAML	Root pipeline definition for entire solution
`pipelines/booking.yml`	YAML	Service-specific pipeline (used via templates)
`env/dev.env`	Key=Value	Environment variables for dev
Azure DevOps Pipelines	GUI + JSON	Auto-registered with triggers and tasks
`Event: DevOpsReady`	EventBus	Signals readiness for code scaffolding and commits

🧪 Sample Pipeline Snippet (YAML)¶

trigger:
  branches:
    include:
      - main

stages:
  - stage: Build
    jobs:
      - job: Build
        steps:
          - task: UseDotNet@2
            inputs:
              packageType: sdk
              version: '8.x'
          - script: dotnet build
  - stage: Test
    jobs:
      - job: Test
        steps:
          - script: dotnet test --logger trx
  - stage: Deploy_Dev
    condition: succeeded()
    dependsOn: Test
    jobs:
      - deployment: DeployDev
        environment: dev
        strategy:
          runOnce:
            deploy:
              steps:
                - script: az deployment group create ...

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant DevOpsAgent
  participant AzureDevOps
  participant EventBus
  participant StudioUI

  Orchestration->>DevOpsAgent: Trigger Agent (InfraPlanCreated)
  DevOpsAgent->>AzureDevOps: Create pipelines, environments
  DevOpsAgent->>StudioUI: Show pipeline and env details
  DevOpsAgent->>EventBus: Emit DevOpsReady

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

📘 Notes¶

Default Azure DevOps templates support:
- .NET 8, MSTest, SpecFlow, NHibernate
- Container builds via Docker (future: AKS or App Service deployment)
- NuGet publishing and artifact staging
Pipeline stages are reusable and modular, supporting:
- Feature branch triggers
- Pull request validation
- Deployment approvals

🟣 Cycle 11: Backend Implementation¶

🧩 Overview¶

In this cycle, the Engineering Agent (primarily the Backend Developer Agent) begins the implementation of microservices. Based on the previously generated architecture, events, APIs, and templates, the agent scaffolds and commits production-grade code using the ConnectSoft Microservice Template.

Each service is created with Clean Architecture principles, domain models, handlers, adapters, and test projects.

🧠 Objectives¶

Scaffold microservices using domain models and service blueprints
Implement aggregates, command handlers, and use cases
Wire in event publishers, repositories, and validation logic
Integrate the microservice into CI/CD, API Gateway, and runtime configuration
Commit all code into Azure DevOps with full traceability

🧑‍💼 Actors¶

Actor	Role
Backend Developer Agent	Generates core logic, service structure, handlers, and contracts
Orchestration Layer	Activates after `DevOpsReady`
Microservice Template Engine	Generates structured projects using ConnectSoft templates
Azure DevOps Integrator	Commits code, pushes branches, triggers builds

📝 Inputs to the Agent¶

Input	Source
`ServiceBlueprints/*.yaml`	Defines service responsibilities, structure, adapters
`DomainModel.json`	Entities, aggregates, value objects
`Events/*.json`	Event payloads to publish
`openapi/*.yaml`	Endpoint contracts
`TemplateSettings.yaml`	Selected project scaffolding options

🧪 Agent Behavior¶

Scaffold Solution Structure:
- Uses ConnectSoft Microservice Template
- Projects: Domain, Application, Infrastructure, API, Tests
Generate Domain Code:
- Creates Aggregates and Value Objects
- Implements domain events and invariants
Use Case Implementation:
- Writes Command/Query Handlers (e.g., BookAppointmentHandler)
- Adds Mediator request/response structure
Repository & Adapter Setup:
- Implements NHibernate or EF Core repositories
- Connects to Azure SQL or Cosmos DB
Event Publishing:
- Configures MassTransit publishers with outbox pattern
- Adds message contracts and bus registration
Validation & Logging:
- Integrates FluentValidation, Serilog, exception handling filters
Testing Project Setup:
- Adds MSTest or SpecFlow-based unit/integration tests
- Generates base test data and mocks

📦 Artifacts Generated¶

Artifact	Format	Description
`/src/BookingService`	Code	Clean Architecture microservice
`/tests/BookingService.Tests`	Code	Unit and integration test suite
`README.md`, `CONTRIBUTING.md`	Markdown	Auto-generated docs
Azure DevOps Git Commits	Git	Code + CI triggers + work item links
`Event: MicroserviceScaffolded`	EventBus	Triggers pull request + validation stage

📁 Folder Structure (Example)¶

BookingService/
├── BookingService.Domain/
├── BookingService.Application/
├── BookingService.Infrastructure/
├── BookingService.API/
├── BookingService.Tests/
├── BookingService.sln
└── README.md

🧪 Sample Command Handler¶

public class BookAppointmentHandler : IRequestHandler<BookAppointmentCommand, Guid>
{
    public async Task<Guid> Handle(BookAppointmentCommand command, CancellationToken ct)
    {
        var appointment = Appointment.Create(command.PatientId, command.StartTime);
        await _repository.SaveAsync(appointment, ct);
        await _eventPublisher.PublishAsync(new AppointmentBooked(appointment.Id), ct);
        return appointment.Id;
    }
}

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant BackendAgent
  participant TemplateEngine
  participant AzureDevOps
  participant EventBus

  Orchestration->>BackendAgent: Trigger Agent (DevOpsReady)
  BackendAgent->>TemplateEngine: Use Microservice Template
  BackendAgent->>AzureDevOps: Commit to Git + Push Branch
  BackendAgent->>EventBus: Emit MicroserviceScaffolded

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📘 Notes¶

Microservices follow the ConnectSoft Microservice Standard Blueprint:
- Clean Architecture
- Mediator Pattern
- MassTransit + Azure Service Bus
- NHibernate or EF Core
CI pipelines run on push, triggering build, test, and deploy to dev
Code is linked to Azure Boards work items for traceability

🔷 Cycle 12: Frontend & UX Design¶

🧩 Overview¶

In this cycle, the UX Designer Agent and Frontend Developer Agent collaborate to define and generate the user experience layer of the solution. Based on the previously defined personas, product plan, and APIs, they create wireframes, page flows, and scaffold the frontend application.

The frontend integrates directly with the APIs and supports responsive design, accessibility, and optional multi-tenant theming.

🧠 Objectives¶

Define user interface flows per persona and use case
Generate low/high-fidelity wireframes and UI structures
Scaffold frontend project using selected technology (e.g., Blazor, Angular)
Connect UI actions to backend APIs using generated OpenAPI specs
Apply theming, i18n, and accessibility best practices

🧑‍💼 Actors¶

Actor	Role
UX Designer Agent	Creates wireframes, user journeys, and UI models
Frontend Developer Agent	Scaffolds and implements UI components and app shell
Orchestration Layer	Triggers after `MicroserviceScaffolded`
Studio UI	Allows review and editing of wireframes
User (Optional)	Can provide style preferences, branding, or review UX output

📝 Inputs to the Agents¶

Input	Source
`ProductPlan.json`	Features and goals per persona
`openapi/*.yaml`	APIs to be consumed
`Personas.json`	User types and empathy maps
`BrandingConfig.yaml`	Optional theming and color palette
`FrontendTemplateSettings.yaml`	Technology choice and layout modes

🧪 Agent Behavior¶

UX Flow Modeling:
- Creates persona-specific user journey maps
- Generates page flow diagrams and navigation hierarchy
Wireframe Generation:
- Produces wireframes (markdown, image, or Figma export)
- Includes placeholders for actions, forms, components
UI Project Scaffolding:
- Uses Blazor or Angular frontend template
- Sets up routing, authentication, layouts, shared components
API Integration:
- Generates API client from OpenAPI (NSwag, AutoRest, etc.)
- Connects UI forms and actions to backend endpoints
i18n, Accessibility, and Theming:
- Adds localization scaffolding
- Applies WCAG-compliant component practices
- Supports light/dark mode and tenant theming if enabled

