🧠 Orchestration Layer¶

Overview¶

The ConnectSoft AI Software Factory Orchestration Layer is the autonomous backbone responsible for managing the lifecycle of AI-driven software creation, from user intent to full-stack delivery. It coordinates the execution of thousands of agents, interprets blueprints, sequences tasks, handles failures, provisions infrastructure, and integrates human decision-making when needed.

Built on Clean Architecture, DDD, and Event-Driven Principles, the orchestration layer ensures:

Scalable coordination of distributed agents
Resilient execution with retries, fallbacks, and timeouts
Modular, reusable FSMs for service, infrastructure, and platform workflows
Observability-first execution through tracing, metrics, and structured logs
Blueprint-driven behavior, enabling conditional, dynamic workflows
Support for human-in-the-loop escalation, approvals, and override paths

Role in the ConnectSoft AI Software Factory¶

The orchestration layer sits between:

✍️ User/Client Input (natural language, structured prompts, templates)
🤖 Agentic Workforce (100+ AI agents specialized by domain)
🧱 Reusable Templates, Blueprints, and Coordinators
🚀 Output Systems (Git repos, CI/CD, cloud infra, documentation, portals)

It is the central flow engine that turns declarative blueprints into deployed and tested software systems, using autonomous and semi-autonomous execution flows.

flowchart TD
    U(User Prompt) --> O(Orchestration Layer)
    O --> A1(Vision Architect Agent)
    O --> A2(Enterprise Architect Agent)
    O --> MAC(Microservice Assembly Coordinator)
    MAC -->|Emit Events| Agents(Multiple AI Agents)
    MAC --> Git(Commit, PR)
    Agents --> QA(QA Agent)
    QA --> O

    subgraph Agent Network
      A1
      A2
      Agents
      QA
    end

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

The orchestration layer:

Bootstraps agent sessions (sessionId, traceId)
Delegates tasks to the right agent or coordinator
Tracks cross-agent flows
Handles failures, timeouts, and event chaining
Publishes lifecycle events and metrics to observability layers

Core Capabilities¶

Capability	Description
Task Orchestration	Decomposes and routes execution plans using semantic routing
Coordinator FSMs	Finite-state machines manage assembly logic per domain (e.g., microservices, infra, UI)
Failure Recovery	Detects and resolves task, agent, and flow-level failures with resilience policies
Conditional Logic	Dynamically adapts execution paths based on blueprint metadata
Infrastructure as Code	Provisions cloud resources via dedicated IaC coordinators (e.g., Bicep, Terraform)
Human-in-the-Loop	Supports manual approvals and escalations when automation limits are reached
Observability	Fully traced, logged, and observable through OpenTelemetry + dashboard integrations
Modularity	All components are versioned, decoupled, and replaceable — built for extensibility

Why It Matters¶

ConnectSoft aims to support the autonomous creation of 3,000+ modular SaaS systems, each with unique architectures, domains, and compliance requirements. This demands an orchestration layer that is:

✅ Declarative (blueprint-first)
🤝 Collaborative (agent + human)
🔁 Recoverable (resilient to partial failures)
🧩 Composable (built from reusable modules)
🔍 Auditable (every step traced and reasoned)
🚀 Production-Ready (delivers infrastructure, not just code)

🧠 Introduction to the Orchestration Layer¶

The Orchestration Layer is the central nervous system of the ConnectSoft AI Software Factory. It governs how product intents are transformed into coordinated actions across 100+ AI agents, agentic coordinators, and microservice assembly pipelines.

Unlike traditional workflow engines or static build scripts, the orchestration layer is:

Dynamic — reacting to semantic inputs, events, and task outcomes.
Distributed — coordinating across multiple agents and domains.
Event-Driven — using commands and events to delegate, trace, and observe.

🛠 Key Capabilities¶

Capability	Description
Session Management	Creates scoped sessions with trace IDs for full observability
Semantic Task Planning	Decomposes user intents and blueprints into agent-ready tasks
Agent Routing	Dynamically chooses which agent should perform a task
Domain Event Handling	Publishes and subscribes to events across the entire AI Software Factory
State-Free Flow Control	Delegates micro-level state to domain coordinators
Human-in-the-Loop Routing	Supports manual escalation, review, or fallback routing
SLA Enforcement	Monitors task execution timelines and responds to delays/failures

🧠 What It Is Not¶

Not...	Because...
A traditional orchestrator (like Temporal, Airflow)	It's semantic, modular, and agent-first, not job-pipeline-based
A state machine	The orchestration layer is stateless; coordination state is held by dedicated coordinators
A message bus	It uses a bus (e.g., MassTransit), but adds intelligence on top of it
A scheduler	It reacts to events and traces, not static time-based triggers

✅ Example: From Prompt to System¶

Prompt:

“Build a NotificationService with MongoDB and OpenTelemetry.”

Flow via Orchestration Layer:

Step	Action
1	`ProductIntentSubmitted` → starts session: `traceId = notif-001`
2	Delegates to `Vision Architect Agent` → generates system vision
3	Delegates to `Enterprise Architect Agent` → ports + use cases
4	Triggers `StartMicroserviceAssembly` → launches `MicroserviceAssemblyCoordinator`
5	Routes intermediate events (`HandlerReady`, `AdapterReady`) to subscribed agents
6	Commits code, triggers PR, activates QA phase
7	Ends orchestration when `MicroserviceAssemblyCompleted` + `TestsPassed` received

🔁 Interfaces¶

Interface	Description
`ProductIntentSubmitted`	Entry event from external UI or agent
`StartAgentSession`	Internal command to spin up agent-specific scope
`StartMicroserviceAssembly`	Command sent to external coordinator
`RouteTaskToAgent`	Internal router logic (semantic/blueprint aware)
`AssemblyCompleted`	Final event used to trigger release or downstream systems

🔍 Key Design Principles¶

Clean separation: orchestration does not manage local state — that’s the job of domain-specific coordinators.
Agent-first: all orchestration decisions are scoped to agents, not pipelines.
Observable by default: every session is traceable across agent, task, and coordinator boundaries.
Scalable: supports 1000s of concurrent microservice flows without coupling.

🧭 Core Responsibilities of the Orchestration Layer¶

This section details the primary responsibilities of the Orchestration Layer within ConnectSoft’s AI Software Factory. These responsibilities enable the orchestrator to act intelligently and autonomously — coordinating AI agents, managing sessions, ensuring traceability, and maintaining cross-agent consistency across thousands of system generation flows.

🧩 High-Level Responsibility Areas¶

Responsibility Area	Description
Session & Trace Lifecycle	Creating, managing, and linking `sessionId`, `traceId`, and execution scopes
Task Decomposition & Planning	Splitting blueprints or user prompts into agent-executable commands
Agent Routing & Task Assignment	Selecting which agent or coordinator handles each task based on metadata
Event-Driven Execution Control	Subscribing, reacting, and emitting events to control execution flows
Observability Propagation	Injecting trace spans, telemetry, and logs across all execution steps
Failure Routing & Compensation	Detecting dropped tasks, timeouts, or errors — and rerouting or escalating
Context Preservation	Injecting semantic context and vector memory into downstream tasks
Cross-Agent Coordination	Managing dependencies and outputs between agents and flows (e.g., use cases → adapters → handlers)

🛠 Functional Responsibilities by Category¶

1. 🧠 Session & Identity Management¶

Generate:
- traceId: full orchestration lineage
- sessionId: scoped per agent or flow
- componentTag: e.g. NotificationService/HandlerGen
Store and propagate context in every event

2. ⚙️ Task Planning & Breakdown¶

Example input: "Build NotificationService with MongoDB"
Resulting sub-tasks:
- Generate strategic blueprint
- Define DDD architecture
- Start microservice assembly coordinator
- Generate handlers
- Commit code, trigger PR
Each task is a command/event with routing metadata

3. 🛰️ Agent Routing¶

Match task metadata with agent capabilities
Use agent skill registry:

{
  "agent": "Backend Developer Agent",
  "skills": ["GenerateHandler", "CreateAggregate"],
  "domains": ["Invoices", "Notifications", "Payments"]
}

* Resolve available agents and emit RouteTaskToAgent with contextual inputs

4. 📩 Event Subscription & Dispatch¶

Listens to:
- BlueprintGenerated
- PortsAndUseCasesReady
- HandlerReady
- AssemblyCompleted
Emits:
- StartAgentSession
- StartMicroserviceAssembly
- RouteToFrontendGenerator
Maintains trace-linked task lineage

5. 🔍 Observability & Telemetry¶

Injects:
- OpenTelemetry spans
- Structured logs (AgentX completed task Y in Z ms)
- Metrics on SLAs, retries, error rates
Ensures end-to-end visibility even when agents are stateless

6. 🔄 Failure Detection & Recovery¶

Detects:
- Unresponsive agent
- Event timeout (e.g., HandlerReady not emitted in 60s)
- Coordinator failure
Responds with:
- Retry with alternative agent
- Escalation to human-in-the-loop
- Emit TaskFailed or RouteToFallbackAgent

🧱 Modular Structure¶

Subsystem	Description
`SessionManager`	Creates and tracks orchestration sessions
`PlannerRouter`	Decides task decomposition and destination agent
`EventController`	Subscribes to and dispatches key domain events
`FailureRouter`	Handles timeout, error, and fallback logic
`TracerInjector`	Propagates OpenTelemetry and structured log metadata

✅ Example: NotificationService¶

Event	Action
`PortsAndUseCasesReady`	Orchestrator matches service to `MicroserviceAssemblyCoordinator`
`HandlerReady`	Orchestrator routes to `CodeCommitterAgent` and injects PR metadata
`TestResultsReady`	Orchestrator finalizes trace, emits `AssemblyComplete`

🔍 Design Rules Recap¶

✅ Stateless orchestration logic
✅ All coordination state handled by domain-specific coordinators
✅ Event-driven and agent-pluggable
✅ Observability-first and SLA-aware
✅ Support for fallback, escalation, and async retry

🤖 Agent-Aware Orchestration¶

In ConnectSoft, the orchestration layer is not just a message router — it is agent-aware.