📦 Artifacts Generated¶

Artifact	Format	Description
`wireframes/*.md`	Markdown	UI structure per user flow
`journeys/*.mmd`	Mermaid	User journey diagrams
`/webapp/`	Code	Full frontend solution
`ui-components/`	Code	Shared controls, themes, services
`Event: FrontendScaffolded`	EventBus	Signals UI layer is ready for QA and testing

📁 Folder Structure (Blazor Example)¶

webapp/
├── Pages/
│   ├── BookAppointment.razor
│   ├── Dashboard.razor
│   └── ...
├── Components/
│   ├── DatePicker.razor
│   ├── NotificationPanel.razor
├── Services/
│   └── ApiClient.cs
├── Shared/
│   └── MainLayout.razor
├── wwwroot/
│   └── styles.css
├── Program.cs
└── App.razor

🖼 Sample User Journey Map (Mermaid)¶

journey
  title Book Appointment Flow
  section Pet Owner
    Start -> Login -> Select Date -> Confirm Appointment
  section System
    Validate User -> Query Availability -> Create Appointment -> Send Confirmation

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

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant UXAgent
  participant FrontendAgent
  participant StudioUI
  participant EventBus

  Orchestration->>UXAgent: Trigger UX Design (MicroserviceScaffolded)
  UXAgent->>FrontendAgent: Provide wireframes + flows
  FrontendAgent->>StudioUI: Commit UI Project
  FrontendAgent->>EventBus: Emit FrontendScaffolded

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

📘 Notes¶

Frontend uses ConnectSoft’s UI Framework Standards, including:
- Responsive layouts (Tailwind CSS or Angular Material)
- Localizable UI text
- Typed API clients generated from backend contracts
Future: optional design system alignment (Figma → Code), telemetry UI overlays, UX A/B testing plans

🟤 Cycle 13: Database Modeling¶

🧩 Overview¶

In this cycle, the Database Architect Agent transforms domain models and aggregates into persistent relational or document models. The agent generates database mappings (e.g., NHibernate, EF Core) and schema definitions, aligning them with the bounded contexts and service ownership.

Each service receives its own dedicated schema or database (per DDD and multi-tenant principles), and mappings are optimized for transactional consistency and performance.

🧠 Objectives¶

Map domain entities and aggregates to persistence models
Generate NHibernate (or EF Core) mappings and schemas
Define entity relationships, constraints, and value object flattening
Support multi-tenancy, migrations, and dev/test seeding
Emit artifacts for integration into backend and CI pipelines

🧑‍💼 Actors¶

Actor	Role
Database Architect Agent	Generates database structure and object-relational mappings
Orchestration Layer	Triggers after `FrontendScaffolded`
Backend Agent	Integrates generated mappings into infrastructure layer
Studio UI	Shows entity-relationship diagrams (ERDs) and schema DDL
User (Optional)	May adjust naming conventions or DB engine choice

📝 Inputs to the Agent¶

Input	Source
`DomainModel.json`	Aggregates, entities, value objects
`ServiceBlueprints/*.yaml`	Per-service boundaries
`TemplateSettings.yaml`	Default ORM, DB provider (NHibernate + SQL default)
`MultiTenantConfig.yaml`	DB-per-tenant or schema-per-tenant

🧪 Agent Behavior¶

Entity Mapping Generation:
- Maps aggregates to tables
- Value objects: embedded or flattened fields
- Adds primary keys, uniqueness constraints, indexes
Relationship Definition:
- Identifies one-to-many, many-to-one, many-to-many relationships
- Defines foreign key constraints and cascading behaviors
ORM Mapping Emission:
- For NHibernate:
  - .hbm.xml mappings or fluent config
- For EF Core:
  - DbContext, OnModelCreating logic
Migration & Seeding:
- Generates migration scripts (e.g., V1__Initial.sql)
- Optional dev/test seed data for QA
Schema Documentation:
- Produces ERD diagrams and entity metadata

📦 Artifacts Generated¶

Artifact	Format	Description
`NHibernateMappings/*.hbm.xml`	XML	Table-to-entity mapping files
`DbContext.cs`, `FluentMappings.cs`	C#	EF/NHibernate class mappings
`Schema.sql`	SQL	Initial schema DDL script
`SeedData.json`	JSON	Optional dev/test data
`MermaidERD.mmd`	Mermaid	Entity-relationship diagram
`Event: DatabaseModelCreated`	EventBus	Signals DB layer is ready for integration testing

🗄 Example Entity Mapping (NHibernate)¶

<class name="Appointment" table="Appointments">
  <id name="Id" column="AppointmentId" type="Guid">
    <generator class="guid" />
  </id>
  <property name="StartTime" column="StartTime" type="DateTime"/>
  <property name="Duration" column="Duration" type="int"/>
  <many-to-one name="Customer" column="CustomerId" class="Customer"/>
</class>

🗺 Sample ER Diagram (Mermaid)¶

erDiagram
  Appointment ||--o{ Customer : has
  Appointment {
    Guid AppointmentId
    DateTime StartTime
    int Duration
  }
  Customer {
    Guid CustomerId
    string FullName
  }

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

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant DbArchitect
  participant BackendAgent
  participant StudioUI
  participant EventBus

  Orchestration->>DbArchitect: Trigger Agent (FrontendScaffolded)
  DbArchitect->>BackendAgent: Send mappings
  DbArchitect->>StudioUI: Render ERD and schema preview
  DbArchitect->>EventBus: Emit DatabaseModelCreated

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

📘 Notes¶

Each service uses separate schema by default (recommended for modular SaaS)
Table and column names follow snake_case or PascalCase conventions (configurable)
Future support: document stores (e.g., CosmosDB), time-series data, NoSQL mapping layers
Migration support can be integrated into CI/CD (Flyway, DbUp, or EF Migrations)

🔶 Cycle 14: Testing Plan Generation¶

🧩 Overview¶

In this cycle, the QA Strategy Agent defines a complete and traceable testing strategy for the entire solution, covering unit, integration, contract, behavioral, and performance testing layers. The plan aligns with the product goals, APIs, and domain models, and prepares executable test projects and scenarios for automation.

The output ensures quality assurance is embedded from the first commit and throughout all CI/CD stages.

🧠 Objectives¶

Define test types, scopes, and responsibilities
Generate unit tests, integration tests, and BDD feature files
Create test coverage maps linking features to tests
Configure test runners, CI validation stages, and dashboards
Enable future load testing, security testing, and AI-assisted QA

🧑‍💼 Actors¶

Actor	Role
QA Strategy Agent	Creates and scaffolds test plans and specs
Orchestration Layer	Triggers after `DatabaseModelCreated`
Backend & Frontend Agents	Consume and run generated test code
Studio UI	Displays test plan, specs, and coverage matrices
User (Optional)	May contribute domain-specific edge cases or test rules

📝 Inputs to the Agent¶

Input	Source
`ProductPlan.json`	Goals and outcomes to validate
`openapi/*.yaml`	Contract testing targets
`DomainModel.json`	Entities and business rules
`UseCases/*.cs`	Handlers and expected behaviors
`UI Wireframes`	Frontend flow test targets
`CI Pipeline Config`	Defines test stages and environments

🧪 Agent Behavior¶

Test Strategy Definition:
- Defines testing pyramid: unit, integration, e2e, perf
- Assigns responsibility per layer: API, domain, UI, messaging
Spec Generation:
- Creates:
  - Unit test classes (e.g., BookAppointmentHandlerTests)
  - API integration test classes
  - SpecFlow BDD .feature files with scenarios
CI Test Integration:
- Adds test jobs to pipeline (dotnet test, reportgenerator, etc.)
- Publishes test results and coverage to Azure DevOps
Test Documentation:
- Produces a matrix: Features ↔ Tests ↔ Assertions
- Generates Markdown summaries and traceability links

📦 Artifacts Generated¶

Artifact	Format	Description
`tests/BookingService.Tests`	C# Code	Unit & integration test classes
`features/*.feature`	Gherkin	SpecFlow BDD scenarios
`test-plan.md`	Markdown	QA scope, methodology, strategy
`coverage-report/`	HTML + JSON	Test coverage visual report
`test-matrix.csv`	CSV	Traceability between features and tests
`Event: TestingPlanReady`	EventBus	Signals system is testable and verified

🧪 Sample SpecFlow Feature¶

Feature: Book Appointments
  In order to allow pet owners to reserve appointments
  As a clinic admin
  I want to create appointment entries via the booking service

Scenario: Booking a valid appointment
  Given a pet owner with ID "abc123"
  And a free slot on "2025-05-10T15:00"
  When the user books the appointment
  Then the appointment should be saved and confirmed

🧪 Sample Test Class (C#)¶

[TestClass]
public class BookAppointmentHandlerTests
{
    [TestMethod]
    public async Task Should_Save_And_Emit_Event_When_Valid()
    {
        var command = new BookAppointmentCommand(...);
        var result = await handler.Handle(command, CancellationToken.None);
        Assert.IsTrue(result != Guid.Empty);
        eventBus.Received(1).PublishAsync(Arg.Any<AppointmentBooked>());
    }
}

🧪 Sample Matrix (CSV)¶

Feature	Test Class	Scenario / Assertion
Book Appointment (API)	`BookAppointmentHandlerTests`	Valid booking triggers event
View Schedule (UI)	`ViewScheduleTests`	Calendar shows upcoming bookings
Cancel Appointment	`CancelAppointmentTests`	Removes record and emits event

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant QAAgent
  participant StudioUI
  participant EventBus

  Orchestration->>QAAgent: Trigger Agent (DatabaseModelCreated)
  QAAgent->>StudioUI: Publish test plan, specs, and coverage links
  QAAgent->>EventBus: Emit TestingPlanReady

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

📘 Notes¶

Testing strategy complies with ConnectSoft standards:
- AAA pattern for unit tests
- SpecFlow for human-readable behavior specs
- CI-integrated coverage and test result reporting
Coverage goals:
- 90%+ unit test coverage for core services
- 100% feature-to-test traceability for MVP
Future: AI-guided test case generation, mutation testing, chaos testing

Would you like to continue with Cycle 15: Security Hardening, where agents analyze the system for vulnerabilities, define auth policies, and inject secure-by-default configurations?

Here is the documentation for Cycle 15: Security Hardening in the ConnectSoft AI Software Factory lifecycle.

🔐 Cycle 15: Security Hardening¶

🧩 Overview¶

This cycle introduces the Security Architect Agent, responsible for applying Security-First Design across all layers of the system. It ensures that authentication, authorization, secrets, and secure communication are embedded from the start. The agent also integrates policies for data protection, identity management, and compliance (e.g., GDPR, HIPAA-ready if configured).

This step is critical to guarantee that the SaaS product is secure by default.

🧠 Objectives¶

Define and implement authentication and authorization policies
Configure API security, CORS, rate limiting, and input validation
Secure storage, message buses, and database connections
Inject secrets management and environment isolation strategies
Emit policies for DevSecOps, penetration testing, and compliance

🧑‍💼 Actors¶

Actor	Role
Security Architect Agent	Applies system-wide security strategies and configurations
Orchestration Layer	Triggers after `TestingPlanReady`
API Gateway Integrator	Applies token validation and scopes
Identity Agent	Provides auth server, claims mapping, and user identity scaffolding
User (Optional)	Can specify external IdP, token lifetimes, or access levels

📝 Inputs to the Agent¶

Input	Source
`openapi/*.yaml`	Endpoint exposure and method protection needs
`ServiceBlueprints/*.yaml`	Adapter types and external access needs
`TemplateSettings.yaml`	Identity model, token configuration
`FrontendTemplateSettings.yaml`	CORS, login UI, token injection settings

🧪 Agent Behavior¶

Authentication Setup:
- Integrates OpenIddict-based Auth Server (ConnectSoft standard)
- Supports OAuth2 / OpenID Connect flows (PKCE, Client Credentials)
- Issues access tokens (JWT) with audience/scope enforcement
Authorization Policies:
- Applies [Authorize] attributes with policies per controller/action
- Maps endpoints to scopes and roles (e.g., booking.read, admin.write)
Gateway & CORS Configuration:
- Sets up:
  - Token validation (via introspection or JWKS)
  - CORS policy (whitelist domains, methods, headers)
  - Rate limiting (per route, per IP, per tenant)
Secrets & Identity Isolation:
- Injects Azure Key Vault references in services and pipelines
- Assigns Managed Identities or service principals where needed
Static & Runtime Protections:
- Enables anti-CSRF middleware
- Adds input sanitization and exception filters
- Registers validation filters and error masking

📦 Artifacts Generated¶

Artifact	Format	Description
`SecurityPolicies.yaml`	YAML	Full authZ/authN mapping per endpoint
`gateway/security-routes.yaml`	YAML	Scopes, validation, CORS, rate limiting
`KeyVaultBindings.json`	JSON	Secure config and secret references
`IdentityServerConfig.cs`	Code	Clients, resources, token settings
`Event: SecurityHardened`	EventBus	Signals platform is hardened and audit-ready

🔐 Example Security Policy Snippet¶

api: booking-service
endpoints:
  - path: /appointments
    method: POST
    scopes: [booking.write]
    roles: [PetOwner]
    rateLimit: 10r/s
    cors:
      origins: ["https://app.customer.com"]
      methods: ["GET", "POST"]

🔐 Identity Server Client (OpenIddict)¶

new OpenIddictApplicationDescriptor
{
    ClientId = "frontend",
    DisplayName = "Main UI",
    RedirectUris = { "https://app.customer.com/signin-oidc" },
    Permissions =
    {
        Permissions.Endpoints.Authorization,
        Permissions.GrantTypes.AuthorizationCode,
        Permissions.ResponseTypes.Code,
        Permissions.Scopes.Profile,
        Permissions.Scopes.Email
    }
}

🧰 CI/CD Security Enhancements¶

YAML pipeline includes:
- Credential scans using DevSkim/Gitleaks
- Secrets injection from Key Vault
- Static analysis for security vulnerabilities
- Token leak protection

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant SecurityAgent
  participant IdentityAgent
  participant Gateway
  participant EventBus

  Orchestration->>SecurityAgent: Trigger Agent (TestingPlanReady)
  SecurityAgent->>IdentityAgent: Register clients and scopes
  SecurityAgent->>Gateway: Inject security rules
  SecurityAgent->>EventBus: Emit SecurityHardened

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

📘 Notes¶

OpenAPI is enriched with security blocks per operation
All token issuance and validation settings are traceable to IdentityConfig
Secrets and sensitive configs never stored in plain text — Key Vault binding enforced
Future: penetration testing simulation agent, compliance alignment (SOC2, HIPAA)

📘 Cycle 16: Documentation & Developer Portal¶

🧩 Overview¶

In this cycle, the Documentation Agent and Developer Experience (DX) Agent generate and publish all technical, functional, and operational documentation for the solution. This includes API references, domain models, developer onboarding, and a complete developer portal powered by MkDocs.

This step transforms the system into a self-service platform for internal teams, customers, and integrators.