This means it understands:

The roles, skills, and domains of every agent
The context in which each agent should be invoked
How to dynamically select, schedule, and monitor agent tasks based on blueprint metadata

This section explains how orchestration intelligently matches the right agent (or coordinator) to each task.

🧠 What Makes Orchestration “Agent-Aware”?¶

Capability	Description
🧬 Agent Identity	Every agent has a globally unique name and metadata (`agentId`, `agentType`, `skills`)
🧠 Skill Registry	The orchestrator maintains a registry of available agent skills and task patterns
🗺️ Blueprint Context Mapping	Orchestration uses service blueprints to identify task → agent mappings
🛰️ Semantic Routing	Tasks are not routed statically — they’re routed based on roles, load, and domain alignment
🔄 Dynamic Fallback	If an agent is unavailable or fails, the orchestrator can reroute to alternatives with similar skills

🧩 Agent Metadata Model¶

{
  "agentId": "backend-dev-agent-1",
  "agentType": "Backend Developer Agent",
  "skills": ["GenerateHandler", "CreateAggregate", "ValidateUseCase"],
  "domains": ["Invoices", "Notifications", "Appointments"],
  "availability": "Healthy"
}

🔁 Orchestration Task → Agent Flow¶

Example Task: `GenerateHandler: SendNotificationHandler`¶

Step	Action
1	Orchestrator receives task from coordinator or blueprint: `taskType: GenerateHandler`, `domain: NotificationService`
2	Looks up all agents with `skills.includes("GenerateHandler")` and `domains.includes("Notifications")`
3	Filters by availability and load
4	Selects best-fit agent and emits `RouteTaskToAgent` event
5	Waits for response event: `HandlerReady`

🎯 Agent Selection Rules¶

Rule	Description
Skill match	Agent must support the task’s skill (`GenerateHandler`)
Domain affinity	Agent must have relevant domain tags (`Notifications`)
Availability	Agent must be online, not overloaded, and healthy
Fallback scoring	If no direct match, use fuzzy matching or backup agent class

📚 Skill Registry Example (Internal Dictionary)¶

Agent Type	Skills
`Backend Developer Agent`	`GenerateHandler`, `BuildDomainLogic`, `ValidateAggregate`
`Adapter Generator Agent`	`CreateRepository`, `GenerateAdapter`, `ConfigurePersistence`
`QA Agent`	`RunUnitTests`, `RunIntegrationTests`, `EmitCoverageReport`
`Frontend SPA Agent`	`GenerateSPA`, `LinkAPIContracts`, `SetupRouting`

This registry is either:

Static (JSON/config)
Or discovered dynamically via agent registration events (e.g., AgentOnline, AgentCapabilityDeclared)

🛠 Example Orchestration Logic¶

var candidates = agentRegistry
    .Where(a => a.Skills.Contains("GenerateHandler") && a.Domains.Contains("Notifications"))
    .OrderBy(a => a.LoadScore)
    .FirstOrDefault();

if (candidates != null)
{
    EmitRouteCommand(candidates.AgentId, taskPayload);
}

✅ Benefits of Agent-Aware Orchestration¶

Benefit	Description
Pluggability	New agents can join with skill declarations — no code change needed
Load-balanced	Multiple agents can support the same skill across shards
Semantic	Uses domain and skill metadata, not hardcoded routing
Resilient	Agents can be replaced, scaled out, or rerouted at runtime
Composable	Agents can be assigned per stage in a multi-service system blueprint

🔍 Real-World Example: WellnessTrack¶

In the WellnessTrack project, orchestration:

Receives task: GenerateAdapter for UserProfileService
Finds all agents with:
- skill: GenerateAdapter
- domain: UserProfiles
Selects adapter-gen-agent-2
Emits RouteTaskToAgent → includes traceId, blueprintId, and task context
Waits for AdapterReady → routes to commit flow

🧾 Sessions and Traces¶

Sessions and traces are foundational to observability, state continuity, and cross-agent coordination in the ConnectSoft AI Software Factory. Every execution — from a single task to a full system blueprint — is wrapped in a structured session and connected by a traceId.

This section describes how sessions and traces are created, managed, and propagated through all orchestrated components: agents, coordinators, and infrastructure.

🧩 Core Concepts¶

Term	Description
traceId	Globally unique identifier for the full orchestration run (blueprint scope)
sessionId	Unique to each scoped process (e.g., per agent execution, per microservice coordinator)
componentTag	Identifies the agent, coordinator, or subflow handling a task (`AdapterGen:NotificationService`)
blueprintId	ID of the original input blueprint or system intent
parentTraceId	Optional link to a higher-level orchestration or external caller trace

🧠 Session Lifecycle¶

Step-by-Step¶

Step	Event	Description
1	`ProductIntentSubmitted`	Orchestrator generates `traceId: wellness-456`
2	`StartAgentSession`	For Vision Agent: `sessionId: vision-001`, links to `traceId`
3	`StartMicroserviceAssembly`	Creates another session: `sessionId: svc-appointment-001`
4	Downstream Commands	Each command (e.g., `GenerateHandler`) includes both `traceId` + `sessionId`
5	Completion	All `Completed` events trace back to original `traceId` for SLA and lineage

🧾 Message Metadata Format¶

Every event/command emitted by the orchestration layer includes this envelope:

{
  "traceId": "notif-001",
  "sessionId": "notif-service-assembly-01",
  "blueprintId": "blueprint-abc123",
  "componentTag": "HandlerGeneratorAgent:CreateNotificationHandler",
  "timestamp": "2025-05-04T13:10:00Z"
}

This metadata is:

Injected by the orchestrator
Verified and extended by each downstream coordinator or agent
Used for observability and correlation

🔍 Tracing Across Boundaries¶

1. Across Agents¶

Task started in Vision Agent, continued in Architecture Agent → both share the same traceId

2. Across Coordinators¶

MicroserviceAssemblyCoordinator uses its own sessionId, but shares parent traceId

3. Across Systems¶

If triggered by an external platform (e.g., DevOps CI), parentTraceId is preserved for trace continuity

📊 Observability Injection¶

The orchestrator integrates with OpenTelemetry:

Propagated Field	Purpose
`traceparent` / `tracestate`	Injected into HTTP/gRPC headers
`traceId`, `sessionId`	Included in log scopes
`componentTag`	Used for log correlation (`[AdapterGen]`, `[PR Agent]`)
`spanName`	Labeled per task (e.g., `GenerateHandler:Notification`)

This enables:

Jaeger / Azure Monitor / Grafana Tempo views of multi-agent flows
Failure tracking across sub-systems
SLA dashboards per blueprint, session, and domain

✅ Real-World Example¶

User Input: “Build NotificationService”

traceId: notif-001
  ├── session: vision-001       → Vision Architect Agent
  ├── session: arch-001         → Enterprise Architect Agent
  ├── session: svc-001          → MicroserviceAssemblyCoordinator
       ├── AdapterReady
       ├── HandlerReady
       ├── CommitComplete
  ├── session: pr-001           → PR Agent
  └── session: qa-001           → QA Agent

Each node logs, emits telemetry, and routes events tagged with traceId: notif-001.

🧱 Best Practices¶

Always generate sessionId per execution scope (agent or coordinator)
Use deterministic componentTag naming: {AgentType}:{Task}
Store trace-mapped logs and events in structured stores (e.g., Application Insights, Elastic, OTLP backends)
Span nesting: trace → session → span → event → log

🧭 Orchestrator vs Coordinator — Division of Responsibility¶

This section defines the clear boundary between the Orchestration Layer and Coordinators (like the MicroserviceAssemblyCoordinator). Understanding this distinction is crucial to maintain modularity, scalability, and clean domain separation across the ConnectSoft AI Software Factory.