🧠 Objectives¶

Generate a full developer portal using MkDocs + Material
Create structured docs for:
- APIs, events, domain models, architecture
- Setup guides, usage patterns, CI/CD flows
Publish SDKs, README.md, and dev environment guides
Ensure cross-linking and traceability across features, services, and repos

🧑‍💼 Actors¶

Actor	Role
Documentation Agent	Generates markdown docs, diagrams, and reference content
DX Agent	Builds onboarding guides, SDKs, and usage instructions
Orchestration Layer	Triggers after `SecurityHardened`
Studio UI	Allows previewing and editing of generated documentation

📝 Inputs to the Agent¶

Input	Source
`openapi/*.yaml`	API references
`DomainModel.json`, `ContextMap.json`	For architecture and domain docs
`ServiceBlueprints/*.yaml`	For service-specific pages
`Events/*.json`	For messaging docs
`CI/CD Pipelines`	For operational/deployment guide generation

🧪 Agent Behavior¶

MkDocs Site Scaffolding:
- Creates /docs folder with full hierarchy:
  - Introduction
  - Services
  - APIs
  - Events
  - Architecture
  - Setup
  - Environments
API & Domain Reference Generation:
- Auto-generates REST API docs from OpenAPI
- Visualizes domain models (Mermaid, diagrams)
- Includes command/handler/event mappings
SDK & Code Examples:
- Generates sample requests and .http files
- (Optional) emits C#/JavaScript SDK stubs using NSwag/AutoRest
Developer Onboarding:
- Provides environment setup guides (local + cloud)
- Explains CI/CD, branching, secrets, testing
Publication & Hosting:
- Builds static MkDocs site
- Pushes to Azure Static Web Apps or GitHub Pages
- Links to main Azure DevOps repo

📦 Artifacts Generated¶

Artifact	Format	Description
`/docs/`	Markdown	All documentation files (site source)
`/site/`	HTML	Built MkDocs site for local or cloud hosting
`mkdocs.yml`	YAML	Site navigation and metadata
`Postman Collection.json`	JSON	Importable API requests
`README.md`	Markdown	Service-specific README files
`Event: DocumentationReady`	EventBus	Signals all documentation is published

📁 Sample MkDocs Structure¶

docs/
├── index.md
├── architecture/
│   ├── clean-architecture.md
│   └── context-map.md
├── services/
│   ├── booking-service.md
│   ├── notification-service.md
├── api/
│   ├── booking-api.md
│   └── auth-api.md
├── events/
│   └── appointment-booked.md
├── devops/
│   ├── ci-pipeline.md
│   ├── deployment-guide.md
├── guides/
│   ├── local-setup.md
│   ├── sdk-usage.md
mkdocs.yml

🧰 API Reference Snippet¶

## POST /appointments

Books a new appointment.

### Request Body
```json
{
  "customerId": "string",
  "startTime": "2025-05-10T14:00:00Z"
}

Response¶

{
  "appointmentId": "guid"
}

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant DocumentationAgent
  participant DXAgent
  participant StudioUI
  participant EventBus

  Orchestration->>DocumentationAgent: Trigger Agent (SecurityHardened)
  DocumentationAgent->>DXAgent: Generate SDKs, onboarding guides
  DocumentationAgent->>StudioUI: Publish docs site
  DocumentationAgent->>EventBus: Emit DocumentationReady
````

---

### 📘 Notes

* Uses **Material for MkDocs** (dark/light mode, search, tabs)
* Full documentation is version-controlled and **deployable with CI**
* Traceability between use cases → services → endpoints → code → tests
* Future: plugin for Mermaid live diagrams, Figma sync, and API test runners

---

## 🔗 Cycle 17: Integration Setup

### 🧩 Overview

This cycle introduces the **Integration Agent**, responsible for preparing and configuring connections to **external systems** such as payment gateways, messaging platforms, identity providers, analytics tools, and CRMs. The agent ensures that all outbound and inbound integration points are designed with **abstraction**, **resiliency**, and **security** in mind.

These integrations follow ConnectSoft’s plug-and-play adapter model and are declaratively configurable.

---

### 🧠 Objectives

* Discover, define, and scaffold **third-party service integrations**
* Generate **integration adapters**, abstraction interfaces, and HTTP clients
* Configure authentication flows (OAuth2, API keys, webhooks)
* Enable **infrastructure bindings**, retry logic, and observability
* Document integration contracts, schemas, and test flows

---

### 🧑‍💼 Actors

| Actor                   | Role                                                                                              |
| ----------------------- | ------------------------------------------------------------------------------------------------- |
| **Integration Agent**   | Implements and documents integrations with external systems                                       |
| **Orchestration Layer** | Triggers after `DocumentationReady`                                                               |
| **Security Agent**      | Shares secret injection rules and key vault paths                                                 |
| **User (Optional)**     | Selects integrations (e.g., Stripe, Twilio, SendGrid, etc.) and provides credentials or test keys |

---

### 📝 Inputs to the Agent

| Input                      | Source                                             |
| -------------------------- | -------------------------------------------------- |
| `ProductPlan.json`         | Integration needs based on features                |
| `IntegrationSettings.yaml` | Declared third-party platforms                     |
| `SecretsManifest.json`     | Credentials for APIs (test or live)                |
| `openapi/*.yaml`           | Used to wire up outgoing calls from services       |
| `TemplateSettings.yaml`    | Integration patterns and error handling strategies |

---

### 🧪 Agent Behavior

1. **Integration Discovery**:
     * Matches features to common integrations (e.g., “Send Email” → SendGrid)
     * Parses declared integrations (`IntegrationSettings.yaml`)
1. **Adapter Generation**:
     * Generates:
         * Interface (e.g., `INotificationSender`)
         * Implementation (e.g., `SendGridEmailAdapter`)
         * Configuration settings and DI bindings
1. **Authentication & Secrets**:
     * Supports:
         * API Key (header or query param)
         * OAuth2 (client credentials, PKCE)
         * Webhook signatures (HMAC)
     * Injects secrets via Azure Key Vault or managed identity
1. **Error Handling & Retry**:
     * Adds Polly-based retry + fallback + circuit breaker logic
     * Validates timeout and throttling policies
1. **Integration Test Setup**:
     * Generates `.http` request test files and Postman examples
     * Adds integration tests to CI pipeline if flagged

---

### 📦 Artifacts Generated

| Artifact                           | Format   | Description                                          |
| ---------------------------------- | -------- | ---------------------------------------------------- |
| `Adapters/SendGridEmailAdapter.cs` | Code     | Integration class with abstraction                   |
| `IExternalService.cs`              | Code     | Contract/interface for consuming service             |
| `SecretsConfig.json`               | JSON     | Mapped keys to Key Vault entries                     |
| `test/integrations/sendgrid.http`  | HTTP     | Test call sample                                     |
| `integration-docs/sendgrid.md`     | Markdown | Usage, credentials, expected formats                 |
| `Event: IntegrationsConfigured`    | EventBus | Triggers downstream simulation or deployment testing |