🧩 High-Level Comparison¶

Responsibility	Orchestrator Layer	Coordinator (e.g. AssemblyCoordinator)
Scope	Cross-agent, cross-domain	Micro-level, per-domain or per-microservice
Stateful?	❌ Stateless	✅ Stateful (FSM, persisted state)
Focus	Routing, session tracking, task dispatch	Lifecycle and sequencing of task execution
Knows About Agents?	✅ Yes (via registry)	✅ Yes (via events/subscriptions)
Knows Blueprint Structure?	✅ Partial (routing metadata)	✅ Deep (uses ports, adapters, use cases)
Failure Handling	Retry, reroute, fallback agents	Retry or compensate within the task lifecycle
Implements Business Logic?	❌ Never	✅ Orchestrates domain service assembly logic
Output	Routes commands/events across system	Emits domain events for specific microservice

🛠 Real Responsibilities¶

Orchestration Layer¶

✅ Manages:

Trace/session lifecycle
Semantic routing to 100+ AI agents
Skill-based agent selection
Triggering StartMicroserviceAssembly
Routing HandlerReady, AdapterGenerated, etc.
Observability, event logging, fallback

🚫 Does not:

Sequence microservice scaffolding steps
Track intermediate stage state (e.g., Scaffolded → HandlerReady)
Hold blueprint structure

Microservice Assembly Coordinator (MAC)¶

✅ Manages:

Lifecycle of a single microservice: scaffold → handlers → adapter → commit
Blueprint metadata for that microservice
Event subscriptions for tasks (HandlerReady, DTOsReady)
FSM-based state tracking (AssemblyProgressState)
Compensation and retries at service level

🚫 Does not:

Route to other microservices or frontend systems
Track trace/session lineage across agents
Choose which agent to assign a task to (delegated to orchestrator)

📊 Visual: Division of Responsibility¶

flowchart TD
    UI[User Input] --> ORCH(Orchestration Layer)
    ORCH -->|StartAssembly| MAC1(MicroserviceAssemblyCoordinator)
    ORCH -->|RouteToAgent| AGENT1[Handler Generator Agent]
    MAC1 -->|EmitEvent| ORCH

    subgraph Microservice Assembly Coordinator
      MAC1
    end

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

The orchestrator starts the coordinator, tracks its output, and routes intermediate tasks to agents.
The coordinator knows which events to emit, but not where to send them — that’s orchestration’s job.

🧠 Why This Separation Matters¶

Concern	Without Separation	With Separation
Scalability	State and routing logic grow uncontrollably	Clean FSM scaling per domain
Agent Modularity	Tight coupling between task routing and logic	Loose coupling via events and contracts
Debuggability	Hard to trace who failed	Clear trace: coordinator emits → agent handles
Extensibility	Hard to reuse flows	Can build new coordinators using same orchestration hooks
Responsibility Clarity	Who owns what?	Orchestrator = router, Coordinator = builder

✅ Real-World Trace Example: `NotificationService`¶

Component	Role
Orchestrator	Receives `PortsAndUseCasesReady`, triggers `StartMicroserviceAssembly(traceId: notif-001)`
Coordinator	Begins `NotificationServiceAssemblyCoordinator`, tracks FSM state
Orchestrator	Routes each emitted event (e.g., `AdapterNeeded`, `HandlerReady`) to best-fit agents
Coordinator	Emits `MicroserviceAssemblyCompleted` when done
Orchestrator	Triggers `CreatePullRequest`, `RunTests` to finalize flow

📡 Cycle 6: Event-Driven Architecture in Orchestration¶

The ConnectSoft Orchestration Layer is fully powered by an event-driven architecture (EDA), which enables decoupled, asynchronous, and scalable coordination across agents, coordinators, and system components.

This cycle explains how the orchestration layer uses commands, events, and event buses to drive the lifecycle of blueprint-based software generation.

⚙️ Event-Driven Design Principles¶

Principle	Implementation in ConnectSoft
Asynchronous Task Flow	Every task is emitted as a message — not executed inline
Loose Coupling	Senders don’t need to know who handles the task
Reactive Agents	Agents subscribe to task/event types based on skill & domain
Replayable Execution	All events are persistable, observable, and traceable
Parallelizable Pipelines	Multiple coordinators and agents can operate simultaneously

🛠 Core Message Types¶

📥 Commands¶

Commands represent intent to act. They are typically emitted by the Orchestrator or Coordinators.

Command	Triggered By	Example Payload
`StartMicroserviceAssembly`	Orchestrator	`{ serviceName: "NotificationService" }`
`GenerateAdapter`	Coordinator	`{ adapter: "INotificationRepository" }`
`RouteTaskToAgent`	Orchestrator	`{ skill: "GenerateHandler", traceId: ... }`
`CreatePullRequest`	Orchestrator	`{ repo: "svc-notification", branch: "feature/..." }`

📤 Events¶

Events represent something that has happened. They are published by agents and coordinators.

Event	Emitted By	Description
`PortsAndUseCasesReady`	Enterprise Architect Agent	DDD architecture emitted for service
`HandlerReady`	Backend Developer Agent	Handler has been generated
`AdapterGenerated`	Adapter Generator Agent	Adapter complete
`MicroserviceAssemblyCompleted`	Coordinator	Full lifecycle for service is done
`TestResultsReady`	QA Agent	Unit/integration tests passed

🔄 Orchestrator Event Flow Example¶

sequenceDiagram
    participant UI
    participant Orchestrator
    participant MAC as MicroserviceAssemblyCoordinator
    participant Agent as Backend Dev Agent

    UI->>Orchestrator: ProductIntentSubmitted
    Orchestrator->>MAC: StartMicroserviceAssembly
    MAC->>Orchestrator: AdapterNeeded
    Orchestrator->>Agent: RouteTaskToAgent (GenerateAdapter)
    Agent->>Orchestrator: AdapterGenerated
    Orchestrator->>MAC: AdapterReady
    MAC->>Orchestrator: MicroserviceAssemblyCompleted

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

🛰️ Event Bus Integration¶

Implementation	Details
Broker	Azure Service Bus or RabbitMQ
Middleware	MassTransit (message routing + FSM support)
Message Format	JSON-based contracts with tracing envelope
Error Handling	Retry policies, dead-letter queue, fallback routing
Security	Signed/encrypted payloads, tenant-aware envelopes

📦 Standard Envelope Format¶

Every command or event includes the orchestration envelope:

{
  "traceId": "notif-001",
  "sessionId": "notif-service-assembly-01",
  "componentTag": "BackendDevAgent:GenerateHandler",
  "timestamp": "2025-05-04T13:10:00Z",
  "payload": { ... }
}

✅ Advantages of Event-Driven Orchestration¶

Benefit	Impact
Agent Decoupling	Agents evolve independently; only contracts matter
Resilience	Retries and error isolation without full failure
Scalability	Easily parallelize 1000s of builds
Observability	Each event is logged, traced, and searchable
Auditability	Event history provides a full audit trail of software generation

🔁 Retry & Fallback¶

When events or commands fail:

MassTransit handles retries (x-retries, exponential backoff)
Orchestrator may emit FallbackToAgent, RouteToHumanReview
Failed flows emit TaskFailed or SessionTerminated events

🧠 Cycle 7: Task Intake & Intent Decomposition¶

This section details how the Orchestration Layer processes initial product input (such as prompts, blueprints, or APIs) and decomposes it into a structured, semantic task plan that can be distributed to agents and coordinators.

This is the starting point for all autonomous workflows in the ConnectSoft AI Software Factory.

🎯 Input Sources¶

Task intake can originate from:

Source	Example
UI Prompt	"Build NotificationService with MongoDB"
API Request	JSON blueprint POSTed to Orchestration Gateway
System-Level Trigger	Internal pipeline invoking new product build
Predefined Template	Reuse of stored blueprint with version/edits

📩 Entry Message¶

All inputs are wrapped into a common event:

`ProductIntentSubmitted`¶

{
  "traceId": "notif-001",
  "userPrompt": "Build a NotificationService with MongoDB and JWT auth",
  "source": "UI",
  "priority": "normal",
  "submittedAt": "2025-05-04T10:02:00Z"
}

The Orchestrator uses this as the root of the execution plan.

🔍 Step 1: Blueprint Inference (Optional)¶

If not pre-supplied, the orchestrator dispatches the prompt to:

→ `Vision Architect Agent`¶

Task:

{
  "command": "GenerateVisionBlueprint",
  "prompt": "Build NotificationService with MongoDB..."
}

Resulting event:

`StrategicBlueprintGenerated`¶

{
  "blueprintId": "blueprint-abc123",
  "modules": ["NotificationService"],
  "capabilities": ["Send", "Retrieve", "Mark as Read"],
  "storage": "MongoDB",
  "auth": "JWT"
}

🧠 Step 2: Semantic Task Planning¶

The orchestrator now builds a task plan from the blueprint. This includes:

Extracted From	Used To Plan
`modules[]`	One `MicroserviceAssemblyCoordinator` per module
`capabilities[]`	Use case handlers
`storage`	Adapter type (`MongoRepository`)
`auth`	Security layer (`AddJwtAuth`)
`blueprintId`	Global trace context

🛠 Step 3: Generate Task Plan¶

Each unit of work becomes a task dispatch. Examples:

Command	Receiver	Purpose
`StartMicroserviceAssembly`	`MicroserviceAssemblyCoordinator`	Begin service assembly
`GenerateUseCaseHandler`	`Backend Developer Agent`	Implement `SendNotificationHandler`
`GenerateAdapter`	`Adapter Generator Agent`	Build `INotificationRepository`
`ConfigureJWTAuth`	`Security Architect Agent`	Add auth setup
`CreatePullRequest`	`PR Agent`	Integrate assembled code

🧾 Task Dispatch Format¶

Each task includes the blueprint context and trace data:

{
  "traceId": "notif-001",
  "sessionId": "svc-notification-01",
  "componentTag": "AdapterGeneratorAgent:INotificationRepository",
  "taskType": "GenerateAdapter",
  "blueprintRef": "blueprint-abc123",
  "metadata": {
    "storage": "MongoDB",
    "serviceName": "NotificationService"
  }
}

🧱 Planner Logic (Orchestration Subsystem)¶

Subsystem	Role
`IntentParser`	Parses user prompt or blueprint
`TaskPlanner`	Breaks blueprint into atomic tasks
`TaskRouter`	Decides best-fit agent for each task
`SessionManager`	Assigns `traceId`, `sessionId` for each task stream

✅ Benefits of This Model¶

Benefit	Description
Fully Declarative Input	Prompts or JSON blueprints are enough to generate full delivery plan
Pluggable Task Types	New tasks can be introduced without changing orchestrator core
Scalable Work Decomposition	Thousands of tasks can be generated and distributed independently
Traceable & Observable	All tasks are trace-bound and self-contained
Multi-Agent Compatible	Output flows can span 10s–100s of agents and coordinators

🧪 Real Example Trace¶

Prompt: “Build UserProfileService with SQL Server and role-based access”

Generated task stream:

StartMicroserviceAssembly("UserProfileService")
GenerateHandler("CreateUserHandler")
GenerateHandler("GetUserByIdHandler")
GenerateAdapter("IUserRepository")
ConfigureRBAC("UserProfileService")
CommitCode
RunTests
CreatePullRequest

Each routed independently with full trace metadata.

🧭 Cycle 8: Semantic Routing of Tasks¶

Once a task plan is generated (from a blueprint or user intent), the Orchestration Layer must route each task to the most appropriate agent or coordinator. This is done through a semantic routing engine — a pluggable system that matches tasks with agents based on skills, domains, capabilities, and runtime health.

This cycle details how routing decisions are made intelligently and dynamically in ConnectSoft.

🧠 Semantic Routing Model¶

Semantic routing is built on the idea that tasks describe what needs to be done, and agents advertise what they’re good at.

Routing Inputs:¶

taskType (e.g., GenerateHandler)
domain (e.g., NotificationService)
blueprintMetadata (e.g., storage: MongoDB)
agentRegistry (skills, availability, domain coverage)

Routing Output:¶

agentId to send the task to

🗂 Task Envelope for Routing¶

{
  "taskType": "GenerateAdapter",
  "service": "NotificationService",
  "domain": "Notifications",
  "blueprintId": "blueprint-abc123",
  "traceId": "notif-001"
}

📚 Agent Capability Metadata¶

Each agent registers its capabilities at startup:

{
  "agentId": "adapter-generator-2",
  "agentType": "Adapter Generator Agent",
  "skills": ["GenerateAdapter", "CreateRepository"],
  "domains": ["Notifications", "Users", "Payments"],
  "status": "Healthy"
}

⚙️ Routing Algorithm (Simplified)¶

var candidates = agentRegistry
    .Where(a => a.Skills.Contains(task.TaskType))
    .Where(a => a.Domains.Contains(task.Domain))
    .Where(a => a.Status == "Healthy")
    .OrderBy(a => a.Load)
    .ToList();

var selectedAgent = candidates.FirstOrDefault();

You may enhance this with:

Skill weight scoring
Domain proximity
Agent versioning or capability tiering
Context-based fallback

🛠 Subsystem: TaskRouter¶

The TaskRouter is a core component inside the Orchestrator. It:

Receives tasks from the Planner
Queries the Agent Registry
Emits RouteTaskToAgent commands with full session context
Logs decision trails for observability

📡 Routing Command Format¶

`RouteTaskToAgent`¶

{
  "agentId": "adapter-generator-2",
  "traceId": "notif-001",
  "sessionId": "notif-service-assembly-01",
  "taskType": "GenerateAdapter",
  "blueprintId": "blueprint-abc123",
  "componentTag": "AdapterGeneratorAgent:INotificationRepository",
  "payload": {
    "interface": "INotificationRepository",
    "storage": "MongoDB"
  }
}

✅ Routing Outcomes¶

Outcome	Description
✅ Routed	Best-fit agent found and task assigned
🔁 Reroute	Primary agent failed, fallback found
⏸️ Escalated	No agent available → human review
❌ Dropped	Error reported or task failed permanently

📈 Observability & Auditing¶

Each routing decision is:

Logged in the Orchestrator
Emitted as an event: TaskRouted, AgentNotFound, RoutingFallbackTriggered
Propagated into telemetry (OpenTelemetry spans)
Recorded in task lineage reports (via traceId)

🧪 Real Example: UserProfileService¶

Task: GenerateHandler("CreateUserHandler")

Available agents:

✅ backend-dev-1: skills = ["GenerateHandler"], domains = ["Users", "Invoices"]
❌ backend-dev-2: offline
❌ backend-dev-3: not skilled in this domain

✅ Selected → backend-dev-1

Routing result:

{
  "event": "TaskRouted",
  "toAgent": "backend-dev-1",
  "componentTag": "BackendDevAgent:CreateUserHandler",
  "traceId": "userprofile-002"
}

🔁 Fallback Strategy¶

If routing fails:

Retry 3x (configurable)
Reroute to secondary agent
Escalate to FallbackAgent or HumanReviewerAgent
Emit TaskRoutingFailed or TaskEscalated

🔁 Cycle 9: Command and Event Lifecycle¶

In the ConnectSoft AI Software Factory, every orchestration action — from microservice generation to frontend wiring — is driven by a command-event lifecycle. This lifecycle enables complete decoupling, traceability, and reactivity across agents and coordinators in a multi-agent system.

This cycle documents the structure, lifecycle, and rules governing commands and events.

🧩 Core Concepts¶

Term	Description
Command	A directed instruction: “Do this” — always has an intended target
Event	A fact: “This happened” — emitted by agents or coordinators after completing a task
Envelope	Metadata attached to all messages (traceId, sessionId, componentTag, etc.)
Bus	Transport layer: Azure Service Bus / RabbitMQ via MassTransit
Observer	Subsystems or agents that listen to events (e.g., for logging, chaining, or recovery)

📬 Command Structure¶

{
  "type": "GenerateHandler",
  "traceId": "svc-002",
  "sessionId": "svc-002-handlergen",
  "componentTag": "BackendDevAgent:CreateUserHandler",
  "targetAgent": "backend-dev-1",
  "payload": {
    "service": "UserProfileService",
    "handler": "CreateUserHandler",
    "useCase": "CreateUser"
  }
}

Command Properties:

type: task/command name
targetAgent: who should process it (determined via routing)
payload: domain-specific input
traceId / sessionId: orchestration-level context
componentTag: for telemetry and logs

📢 Event Structure¶

{
  "type": "HandlerReady",
  "traceId": "svc-002",
  "sessionId": "svc-002-handlergen",
  "componentTag": "BackendDevAgent:CreateUserHandler",
  "emittedBy": "backend-dev-1",
  "timestamp": "2025-05-04T13:32:00Z",
  "payload": {
    "file": "CreateUserHandler.cs",
    "status": "completed"
  }
}

Event Properties:

type: domain event (post-action)
emittedBy: agent or coordinator
timestamp: event time
payload: result or reference
Fully traceable via traceId, sessionId, and componentTag

🛠 Lifecycle Flow (Simplified)¶

1. Task planned → Orchestrator emits `Command: GenerateHandler`
2. Agent receives & processes → emits `Event: HandlerReady`
3. Orchestrator consumes event → updates state, triggers next task
4. Event observers log, trace, and react (e.g., QA phase, PR phase)