---

### 📂 Folder Structure (Partial)

```plaintext
Integration/
├── Interfaces/
│   └── IEmailSender.cs
├── Adapters/
│   └── SendGridEmailAdapter.cs
├── Configuration/
│   └── SendGridOptions.cs
├── Docs/
│   └── sendgrid.md

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

🔐 Example Adapter with Retry¶

public class SendGridEmailAdapter : IEmailSender
{
    public async Task SendAsync(EmailMessage message, CancellationToken ct)
    {
        var retryPolicy = Policy
            .Handle<HttpRequestException>()
            .OrResult<HttpResponseMessage>(r => !r.IsSuccessStatusCode)
            .WaitAndRetryAsync(3, retryAttempt => TimeSpan.FromSeconds(2));

        await retryPolicy.ExecuteAsync(async () =>
        {
            var request = BuildHttpRequest(message);
            var response = await _httpClient.SendAsync(request, ct);
            response.EnsureSuccessStatusCode();
        });
    }
}

📘 Example Integration Doc¶

# SendGrid Integration

## Purpose
Used by NotificationService to send transactional emails.

## Authentication
- Method: API Key (Header `Authorization: Bearer`)
- Secret stored in: Azure Key Vault → `SendGridApiKey`

## Endpoints
- `POST https://api.sendgrid.com/v3/mail/send`

## Retry Policy
- Max 3 attempts
- 2s delay with exponential backoff

## Usage
Call `IEmailSender.SendAsync(...)`

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant IntegrationAgent
  participant StudioUI
  participant EventBus

  Orchestration->>IntegrationAgent: Trigger Agent (DocumentationReady)
  IntegrationAgent->>StudioUI: Push adapter code and config
  IntegrationAgent->>EventBus: Emit IntegrationsConfigured

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

📘 Notes¶

Integrations follow adapter + abstraction pattern
Each is unit-testable and mockable (interfaces are injected)
Secure by design:
- No secret in code
- Built-in validation and fail-safe logic
Future: Marketplace of pluggable integration blueprints (e.g., Braze, Stripe, HubSpot)

📊 Cycle 18: Observability Setup¶

🧩 Overview¶

In this cycle, the Observability Agent equips all services with deep telemetry, logging, tracing, and health checking capabilities. This includes automatic configuration of structured logs, distributed tracing, metrics collection, and dashboards using standard ConnectSoft practices.

The goal is to make the system fully transparent, debuggable, and production-ready from day one.

🧠 Objectives¶

Enable structured, centralized logging across all services
Configure distributed tracing using OpenTelemetry
Add metrics exporters and health check endpoints
Provision default Grafana dashboards and Azure Monitor alerts
Ensure observability is CI-integrated, tenant-aware, and scalable

🧑‍💼 Actors¶

Actor	Role
Observability Agent	Configures logging, tracing, metrics, and dashboards
Orchestration Layer	Triggers after `IntegrationsConfigured`
DevOps Agent	Injects observability steps into CI/CD pipelines
User (Optional)	Can request additional metrics, alert rules, or custom visualizations

📝 Inputs to the Agent¶

Input	Source
`ServiceBlueprints/*.yaml`	To determine health check endpoints and dependencies
`TemplateSettings.yaml`	Observability preferences (e.g., tracing system, log format)
`InfrastructurePlan.yaml`	Target Azure Monitor, Grafana, or Elastic stack
`Events/*.json`	To emit operational telemetry from message flows

🧪 Agent Behavior¶

Logging Setup:
- Configures Serilog with:
  - Console
  - File (rotated)
  - Azure Monitor or Seq sink
  - Structured enrichment (tenant, correlationId, context)
Tracing Injection:
- Adds OpenTelemetry + exporters (Azure Monitor / Jaeger / Zipkin)
- Propagates trace IDs via HTTP and messaging
- Registers tracing middleware in all services
Health & Readiness Probes:
- Adds /healthz, /ready, and /live endpoints
- Registers checks for database, message bus, secrets, etc.
Metrics Collection:
- Integrates Prometheus exporters
- Emits custom metrics (e.g., appointments_booked_total)
- Optionally enables Application Insights
Dashboards & Alerts:
- Generates:
  - Grafana dashboard templates
  - Alert rules (HTTP 500 rate, CPU usage, message failures)
  - Azure Monitor workbooks (if selected)

📦 Artifacts Generated¶

Artifact	Format	Description
`observability/logging.json`	JSON	Serilog config (sinks, formats)
`otel-config.yaml`	YAML	OpenTelemetry setup
`metrics/prometheus-*.yml`	YAML	Metric definitions
`dashboards/*.json`	JSON	Grafana dashboards per service
`alerts/azure-monitor.json`	JSON	Alert rules
`Event: ObservabilityReady`	EventBus	Signals that system is monitorable and compliant

📁 Folder Structure¶

observability/
├── logging.json
├── otel-config.yaml
├── dashboards/
│   ├── booking-service.json
│   └── api-gateway.json
├── metrics/
│   ├── appointments_metrics.yml
├── alerts/
│   └── high-error-rate.json

📉 Sample Grafana Panels (Booking Service)¶

Appointments Booked (counter)
API Success vs Failure (%)
Event Processing Time (histogram)
Health Status Timeline
Dependency Latency (tracing span duration)

🩺 Sample Health Check Endpoint¶

{
  "status": "Healthy",
  "checks": [
    { "name": "Database", "status": "Healthy" },
    { "name": "ServiceBus", "status": "Healthy" },
    { "name": "KeyVault", "status": "Healthy" }
  ]
}

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant ObservabilityAgent
  participant DevOpsAgent
  participant StudioUI
  participant EventBus

  Orchestration->>ObservabilityAgent: Trigger Agent (IntegrationsConfigured)
  ObservabilityAgent->>DevOpsAgent: Inject pipeline observability steps
  ObservabilityAgent->>StudioUI: Show dashboards and logs config
  ObservabilityAgent->>EventBus: Emit ObservabilityReady

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

📘 Notes¶

All logs are correlated via TraceId, TenantId, and RequestId
Observability is baked into:
- API Gateway
- Background workers
- Message consumers
Supports future integration with:
- OpenAI Observability Extensions
- Self-healing orchestration
- Incident Auto-Diagnostics

🧭 Cycle 19: Multi-Tenant Enablement¶

🧩 Overview¶

In this cycle, the Multi-Tenant Architect Agent enables full multi-tenancy support across all services, infrastructure, and observability components. It ensures that the solution is tenant-aware, securely isolated, and ready to scale across multiple customers with customized experiences and resources.

This includes tenant resolution, data partitioning, scoped API access, and tenant-specific configuration.

🧠 Objectives¶

Implement tenant context resolution in APIs, background jobs, and events
Enable data isolation using schema-per-tenant, database-per-tenant, or row-level filtering
Configure per-tenant routing, branding, and feature flags
Inject tenant metadata into logs, metrics, and events
Prepare tenant provisioning flows and operational dashboards

🧑‍💼 Actors¶

Actor	Role
Multi-Tenant Architect Agent	Adds tenant-awareness to services and infrastructure
Orchestration Layer	Triggers after `ObservabilityReady`
Infrastructure Planner Agent	Provides multi-tenant schema or resource strategies
Studio UI	Displays tenant metadata, context mappings, and routes
User (Optional)	Can define provisioning models, default tenants, or branding overrides

📝 Inputs to the Agent¶

Input	Source
`MultiTenantConfig.yaml`	Chosen tenant model: schema, DB, row-level
`ServiceBlueprints/*.yaml`	For tenant boundaries and context ownership
`IdentityClaimsConfig.yaml`	Maps access tokens to tenant claims
`TemplateSettings.yaml`	Includes tenant resolution strategy (e.g., subdomain, header, token claim)

🧪 Agent Behavior¶

Tenant Resolution Setup:
- Implements tenant resolution middleware:
  - From X-Tenant-ID header
  - From subdomain or path
  - From access_token claims
- Registers ITenantContextAccessor
Data Isolation Strategy:
- Applies selected isolation model:
  - Database-per-tenant (separate connection string)
  - Schema-per-tenant (shared DB, isolated schemas)
  - Row-level security (TenantId FK filtering)
- Injects filters and mappings in ORM configuration
Routing & Branding:
- Supports tenant-aware routing:
  - e.g., *.customer1.app.com
- Loads per-tenant branding, configuration, and feature flags
Telemetry Enrichment:
- Adds TenantId to:
  - Logs
  - Traces