🧠 Example Message Sequence¶

sequenceDiagram
    participant Orchestrator
    participant Agent as Backend Dev Agent
    participant Coordinator as Assembly Coordinator

    Orchestrator->>Agent: GenerateHandler (Command)
    Agent-->>Orchestrator: HandlerReady (Event)
    Orchestrator->>Coordinator: HandlerReady
    Coordinator-->>Orchestrator: AssemblyStageCompleted

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

🧾 Naming Conventions¶

Message Type	Pattern
Command	`VerbNoun` (e.g., `GenerateHandler`, `CreatePullRequest`)
Event	`NounVerb` (e.g., `HandlerReady`, `TestsPassed`, `AdapterGenerated`)
Envelope Fields	camelCase (`traceId`, `componentTag`, `sessionId`)

🔒 Message Envelope Requirements¶

All orchestration messages MUST include:

Field	Description
`traceId`	Global execution trace
`sessionId`	Scoped task/session ID
`componentTag`	Identifies source/role
`timestamp`	UTC ISO 8601
`payload`	Domain/task-specific content

🎯 Routing Rules¶

Rule	Description
Commands	Routed directly to target agent or coordinator
Events	Broadcast to orchestrator, observers, and subscribed listeners
Internal Events	Used inside coordinators (e.g., state transitions)
Global Events	Used for cross-agent orchestration triggers (`AssemblyCompleted`)

📡 Integration with Bus¶

Layer	Tool
Message Broker	Azure Service Bus or RabbitMQ
Bus Middleware	MassTransit (with saga/coordinator support)
Event Store (optional)	Azure Table Storage, MongoDB, or PostgreSQL for audit logs

✅ Benefits of Command-Event Design¶

Benefit	Description
Loose Coupling	Senders and receivers don't depend on direct invocation
Traceability	Every step logged and span-connected
Resilience	Failures don’t break entire flows; they emit observable `TaskFailed`
Observability	Events can be replayed, analyzed, or routed to dashboards
Modularity	New agents or tasks can be added by emitting or subscribing to events

⚠️ Cycle 10: Failure Handling & Recovery¶

In an autonomous, distributed AI Software Factory, failures are expected — agents may timeout, coordinators may stall, events may be dropped, and tasks may fail due to misalignment or incomplete context. The orchestration layer must not only detect these failures but also respond intelligently and autonomously.

This cycle outlines how ConnectSoft’s Orchestration Layer handles execution failures, recovers workflows, and preserves delivery SLAs.

💥 What Can Fail?¶

Failure Type	Example
Agent Timeout	`HandlerReady` not received within 60s
Coordinator Crash	`MicroserviceAssemblyCoordinator` FSM terminated unexpectedly
Task Error	`GenerateAdapter` throws exception (invalid interface definition)
Routing Gap	No healthy agent found to handle `GenerateHandler`
Event Dropped	Message not consumed due to network/broker issue
Cascading Failure	One failed task prevents downstream handlers from running

🧠 Orchestration Failure Detection¶

1. Task Timeout Monitoring¶

Each dispatched command has a TTL/SLA (e.g., 60s, 5m)
If no response event received → emit TaskTimedOut

2. Command Execution Failure¶

If agent returns TaskFailed, orchestrator logs and evaluates fallback

3. Routing Failures¶

If no agent can handle a task → RoutingFailed

4. Coordinator FSM Abandonment¶

Coordinator crashes mid-way or enters invalid state → CoordinatorHalted

🔁 Recovery Strategies¶

Scenario	Recovery Action
Agent timeout	Retry task with same agent (1–3 times, exponential backoff)
Agent unhealthy	Re-route task to alternate agent with same skill
Command failed	Route to fallback agent, or escalate
Coordinator crashed	Restart FSM (MassTransit saga rehydration)
Event lost	Re-emit or rehydrate from event store
Unrecoverable	Emit `TaskEscalated` to trigger human-in-the-loop path

📬 Failure Events¶

Event Name	Trigger
`TaskTimedOut`	No event received within SLA
`TaskFailed`	Agent emitted explicit failure
`RoutingFailed`	No valid agent found
`CoordinatorHalted`	Coordinator FSM crashed
`TaskEscalated`	Orchestrator escalated task for human review

Each includes full trace metadata:

{
  "event": "TaskTimedOut",
  "traceId": "svc-002",
  "sessionId": "svc-002-handlergen",
  "componentTag": "BackendDevAgent:CreateUserHandler",
  "elapsedMs": 61000
}

⚙️ Subsystems Involved¶

Subsystem	Role
`SLAWatcher`	Monitors task SLA windows
`RetryManager`	Handles retries and backoff logic
`FallbackRouter`	Finds alternative agents or coordinators
`EscalationService`	Triggers notifications or UI-based handoffs
`DeadLetterQueue`	Captures terminally failed messages for inspection

✅ Recovery Policy Examples¶

Task	Agent	Failure	Action
`GenerateHandler`	backend-dev-1	Timeout	Retry once, reroute to backend-dev-2
`ConfigureJWTAuth`	security-arch-1	Exception	Escalate to SecurityReviewAgent
`AdapterGenerated`	—	Event dropped	Re-emit from event store
`AssemblyCoordinator`	—	FSM halted	Restart saga instance, restore state

🔍 Monitoring Dashboards¶

Failures are visualized and tracked via:

OpenTelemetry alerts
Azure Monitor or Grafana dashboards
Trace Explorer logs per traceId
Dead-letter queues (MassTransit + broker)
SLA breach reports per service/blueprint

🧯 Human-In-The-Loop Path¶

When all retries/fallbacks fail:

Orchestrator emits TaskEscalated
Message routed to HumanReviewerAgent or UI system
Admin may approve, modify, or manually complete the task
Resume orchestration flow

🤝 Cycle 11: Multi-Agent Coordination Patterns¶

The ConnectSoft Orchestration Layer must coordinate multiple autonomous agents — often in parallel — to deliver complete, interdependent software systems. This cycle explores key multi-agent patterns used in orchestration flows, including sequential, parallel, conditional, and iterative coordination models.

🧠 Coordination Principles¶

Agents are decoupled but linked by shared trace/session metadata
Orchestration layer handles flow sequencing, not task logic
Coordination happens through event chaining and task dependencies
Some agents may be coordinated directly (via RouteTaskToAgent) or indirectly (via coordinator events)

🔄 Coordination Pattern Types¶

1. Sequential (Waterfall)¶

Task A must complete before Task B starts

Example:

GenerateUseCaseHandler → then → GenerateAdapter → then → CommitCode

graph TD
  A[UseCase Handler] --> B[Adapter] --> C[Commit Code]

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

2. Parallel (Fan-Out)¶

Multiple tasks executed in parallel, orchestrator waits for all

Example:

Generate Create, Update, and GetById handlers simultaneously

graph TD
  A[Start Orchestration] --> B1[CreateHandler]
  A --> B2[UpdateHandler]
  A --> B3[GetByIdHandler]
  B1 --> C[Commit Code]
  B2 --> C
  B3 --> C

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

3. Conditional (Branching)¶

Route based on context, blueprint, or previous event outcome

Example:

If blueprint specifies "auth": "JWT", route to ConfigureJWTAuth

graph TD
  A[Blueprint Analyzed]
  A -->|Auth = JWT| B[ConfigureJWTAuth]
  A -->|Auth = OAuth| C[ConfigureOAuthAuth]

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

4. Iterative (Per Item Fan-Out)¶

For each use case or port in the blueprint, emit a new task

Example:

For each defined port → generate adapter and handler

graph TD
  A[Ports = 3] --> B1[GenerateAdapter:Port1]
  A --> B2[GenerateAdapter:Port2]
  A --> B3[GenerateAdapter:Port3]

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

🛠 Orchestration Flow Model¶

Each pattern uses orchestration constructs:

Construct	Role
`RouteTaskToAgent`	Initiates agent task with trace context
`TaskRouted`, `TaskCompleted`	Used to trigger next step or join point
`TaskGroup`	Logical group of tasks (e.g., `useCaseHandlers[]`)
`JoinStrategy`	Wait-all, wait-any, or N-of-M before continuing
`ContextEvaluator`	Decides if/which branch to activate based on blueprint/metadata

✅ Example: NotificationService Flow (Simplified)¶

Step	Pattern	Agents Involved
1. `GenerateVisionBlueprint`	Sequential	Vision Architect Agent
2. `GenerateUseCaseHandlers[]`	Parallel	Backend Dev Agents
3. `GenerateAdapters[]`	Parallel	Adapter Generator Agents
4. `ConfigureAuth`	Conditional	Security Architect Agent
5. `RunTests` → `CreatePR`	Sequential	QA Agent → PR Agent

🔁 Join/Wait Logic¶

Orchestrator tracks task group completions:

{
  "taskGroupId": "notif-usecases",
  "expectedCount": 3,
  "completed": ["CreateHandler", "UpdateHandler", "GetByIdHandler"],
  "status": "complete"
}

Once completed:

Trigger downstream task: CommitCode
Emit TaskGroupCompleted

🔍 Advanced Use Cases¶

Scenario	Pattern	Coordination
Multi-microservice system	Parallel (per service)	1 coordinator per service
API Gateway + SPA + Mobile	Parallel + Conditional	Based on frontend flags
Retry N-of-M tasks	Iterative + Wait N	Accept 2 of 3 tasks before moving on
Blue/Green deployment	Conditional	Branch for CI/CD steps

📱 Cycle 12: Coordinating Frontend, API Gateway, and Mobile in Parallel¶

When generating a full-stack SaaS product, the Orchestration Layer must coordinate multiple surface layers in parallel:

API Gateway (e.g., for routing/auth)
SPA Web Frontend
Mobile App (Cross-platform)

This cycle demonstrates how ConnectSoft's orchestrator uses parallel, conditional, and join-based coordination patterns to deliver a fully assembled front door to the system.

🧠 Scenario Setup¶

Input Prompt: “Build a BookingSystem with API Gateway, a mobile app, and a responsive web portal (SPA). Use OAuth2 auth.”

Blueprint inference returns:

{
  "modules": ["BookingService", "UserService"],
  "frontend": {
    "spa": true,
    "mobile": true
  },
  "gateway": {
    "enabled": true,
    "auth": "OAuth2"
  }
}

🔁 Orchestration Flow Overview¶

1. Start System Assembly¶

Command: StartSystemAssembly
traceId: booking-001

2. In Parallel:¶

Start MicroserviceAssemblyCoordinator for each service
Start FrontendCoordinator for SPA
Start MobileAppCoordinator
Start ApiGatewayCoordinator

📡 Flow Diagram¶

flowchart TD
    A[ProductIntentSubmitted] --> B[StartSystemAssembly]
    B --> MS1[BookingService Coordinator]
    B --> MS2[UserService Coordinator]
    B --> SPA[SPA Frontend Coordinator]
    B --> MOBILE[Mobile App Coordinator]
    B --> GW[API Gateway Coordinator]
    MS1 --> PR[CreatePullRequest]
    SPA --> PR
    MOBILE --> PR
    GW --> PR

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

All coordinators emit events like:

FrontendGenerated
GatewayConfigured
MobileAppReady
PullRequestCreated

These events are tracked by the orchestrator using a TaskGroup with a wait-all strategy.

🛠 TaskGroup Coordination¶

`taskGroupId`: booking-ui-stack¶

Expected Events	Source
`FrontendGenerated`	SPA Agent
`MobileAppReady`	Mobile Agent
`GatewayConfigured`	API Gateway Agent

Join Point:¶

Once all 3 are complete → Orchestrator triggers:

Command: CreatePullRequest

✅ Example Event Payload¶

{
  "event": "FrontendGenerated",
  "traceId": "booking-001",
  "componentTag": "FrontendSPAAgent:BookingPortal",
  "sessionId": "spa-booking-001",
  "filesGenerated": 42
}

🧠 Conditional Execution Example¶

If blueprint only includes "spa": true, then:

Orchestrator skips MobileAppCoordinator
SPA + Gateway still run in parallel
Join logic waits only for defined artifacts

📈 Observability Across Surface Layers¶

Each session logs and traces:

Coordinator	SessionId	Span Name
SPA Frontend	`spa-booking-001`	`Frontend:GenerateSPA`
Mobile	`mobile-booking-001`	`Mobile:GenerateFlutterApp`
API Gateway	`gateway-booking-001`	`Gateway:ConfigureRoutes`

All are tied together by traceId: booking-001.

🧾 Final Assembly Event¶

When all layers complete:

{
  "event": "FullStackReady",
  "traceId": "booking-001",
  "components": ["SPA", "Mobile", "Gateway"],
  "prLink": "https://dev.azure.com/.../pullrequest/235"
}

🧪 Summary of Parallel Coordination¶

Task	Coordinator	Pattern
`Frontend SPA`	`FrontendCoordinator`	Parallel
`Mobile App`	`MobileAppCoordinator`	Parallel
`API Gateway`	`ApiGatewayCoordinator`	Parallel
`Join & PR`	Orchestrator	Join-All

🧩 Cycle 13: Conditional Execution Based on Blueprint Metadata¶

The ConnectSoft Orchestration Layer is designed to adaptively execute based on blueprint metadata — activating, skipping, or rerouting tasks depending on user prompts, inferred structure, or explicit config.

This cycle explains how orchestration uses conditional logic gates to make execution dynamic, blueprint-aware, and minimal-by-design.

🧠 Why Conditional Execution?¶

Blueprints may define optional or variant behaviors:

Metadata Field	Affects
`"auth": "JWT"`	Auth strategy: use `ConfigureJWTAuth`
`"mobile": true`	Launch `MobileAppCoordinator`
`"platform": "Flutter"`	Select Flutter Agent
`"gateway": { enabled: false }`	Skip API Gateway
`"features": ["booking", "calendar"]`	Add feature-specific flows
`"spa": false`	Do not generate web frontend

🔀 Execution Decision Flow¶

flowchart TD
    BP[Blueprint Ingested]
    BP --> AUTH{auth defined?}
    AUTH -->|JWT| AJWT[Route to ConfigureJWTAuth]
    AUTH -->|OAuth2| AOAUTH[Route to ConfigureOAuth2]
    AUTH -->|false| SKIPAUTH[Skip Auth Coordination]

    BP --> MOBILE{mobile enabled?}
    MOBILE -->|true| MMOB[Start Mobile Coordinator]
    MOBILE -->|false| SKIPMOB[Skip Mobile]

    BP --> GATEWAY{gateway.enabled}
    GATEWAY -->|true| GWCOORD[Start GatewayCoord]
    GATEWAY -->|false| SKIPGW[Skip Gateway]

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

🛠 Orchestration Subsystems Involved¶

Subsystem	Role
`BlueprintInterpreter`	Parses structure and flags
`ContextEvaluator`	Evaluates conditional logic gates
`RoutePlanner`	Only includes enabled paths in task plan
`JoinManager`	Dynamically tracks expected task completions per context

✅ Real Example: Auth Routing¶

Given:

"auth": "OAuth2"

Orchestration will:

Skip ConfigureJWTAuth
Route to SecurityArchitectAgent with:

{ "taskType": "ConfigureOAuth2", ... }

If:

"auth": false

Then:

No auth agent flows triggered
Coordinator is instructed to skip auth setup phase

🔁 Feature Flag Conditionals¶

Example:

"features": ["search", "calendar"]

Triggers:

SearchModuleCoordinator
CalendarEventServiceCoordinator

Skipped:

NotificationsCoordinator (not listed)
RemindersService (not listed)

📊 Dynamic Join Tracking¶

If blueprint only includes:

"spa": true,
"mobile": false,
"gateway": true

Then orchestration waits for:

FrontendGenerated
GatewayConfigured

But not:

MobileAppReady

Join group is built dynamically per taskGroupId.

🧠 Decision DSL (internal)¶

You can define blueprint rules declaratively:

if:
  metadata.auth == "OAuth2"
then:
  route: ConfigureOAuth2
else if:
  metadata.auth == "JWT"
then:
  route: ConfigureJWTAuth
else:
  skip: true

🧠 Cycle 14: Coordinator Blueprints and Internal FSM Design¶

Domain-specific coordinators (e.g., MicroserviceAssemblyCoordinator, ApiGatewayCoordinator) encapsulate the stateful logic of assembling their targets using an internal FSM (finite state machine). These coordinators operate as sub-orchestrators, executing only on a scoped blueprint fragment.

This cycle details how these coordinators:

Interpret service blueprints
Emit task commands
Transition between well-defined states
Recover from partial executions

🧩 Coordinator Responsibilities¶

Responsibility	Description
💡 Interpret Fragment	Parse service/module-specific blueprint section
🔁 FSM Execution	Progress through domain-defined lifecycle states
🧾 Emit Events	Notify orchestrator when stages complete or fail
📦 Track Artifacts	Monitor generated handlers, adapters, DTOs, etc.
🔄 Retry & Recovery	Reissue commands or resume from state snapshot
🧠 Decentralized Control	Act independently within orchestration trace context

🗂 Coordinator Blueprint Input (Fragment Example)¶

{
  "serviceName": "NotificationService",
  "useCases": ["SendNotification", "GetNotification"],
  "storage": "MongoDB",
  "auth": "JWT",
  "features": ["scheduling"]
}

This is passed to the coordinator via:

Command: StartMicroserviceAssembly
payload: { ...above... }

🔁 Coordinator FSM (MassTransit Saga)¶

MicroserviceAssemblyCoordinator FSM:

stateDiagram-v2
    [*] --> Scaffolding
    Scaffolding --> PortsReady
    PortsReady --> UseCasesReady
    UseCasesReady --> AdapterReady
    AdapterReady --> AuthReady
    AuthReady --> TestsReady
    TestsReady --> PRCreated
    PRCreated --> Complete

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

Each state emits events:

State	Event
`PortsReady`	`PortsAndUseCasesReady`
`AdapterReady`	`AdapterGenerated`
`AuthReady`	`AuthConfigured`
`PRCreated`	`PullRequestCreated`

🧠 State Transitions Logic¶

FSM is implemented via MassTransit Saga:

Each transition waits for a specific event (e.g., HandlerReady)
Timeout and retry logic built in per state
Event handling is idempotent and resume-capable

During(UseCasesReady)
    .When(AdapterGenerated)
    .TransitionTo(AdapterReady)
    .Publish(new ConfigureAuth(...));

📊 Artifact Tracker¶

Each coordinator tracks internal sub-tasks and artifacts:

{
  "expectedHandlers": 2,
  "handlersCompleted": 2,
  "adaptersGenerated": 1,
  "authConfigured": true
}

When all required elements complete → AssemblyCompleted is emitted.