  - Events
  - Metrics
Provisioning Flow:
- Generates API and scripts to:
  - Create new tenant identity, schema, config
  - Initialize default resources (dashboards, roles, etc.)

📦 Artifacts Generated¶

Artifact	Format	Description
`TenantContext.cs`	C#	Runtime accessor for tenant metadata
`TenantResolverMiddleware.cs`	C#	Middleware to resolve tenant from request
`orm/tenant-filters.cs`	C#	Row-level or schema-based filters
`provisioning/CreateTenantWorkflow.cs`	C#	Orchestrator for tenant bootstrap
`tenant-routing.yaml`	YAML	Subdomain/path/claim mappings
`Event: TenancyEnabled`	EventBus	Signals full tenant-aware state ready for provisioning

📂 Folder Structure¶

tenancy/
├── TenantContext.cs
├── TenantResolverMiddleware.cs
├── Provisioning/
│   └── CreateTenantWorkflow.cs
├── Config/
│   └── tenant-routing.yaml
│   └── default-tenant-settings.yaml

🧭 Sample Tenant Context¶

public class TenantContext
{
    public string TenantId { get; set; }
    public string TenantName { get; set; }
    public string Environment { get; set; }
    public IDictionary<string, string> Settings { get; set; }
}

🌐 Example Tenant Resolution Config¶

tenantResolution:
  strategy: "header"
  headerName: "X-Tenant-ID"
  fallbackTenant: "default"
  identityClaim: "tenant_id"
  subdomainMapping:
    customer1: "tenant-a"
    customer2: "tenant-b"

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant MultiTenantAgent
  participant StudioUI
  participant EventBus

  Orchestration->>MultiTenantAgent: Trigger Agent (ObservabilityReady)
  MultiTenantAgent->>StudioUI: Publish tenant-aware models and provisioning API
  MultiTenantAgent->>EventBus: Emit TenancyEnabled

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

📘 Notes¶

Every event, log, trace, and metric will now include TenantId and Environment
Future provisioning workflows can be automated by agents
Tenant context is available to all layers: API, Domain, Infrastructure
Future: multi-tenant billing integration, tenant-specific feature toggles (e.g., LaunchDarkly)

🧪 Cycle 20: Simulation & Scenario Playback¶

🧩 Overview¶

In this cycle, the Simulation Agent executes end-to-end synthetic user flows across the entire platform to validate real-world behavior. It simulates typical user interactions, backend events, and system edge cases to stress test integrations, telemetry, and workflows before go-live.

This ensures confidence in the functional, performance, and observability readiness of the multi-agent-generated SaaS solution.

🧠 Objectives¶

Run automated usage simulations based on feature flows
Validate API, event, and UI integration correctness
Capture metrics, logs, and traces from synthetic actions
Test multi-tenant context propagation end-to-end
Surface misconfigurations, latency issues, or integration errors before production usage

🧑‍💼 Actors¶

Actor	Role
Simulation Agent	Orchestrates scenario execution across services
Orchestration Layer	Triggers after `TenancyEnabled`
Observability Agent	Captures telemetry during simulation
Studio UI	Visualizes simulation status and trace maps
User (Optional)	May define custom scenarios, edge cases, or volume thresholds

📝 Inputs to the Agent¶

Input	Source
`ProductPlan.json`	Defines key user journeys and features to simulate
`openapi/*.yaml`	APIs to be called during simulation
`feature/*.feature`	BDD scenarios for behavior simulation
`TenantContext.yaml`	Tenant IDs and context for simulated tenants
`MetricsConfig.yaml`	Target metrics, thresholds, and dashboards to observe

🧪 Agent Behavior¶

Scenario Generation:
- Translates user journeys into executable HTTP flows and event emissions
- Supports parameterization per tenant/environment
Synthetic User Execution:
- Calls API endpoints in sequence (e.g., Register → Book Appointment → Cancel)
- Emits events as message producers (e.g., AppointmentBooked)
- Optionally simulates UI traffic or background task execution
Observation and Validation:
- Measures latency, success rate, event propagation, and data persistence
- Validates telemetry propagation: TraceId, TenantId, CorrelationId
- Logs coverage gaps or alert triggers
Result Aggregation:
- Produces a scenario report with pass/fail status, timing, and system feedback
- Highlights failing endpoints, services, or dependency bottlenecks

📦 Artifacts Generated¶

Artifact	Format	Description
`simulations/*.scenario.yaml`	YAML	Scenario definitions and steps
`simulation-results/*.json`	JSON	Pass/fail results with timings
`metrics/simulation-dashboards.json`	JSON	Synthetic load dashboards
`alerts/simulation-anomalies.json`	JSON	Any triggered alerts during playback
`Event: SimulationValidated`	EventBus	Indicates successful synthetic validation

🧪 Example Scenario (Booking Flow)¶

name: Booking Flow Scenario
tenant: tenant-a
steps:
  - POST /api/v1/users/register
    payload: register-user.json
  - POST /api/v1/appointments
    payload: book-appointment.json
  - GET  /api/v1/appointments/{id}
    extract: appointmentId
  - DELETE /api/v1/appointments/{appointmentId}

📉 Metrics Observed¶

API Success Rate (per endpoint and tenant)
Latency Percentiles (p50, p95, p99)
Event Processing Time (from source to sink)
Database Query Duration (if enabled)
Error Codes and Exception Types

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant SimulationAgent
  participant ObservabilityAgent
  participant StudioUI
  participant EventBus

  Orchestration->>SimulationAgent: Trigger Agent (TenancyEnabled)
  SimulationAgent->>ObservabilityAgent: Monitor scenario execution
  SimulationAgent->>StudioUI: Show live simulation results
  SimulationAgent->>EventBus: Emit SimulationValidated

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

📘 Notes¶

Simulation scenarios are modular and reusable
Validates both synchronous and event-driven flows
Can be triggered per release or in nightly pipelines
Future: Load simulation at scale, chaos injection, AI-driven regression detection

📦 Cycle 21: Release Candidate Packaging¶

🧩 Overview¶

In this cycle, the Release Manager Agent prepares a complete and versioned release candidate of the SaaS solution. It packages all services, SDKs, documentation, infrastructure templates, and deployment configurations into environment-ready bundles. It also generates changelogs and release notes based on the commit history and agent-generated metadata.

This release candidate becomes the baseline for final QA, customer preview, or production cutover.

🧠 Objectives¶

Package all application services, libraries, and frontends with semantic versioning
Generate changelogs, release notes, and diff reports from previous versions
Create deployment artifacts per environment (Dev, Staging, Prod)
Tag source control and pipelines for traceability
Produce full asset manifest for distribution and governance

🧑‍💼 Actors¶

Actor	Role
Release Manager Agent	Orchestrates packaging, versioning, and metadata output
DevOps Agent	Provides latest build outputs and pipeline data
Orchestration Layer	Triggers after `SimulationValidated`
User (Optional)	Can approve version bumping strategy, release naming, or output formats

📝 Inputs to the Agent¶

Input	Source
`BuildArtifacts/`	Binaries, containers, and compiled outputs from CI/CD
`CHANGE_HISTORY.md`	Commit and feature delta
`DeploymentTargets.yaml`	Defines output structure for Dev, Staging, Prod
`TemplateSettings.yaml`	Packaging options (e.g., NuGet, Docker, ZIP, Helm)
`SimulationResults.json`	Final QA gate for packaging approval

🧪 Agent Behavior¶

Artifact Assembly:
- Gathers:
  - API binaries
  - Container images
  - Static frontend bundles
  - Infrastructure scripts (Bicep/Terraform)
  - SDKs (e.g., C# / TypeScript)
Semantic Versioning:
- Determines version bump: MAJOR.MINOR.PATCH
- Applies Git tag and pipeline artifact versioning
- Uses commit messages or feature plan to compute diff
Release Notes & Changelog:
- Aggregates changes by service/module
- Links to Azure DevOps work items or Git commits
- Supports Markdown, HTML, and JSON output
Environment Packaging:
- Creates ZIP or TAR files per target (Dev/Staging/Prod)
- Includes config, secrets placeholders, and deployment scripts
Registry Publishing (Optional):
- Pushes:
  - Docker images to Azure Container Registry (ACR)