🛠 Resilience & Replayability¶

Feature	Mechanism
Task retry	Task TTL + requeue logic
FSM checkpoint	Saga state persisted in Mongo/Postgres
Resume logic	Replay messages from last known state
Idempotency	All handler events have unique IDs + dedup logic

✅ Coordinator Completion¶

Final state:

{
  "event": "MicroserviceAssemblyCompleted",
  "traceId": "notif-001",
  "sessionId": "notif-service-01",
  "service": "NotificationService",
  "pullRequest": "https://dev.azure.com/.../pr/442"
}

Triggers downstream actions:

Orchestrator emits RunTests
QA Agent is routed automatically

🧱 Cycle 15: Coordinators for Cross-Cutting Concerns (Auth, Storage, Observability)¶

In ConnectSoft, cross-cutting concerns like authentication, persistence, logging, and metrics are managed by specialized coordinators, not embedded into service-specific flows. This enables:

Centralized patterns for common infrastructure
Isolated FSMs for reusability
Pluggable execution based on blueprint metadata

This cycle explains how these coordinators are structured and integrated.

🧠 Why Use Dedicated Coordinators?¶

Benefit	Description
🔁 Reusability	`ConfigureJWTAuth` logic used across all services
🧩 Modularity	Cross-cutting concerns stay outside of domain logic
🔍 Observability	Instrumentation added uniformly via `ObservabilityCoordinator`
🛡️ Security	All auth logic is encapsulated and versioned independently
🧠 Extensibility	Coordinators can support multiple strategies (JWT, OAuth, Keycloak, etc.)

📦 Example Coordinators¶

Name	Purpose
AuthCoordinator	Handles security layer setup (e.g., JWT, OAuth2)
StorageCoordinator	Configures NHibernate/Mongo/SQL persistence
ObservabilityCoordinator	Adds tracing, logging, metrics instrumentation
SecretsCoordinator	Integrates Azure Key Vault or HashiCorp Vault
ResilienceCoordinator	Adds retries, circuit breakers, timeout patterns
FeatureFlagsCoordinator	Adds LaunchDarkly or Azure App Config wiring

🔁 Coordination Flow (Example: JWT)¶

sequenceDiagram
    participant Orchestrator
    participant AuthCoord
    participant Agent as Security Architect Agent

    Orchestrator->>AuthCoord: StartAuthAssembly
    AuthCoord->>Agent: ConfigureJWTAuth
    Agent-->>AuthCoord: AuthConfigured
    AuthCoord-->>Orchestrator: AuthLayerReady

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

Blueprint specifies "auth": "JWT"
Orchestrator dispatches to AuthCoordinator
Coordinator triggers ConfigureJWTAuth via agent
Response is tracked and reported back to orchestrator

📄 AuthCoordinator FSM¶

stateDiagram-v2
    [*] --> AuthPending
    AuthPending --> AuthConfigured
    AuthConfigured --> Complete

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

Similar FSM design as microservice coordinator
Timeout, retry, fallback all supported
Can be versioned independently per auth mechanism

🛠 Blueprint Mapping (Auth Example)¶

{
  "auth": {
    "strategy": "OAuth2",
    "provider": "AzureAD"
  }
}

→ Maps to:

AuthCoordinator → ConfigureOAuth2Auth
Passes provider: AzureAD to select appropriate strategy

✅ Resulting Event¶

{
  "event": "AuthLayerReady",
  "strategy": "OAuth2",
  "provider": "AzureAD",
  "componentTag": "AuthCoordinator",
  "traceId": "booking-001"
}

Triggers:

RunTests if all other coordinators complete
Optional SecurityReviewAgent for audits

📊 Tracking Across Concerns¶

Each coordinator emits:

Started, Configured, Ready, and Failed events
Owns sessionId and participates in shared traceId

Join strategy waits for:

TaskGroup: `infra-setup`
`AuthLayerReady`
`StorageReady`
`ObservabilityReady`

💡 Reusability Strategy¶

These coordinators live in separate solutions:

Each has:
MassTransit-based FSM
Well-defined contracts (ConfigureOAuth2, StorageReady)
Sub-agent mappings (e.g., StorageAgent)

→ Maintains separation of orchestration layers by domain vs infrastructure.

🚀 Cycle 16: Pull Request, Testing, and CI/CD Coordination¶

After microservices and cross-cutting layers are assembled, the ConnectSoft Orchestration Layer coordinates integration, testing, and CI/CD actions. This cycle outlines the post-assembly phase: turning generated artifacts into working code in source control, running tests, and optionally triggering deployments.

🧠 Key Concepts¶

Component	Role
PR Agent	Creates pull requests from generated code branches
QA Agent	Runs unit, integration, and service tests
CI Coordinator	Integrates with Azure Pipelines or GitHub Actions
Release Agent	Handles pre-release tasks (tagging, publishing, documentation)

🛠 Flow Overview¶

sequenceDiagram
    participant Orchestrator
    participant Coordinator as MicroserviceAssemblyCoordinator
    participant PR as PR Agent
    participant QA as QA Agent
    participant CI as CI Coordinator

    Coordinator-->>Orchestrator: MicroserviceAssemblyCompleted
    Orchestrator->>QA: RunTests
    QA-->>Orchestrator: TestResultsReady
    Orchestrator->>PR: CreatePullRequest
    PR-->>Orchestrator: PullRequestCreated
    Orchestrator->>CI: TriggerPipeline

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

📦 Task Group: `postAssemblyFlow`¶

Triggered by:¶

Event: MicroserviceAssemblyCompleted

Tasks:¶

Task	Agent/Coordinator
`RunTests`	QA Agent
`CreatePullRequest`	PR Agent
`TriggerPipeline`	CI Coordinator

🧪 Test Flow (QA Agent)¶

Command: RunTests
{
  "service": "NotificationService",
  "testScope": ["Unit", "Integration"],
  "branch": "feature/notif-001"
}

Expected:

Event: TestResultsReady
{
  "result": "Passed",
  "coverage": "92%",
  "traceId": "notif-001"
}

🔀 Pull Request Flow (PR Agent)¶

Command: CreatePullRequest
{
  "branch": "feature/notif-001",
  "targetRepo": "svc-notification",
  "title": "feat(notification): initial implementation"
}

Returns:

Event: PullRequestCreated
{
  "url": "https://dev.azure.com/.../pullrequest/332",
  "status": "open"
}

⚙️ CI/CD Coordination¶

Triggered by:¶

PullRequestCreated (or TestResultsReady)
Blueprint may include ci: true, autoDeploy: false, etc.

Command: TriggerPipeline
{
  "pipelineId": "ci-notification",
  "branch": "feature/notif-001",
  "buildConfig": "Debug"
}

📜 Optional Post-PR Tasks¶

Task	Agent
`GenerateChangelog`	Release Agent
`NotifyStakeholders`	Notification Agent
`CreateReleaseTag`	Release Agent
`AutoMergeOnApprove`	GitOps Agent

🧠 Blueprint Control Example¶

"ci": true,
"autoDeploy": false,
"testScope": ["Unit", "E2E"]

Triggers:

Run unit + E2E tests
Create PR, do not auto-merge
Trigger CI pipeline, skip deploy

✅ Summary of Post-Assembly Coordination¶

Step	Component
✅ `RunTests`	QA Agent
✅ `CreatePullRequest`	PR Agent
✅ `TriggerPipeline`	CI Coordinator
✅ `PublishRelease` (optional)	Release Agent

All steps are:

Traced under traceId
Session-bound (postassembly-notif-001)
Observed via TaskGroup: postAssemblyFlow

🏗️ Cycle 17: Blueprint-Driven Infrastructure Provisioning (IaC)¶

Many SaaS solutions require cloud infrastructure provisioning alongside service generation. ConnectSoft handles this via blueprint-driven orchestration flows, triggering Infrastructure-as-Code (IaC) Coordinators that work with agents skilled in Bicep, Terraform, ARM, and Azure DevOps environment creation.