  - NuGet packages to Azure Artifacts
  - Helm charts or manifests to GitOps repo

📦 Artifacts Generated¶

Artifact	Format	Description
`release-notes.md`	Markdown	Full release changelog
`release-manifest.json`	JSON	All components and their versions
`env-packages/staging.zip`	ZIP	Deployment-ready bundle for staging
`artifacts/bookingservice.1.2.0.nupkg`	NuGet	Published backend library
`Event: ReleaseCandidateReady`	EventBus	Triggers approval or production promotion workflow

📁 Sample Folder Structure¶

release/
├── release-notes.md
├── release-manifest.json
├── env-packages/
│   ├── dev.zip
│   ├── staging.zip
│   └── prod.zip
├── docker/
│   └── bookingservice:1.2.0
├── nuget/
│   └── ConnectSoft.BookingService.1.2.0.nupkg

📘 Sample Release Notes (Markdown)¶

# Release v1.2.0 – May 5, 2025

## New Features
- [BookingService] Added cancellation support
- [NotificationService] Integrated with SendGrid webhook retries

## Improvements
- Reduced latency in appointment queries
- API Gateway rate-limiting rules refined

## Fixes
- Tenant resolution bug on custom domains
- Fixed API 500 error on invalid payload

## Artifacts
- Docker: `bookingservice:1.2.0`
- NuGet: `ConnectSoft.BookingService.1.2.0.nupkg`

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant ReleaseAgent
  participant DevOpsAgent
  participant StudioUI
  participant EventBus

  Orchestration->>ReleaseAgent: Trigger Agent (SimulationValidated)
  ReleaseAgent->>DevOpsAgent: Fetch latest pipeline artifacts
  ReleaseAgent->>StudioUI: Publish release notes, version tags
  ReleaseAgent->>EventBus: Emit ReleaseCandidateReady

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

📘 Notes¶

Release artifacts are digitally traceable, versioned, and reproducible
Multiple release channels supported:
- Nightly
- RC (release candidate)
- Stable
Future: automatic rollback packaging, deployment preview diffing, SBOM generation

🛡️ Cycle 22: Production Readiness & Hardening¶

🧩 Overview¶

In this cycle, the Production Readiness Agent verifies that the solution is safe, stable, and compliant for real-world deployment. It runs infrastructure validation, security audits, checklist verification, and tenant-specific configuration for go-live. The agent ensures environmental isolation, backup readiness, and deployment validation prior to promotion to production.

This is the final gate before formal release.

🧠 Objectives¶

Validate production environment configuration and infrastructure
Perform security hardening checks and compliance validations
Confirm observability, backups, and failover readiness
Run final pre-prod smoke tests and tenant activation procedures
Lock configuration via immutable releases and GitOps manifests

🧑‍💼 Actors¶

Actor	Role
Production Readiness Agent	Confirms system is safe and production-grade
Security Agent	Performs final security scan
DevOps Agent	Validates infrastructure deployment and pipeline gating
Orchestration Layer	Triggers after `ReleaseCandidateReady`
User (Optional)	May review checklist, sign off, or request custom validation logic

📝 Inputs to the Agent¶

Input	Source
`release-manifest.json`	Artifacts, services, and versions for this release
`infrastructure-plan.bicep`	Production deployment template
`SecurityPolicies.yaml`	Policies to validate
`MonitoringRules.json`	Alerts, dashboards, and probes
`TenantProvisioning.yaml`	Expected tenant configuration and feature flags

🧪 Agent Behavior¶

Infrastructure Validation:
- Deploys staging → prod via approved pipeline
- Verifies:
  - Networking (DNS, firewall, TLS)
  - Secrets (Key Vaults, Managed Identities)
  - High availability (replicas, autoscale)
Security Audit & Checklist:
- Runs:
  - Static analysis (e.g., DevSkim, Snyk)
  - Secrets detection
  - Role and permission scan (RBAC validation)
- Enforces:
  - Principle of least privilege
  - Token expiration and refresh policies
Smoke Testing:
- Re-executes top 5 flows per tenant in production staging
- Validates telemetry propagation and SLA readiness
Backup & Failover Check:
- Confirms:
  - DB backups configured and tested
  - Storage replication enabled
  - Event retry queues functional
Immutable Release Finalization:
- Tags code, pipeline, and artifact versions as locked
- Publishes signed GitOps manifests or release artifacts
- Captures final state into production changelog

📦 Artifacts Generated¶

Artifact	Format	Description
`prod-readiness-report.md`	Markdown	Final checklist and validation summary
`smoke-test-results.json`	JSON	Last-mile validation output
`rbac-audit.json`	JSON	Role-to-permission mapping and risk scan
`immutable-release.yaml`	YAML	Final deployment manifest with pinned versions
`Event: ProductionReady`	EventBus	System is certified and green-lit for promotion

✅ Sample Readiness Checklist (Partial)¶

Check	Status
All service health checks pass	✅
TLS enforced with valid certs	✅
Secrets injected via Key Vault	✅
Admin roles scoped and validated	✅
Event bus configured with retries	✅
Dashboard alerts firing correctly	✅
Production backup validated	✅
GitOps manifest tagged	✅

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant ProdReadinessAgent
  participant DevOpsAgent
  participant SecurityAgent
  participant StudioUI
  participant EventBus

  Orchestration->>ProdReadinessAgent: Trigger Agent (ReleaseCandidateReady)
  ProdReadinessAgent->>DevOpsAgent: Validate infra, pipelines, staging
  ProdReadinessAgent->>SecurityAgent: Perform audit and checklist
  ProdReadinessAgent->>StudioUI: Show readiness report
  ProdReadinessAgent->>EventBus: Emit ProductionReady

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

📘 Notes¶

Supports internal approval workflows or user-triggered promotion
Includes environmental diffing to catch config drift
GitOps support ensures declarative, repeatable deployment
Future: ISO/SOC2 automated checklists and deployment sign-offs

🚀 Cycle 23: Production Promotion¶

🧩 Overview¶

In this cycle, the Deployment Agent executes the controlled promotion to production, deploying the validated release candidate into the live environment. It handles traffic shifting, version pinning, post-deployment validation, and canary rollout (if configured).

The system transitions from a pre-release state to live availability, routing real tenant traffic and enabling production observability.

🧠 Objectives¶

Deploy the release to the production environment
Perform canary, blue-green, or full rollout strategy
Activate production tenant access, routing, and secrets
Monitor system behavior in the first minutes/hours after go-live
Confirm rollback procedures are in place if anomalies arise

🧑‍💼 Actors¶

Actor	Role
Deployment Agent	Performs the actual release deployment to production
Orchestration Layer	Triggers after `ProductionReady`
Observability Agent	Monitors health and anomalies during rollout
Studio UI	Displays deployment status, telemetry, and live readiness
User (Optional)	May trigger manual approval step or adjust rollout strategy

📝 Inputs to the Agent¶

Input	Source
`immutable-release.yaml`	Final, validated release manifest
`infrastructure-plan.bicep`	Production infra template
`DeploymentStrategy.yaml`	Canary, full, or blue-green mode
`TenantRouting.yaml`	Production domains, subdomains, and routing rules
`Alerts/MonitorRules.json`	Thresholds for rollback triggers

🧪 Agent Behavior¶

Deployment Initiation:
- Starts production pipeline using GitOps or IaC
- Applies deployment strategy:
  - Canary: 5% → 25% → 100%
  - Blue-Green: switch route after green is healthy
  - Full: all services deployed immediately
Live Routing Activation:
- Routes live tenant domains to new services
- Ensures SSL certs, DNS, and ingress rules are applied
Post-Deployment Validation:
- Replays simulation scenarios on production
- Watches alerting dashboards and trace spikes
- Monitors tenant logs, service latencies, and success rates
Fallback Plan & Lock:
- Locks deployment if healthy
- Keeps rollback plan and prior version image/tag available
- Updates deployment state as successful

📦 Artifacts Generated¶

Artifact	Format	Description
`deployment-status.json`	JSON	Live rollout state and logs
`live-traffic-dashboard.json`	JSON	Grafana config for tenant/live traffic view
`canary-metrics.csv`	CSV	Error/latency metrics during phased rollout
`Event: ProductionDeployed`	EventBus	System is now live and serving traffic

📁 Sample Canary Deployment Flow¶

strategy: canary
steps:
  - percentage: 5
    waitFor: 10m
  - percentage: 25
    waitFor: 15m
  - percentage: 100
    waitFor: manualApproval
rollbackThresholds:
  - http5xx > 2%
  - p95Latency > 1.2s

📈 Key Observability Checks¶

Metric	Threshold	Action
API 500 Error Rate	> 2%	Rollback
Trace Latency (p95)	> 1.2s	Alert
Message Processing Failures	> 0	Investigate
Tenant Login Failures	> 5/min	Pause rollout

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant DeploymentAgent
  participant ObservabilityAgent
  participant StudioUI
  participant EventBus

  Orchestration->>DeploymentAgent: Trigger Agent (ProductionReady)
  DeploymentAgent->>ObservabilityAgent: Monitor live rollout
  DeploymentAgent->>StudioUI: Update live status
  DeploymentAgent->>EventBus: Emit ProductionDeployed

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

📘 Notes¶

Deployment is fully automated, but allows optional manual approval gates
DNS, SSL, and TLS termination are validated as part of go-live checks
Canary and rollback strategies are reusable across environments
Future: auto-tuned rollout speed based on metrics, ML-based anomaly detection

📈 Cycle 24: Post-Release Analytics & Feedback Loop¶

🧩 Overview¶

In this final cycle, the Feedback Loop Agent activates continuous usage monitoring, product analytics, and AI-powered insights to drive future iterations. It connects real user behavior, system metrics, and tenant feedback back into the product planning loop, enabling autonomous evolution of the SaaS solution.

This closes the Dev → Release → Learn loop and prepares the platform for the next improvement cycle.

🧠 Objectives¶

Collect real-time usage metrics, feature adoption, and user feedback
Monitor tenant-specific engagement and satisfaction
Identify top pain points, drop-offs, and improvement opportunities
Feed data into the Vision Architect Agent for new cycles
Enable auto-generated product evolution proposals

🧑‍💼 Actors¶

Actor	Role
Feedback Loop Agent	Analyzes post-release usage and proposes refinements
Observability Agent	Supplies system metrics and logs
Studio UI	Displays analytics dashboards and user feedback
Vision Architect Agent	Consumes insights to plan the next product evolution cycle
User (Optional)	Can contribute manual feedback or NPS-style input

📝 Inputs to the Agent¶

Input	Source
`telemetry/*.json`	Usage and performance logs
`feedback/*.csv`	Survey results, feature ratings
`dashboards/*.json`	Live tenant engagement and error tracking
`ProductPlan.json`	Original intent and feature goals
`Event: ProductionDeployed`	Triggers analysis cycle

🧪 Agent Behavior¶

Usage Pattern Analysis:
- Tracks:
  - Feature invocation frequency
  - Session lengths
  - Drop-off points in user journeys
Tenant-Level Reporting:
- Maps engagement by tenant
- Flags underused or problematic modules
Sentiment & Feedback Capture:
- Collects:
  - In-app surveys (thumbs up/down, NPS)
  - Helpdesk tags
  - Session replays (if enabled)
Insight Generation:
- Identifies:
  - Bug hotspots
  - Performance degradation
  - Top-requested improvements
Cycle Proposal Emission:
- Emits a new EvolutionProposal.yaml file
- Suggests new features, fixes, or technical improvements
- Feeds into Cycle 1 to restart the AI Software Factory lifecycle

📦 Artifacts Generated¶

Artifact	Format	Description
`evolution/EvolutionProposal.yaml`	YAML	List of improvements for next cycle
`analytics/tenant-engagement.csv`	CSV	Usage stats per tenant and module
`feedback/summary.md`	Markdown	Aggregated user feedback
`ai-insights/opportunities.json`	JSON	AI-suggested product improvements
`Event: FeedbackAnalyzed`	EventBus	Triggers Cycle 1 of next generation

📁 Sample Evolution Proposal¶

cycle: next
proposals:
  - feature: Add bulk appointment import
    reason: 37% of users requested batch upload from CSV
  - fix: Reduce booking latency
    metric: 1.8s avg → goal 800ms
  - integration: Add Calendly connector
    requestedBy: 9 tenants
    category: productivity

📊 Dashboard Metrics Monitored¶

Metric	Insight
Feature Usage Heatmap	Identifies top and unused modules
API Success vs Failures	Flags backend health issues
Avg Session Duration	Measures user engagement depth
Feedback Score (NPS)	Gauges overall satisfaction
Error Trends (p95)	Spotlights latency or instability

🔁 Orchestration Flow¶

sequenceDiagram
  participant Orchestration
  participant FeedbackAgent
  participant VisionAgent
  participant StudioUI
  participant EventBus

  Orchestration->>FeedbackAgent: Trigger Agent (ProductionDeployed)
  FeedbackAgent->>StudioUI: Show insights and evolution proposal
  FeedbackAgent->>VisionAgent: Feed EvolutionProposal.yaml
  FeedbackAgent->>EventBus: Emit FeedbackAnalyzed (starts Cycle 1 again)

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

📘 Notes¶

All cycles become self-reinforcing, enabling:
- Continuous evolution of software
- Autonomous planning of feature iterations
Feedback data can optionally:
- Be used to train internal AI copilots
- Trigger notifications to stakeholders
Future: dynamic experimentation (A/B), automatic roadmap updates, AI roadmap generation

✅ Conclusion¶

The ConnectSoft AI Software Factory Lifecycle is more than just an automated pipeline — it is a blueprint for intelligent, scalable, and secure SaaS delivery. By combining clean architecture, domain-driven design, multi-agent orchestration, and cloud-native automation, the platform delivers production-ready software that is not only functional, but maintainable, evolvable, and customer-aligned from day one.

Each cycle contributes to a self-reinforcing loop of:

Planning with clarity and intent
Generation with precision and speed
Validation with confidence and visibility
Delivery with security and reliability
Learning through post-release insights and usage patterns

Together, they represent a new standard for software engineering — one where human creativity meets autonomous execution, and every SaaS solution can grow, adapt, and improve continuously. The journey doesn’t end at deployment. With every feedback loop, the platform evolves — and so does the product, the process, and the ecosystem around it.

🔄 Project Lifecycle¶

🧭 End-to-End Project Lifecycle Diagram¶

🔵 Cycle 1 - Project Initialization¶

🧩 Overview¶

🧑‍💼 Actors¶

📝 Inputs Collected from User¶

🧠 System Behavior¶

1. Project Creation Trigger¶

2. Validation & Metadata Enrichment¶

3. Repository & Folder Setup¶

4. Event Emission¶

📦 Artifacts Generated¶

🔁 Orchestration Flow¶

📘 Notes¶

🟢 Cycle 2 - Source Control Configuration & Template Provisioning¶

🧩 Overview¶

🧑‍💼 Actors¶

⚙️ Tooling & Stack Selection¶

🧠 User Choices¶

🧰 Supported Solution Templates¶

⚙️ System Behavior¶

1. Source Control Workspace Initialization¶

2. Template Provisioning¶

3. Work Item Seeding¶

4. Event Emission¶

📦 Artifacts Generated¶

🔁 Orchestration Flow¶

📘 Notes¶

🔷 Cycle 3 - Vision Agent Activation¶

🧩 Overview¶

🧠 Objectives¶

🧑‍💼 Actors¶

🧾 Inputs to the Agent¶

🧪 Agent Behavior¶

📦 Artifacts Generated¶

🔁 Orchestration Flow¶

📘 Notes¶

🟣 Cycle 4: Product Planning¶

🧩 Overview¶

🧠 Objectives¶

🧑‍💼 Actors¶

📝 Inputs to the Agent¶

🧪 Agent Behavior¶

📦 Artifacts Generated¶

🗂 Sample Output in Markdown¶

🔁 Orchestration Flow¶

📘 Notes¶

🟠 Cycle 5: Enterprise Architecture¶

🧩 Overview¶

🧠 Objectives¶

🧑‍💼 Actors¶

📥 Inputs to the Agent¶

🧪 Agent Behavior¶

📦 Artifacts Generated¶

📉 Sample Context Map (Mermaid)¶

📑 Sample Domain Modeling Snippet (JSON)¶

🔁 Orchestration Flow¶

📘 Notes¶

🟡 Cycle 6: Solution Blueprinting¶

🧩 Overview¶

🧠 Objectives¶

🧑‍💼 Actors¶

📝 Inputs to the Agent¶

🧪 Agent Behavior¶

📦 Artifacts Generated¶

📂 Sample Microservice Blueprint (BookingService.yaml)¶

🖼 Mermaid Diagram (Clean Architecture Example)¶

🔁 Orchestration Flow¶

📘 Notes¶

🟤 Cycle 7: Event-Driven Design¶

🧩 Overview¶

🧠 Objectives¶

🧑‍💼 Actors¶

📥 Inputs to the Agent¶

🧪 Agent Behavior¶

📦 Artifacts Generated¶

🧾 Sample Event Schema¶

🗺 Sample Event Topology (Mermaid)¶

🔁 Orchestration Flow¶

📘 Notes¶

📂 Sample Microservice Blueprint (`BookingService.yaml`)¶

🛠 Sample Infra YAML (`azure-resources.yaml`)¶