This cycle covers how infrastructure plans are derived, managed, and integrated into delivery pipelines.

🧩 When IaC Is Triggered¶

Blueprint-Driven¶

{
  "infrastructure": {
    "provision": true,
    "cloud": "Azure",
    "services": ["AppService", "PostgreSQL", "KeyVault"],
    "region": "westeurope",
    "strategy": "Bicep"
  }
}

→ Orchestrator triggers:

Command: StartInfrastructureProvisioning

🛠 Key Coordinators¶

Coordinator	Purpose
IaCCoordinator	Main FSM to manage infra plan creation & apply
SecretsCoordinator	Bootstrap secrets stores (e.g., Azure Key Vault)
NetworkingCoordinator	VNet, DNS, IP, routing setup
StorageCoordinator	DB, blob, cache provisioning
MonitoringCoordinator	Logging, tracing, alerting infra (e.g., App Insights)

🧱 IaC FSM Example¶

stateDiagram-v2
    [*] --> PlanGenerated
    PlanGenerated --> Approved
    Approved --> ResourcesCreated
    ResourcesCreated --> Complete

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

Events:¶

IaCPlanGenerated
ProvisioningApproved
ResourcesProvisioned

🧪 Example IaC Flow (Azure + Bicep)¶

Command: StartInfrastructureProvisioning
{
  "strategy": "Bicep",
  "services": ["AppService", "PostgreSQL"],
  "region": "westeurope"
}

Coordinated Steps:

Generate Bicep templates
Validate and simulate (what-if)
Await approval (manual or auto)
Apply via Azure CLI or DevOps pipeline
Emit ResourcesProvisioned

🔐 Secrets Coordination¶

{
  "vault": "notif-app-kv",
  "secrets": [
    { "name": "JwtSigningKey", "source": "Random256" },
    { "name": "DbPassword", "source": "Generated" }
  ]
}

SecretsCoordinator will:

Provision vault
Generate or pull values
Store securely
Emit SecretsProvisioned with references

📂 Resulting Events¶

{
  "event": "ResourcesProvisioned",
  "strategy": "Bicep",
  "resources": ["notif-appservice", "notif-db", "notif-kv"],
  "traceId": "notif-001"
}

Triggers:

Service config updates (e.g., connection strings)
Readiness signals for QA or release phases

📦 IaC Strategies Supported¶

Strategy	Toolchain	Notes
Bicep	Azure CLI, AZ DevOps	Native to Azure, declarative
Terraform	azurerm provider	Multi-cloud capable
ARM	Legacy fallback	Complex, verbose
Pulumi (planned)	TS/.NET Infra-as-Code	Future support

🧠 Blueprint-to-Infrastructure Mapping¶

Blueprint Field	Infrastructure Action
`"storage": "PostgreSQL"`	Provision Azure Database for PostgreSQL
`"auth": "OAuth2"`	Add App Registration in Entra ID
`"monitoring": true`	Add AppInsights, log analytics
`"autoscale": true`	Configure App Service autoscaling rules

✅ Benefits of IaC Coordination¶

Benefit	Description
☁️ Full Cloud Automation	All infra components built from orchestration
🔁 Repeatable	Idempotent and versioned IaC plans
🧩 Blueprint-Aware	Aligned to service needs, not static templates
🔐 Secure by Design	Secrets, RBAC, and networks centrally handled
🧪 Testable	All infra plans support `plan` and `apply` phases

📊 Cycle 18: Observability, Tracing, and Telemetry in Orchestration¶

To operate autonomously and at scale, the ConnectSoft Orchestration Layer requires deep observability: tracing every command, event, agent action, and failure across thousands of tasks and flows.

This cycle explains how observability is implemented using OpenTelemetry, structured logs, span-based tracing, and agent-level metrics.

📡 Key Observability Pillars¶

Pillar	Implementation
📈 Traces	Distributed across commands, agents, coordinators
📋 Logs	Structured JSON logs with context (traceId, sessionId, componentTag)
📉 Metrics	Collected from agents, queues, coordinator FSMs
🔍 Dashboards	Visualizations via Grafana, Azure Monitor, or Kibana

🔁 Span-Based Tracing Model¶

Each orchestration traceId contains:

Parent: ProductIntentSubmitted
Child spans:
- StartMicroserviceAssembly
- GenerateHandler
- AdapterGenerated
- CreatePullRequest
- RunTests

All spans propagate:

{
  "traceId": "booking-001",
  "spanId": "generate-handler-1",
  "parentSpanId": "start-assembly",
  "componentTag": "BackendDevAgent:CreateBookingHandler",
  "status": "completed",
  "durationMs": 1842
}

🧾 Log Format (per Event or Command)¶

{
  "timestamp": "2025-05-04T14:22:00Z",
  "level": "Information",
  "traceId": "booking-001",
  "sessionId": "svc-booking-001",
  "component": "PR Agent",
  "event": "PullRequestCreated",
  "message": "Created PR #442 for BookingService"
}

Emitted via Serilog (JSON sink)
Correlates with task outcomes, coordinator transitions, retries, etc.

📟 Metrics Model¶

Metric	Labels	Source
`orchestrator_task_duration_ms`	`taskType`, `agentId`	Orchestrator
`agent_error_count`	`agentType`, `errorType`	Each agent
`fsm_state_transitions_total`	`coordinatorName`, `state`	Coordinators
`command_queue_length`	`commandType`	MassTransit queues

Prometheus-compatible endpoints are exposed for scraping.

📊 Example Grafana Dashboard Panels¶

Task durations per agent
Failed task count per service/module
Active coordinator sessions by state
Top failing commands/events
SLA breaches (timeouts)

🧠 Integration Points¶

Component	Observability Hook
Orchestrator	Logs, spans, metrics for all command dispatches
Agents	Emit `AgentActionStarted`, `AgentActionCompleted`
Coordinators	FSM transition logging and metrics
MassTransit	Built-in diagnostics for queue size, retry, DLQ
CI/CD	Pipeline run logs linked by `traceId`

🔐 Sensitive Data Handling¶

PII removed from logs
Secrets masked in all command/event payloads
Sensitive config stored via secure logging keys

📈 SLA & Health Monitoring¶

Orchestrator monitors:

task SLA breaches
agent health checks (ping or heartbeat)
DLQ overflow warnings
retry exhaustion thresholds

Alerts can be routed to:

Ops dashboard
Incident response bot
Email/SMS

✅ Benefits of Unified Observability¶

Benefit	Impact
🔎 Full Traceability	Every build, task, and flow can be reconstructed
🚨 Fast Failure Detection	Agents failing or coordinators crashing → instant alerts
📊 Optimization	Long-running tasks or bottlenecks are visible
🧪 Test Coverage	QA and post-assembly flows are traced and validated
🔐 Audit Trail	Immutable event logs tied to blueprint and session IDs

🙋‍♂️ Cycle 19: Human-in-the-Loop & Approval Escalation Paths¶

While ConnectSoft aims for fully autonomous software delivery, some actions require human confirmation, intervention, or override — especially for:

High-risk configurations (auth, infrastructure)
Missing data
Unresolvable errors
Compliance-driven reviews

This cycle outlines how the orchestration layer supports manual approval, escalation paths, and human-agent collaboration using TaskEscalated, ApprovalRequired, and dedicated HumanReviewAgent.

🧠 Human-In-The-Loop Scenarios¶

Scenario	Trigger
❌ All agents failed	`RoutingFailed` → `TaskEscalated`
⏱️ SLA expired	`TaskTimedOut` → `EscalationPolicy = manual`
🚨 Sensitive task	Blueprint specifies `approvalRequired: true`
🧩 Incomplete blueprint	Orchestrator can’t decompose properly
🧾 Release or PR approval	Manual gate required by org policy

🛠 Event-Driven Escalation¶

Example:¶

{
  "event": "TaskEscalated",
  "traceId": "svc-345",
  "taskType": "ConfigureOAuth2",
  "reason": "Agent unavailable",
  "escalationTarget": "HumanReviewerAgent"
}

This is routed to a HumanReviewAgent via a UI, Slack, Teams, or web portal.

👤 HumanReviewerAgent Responsibilities¶

Task	Description
📝 Complete missing blueprint fields
✅ Approve plan or infrastructure
🔄 Reroute failed task manually
✍️ Provide override values
🔚 Cancel or pause orchestration flow

📩 Command for Manual Approval¶

`RequestApproval`¶

{
  "sessionId": "infra-notif-001",
  "target": "User",
  "reason": "Provisioning PostgreSQL in production",
  "payload": {
    "templateName": "infra-prod-notif.bicep",
    "resources": ["PostgreSQL", "KeyVault"]
  }
}

User approves via connected UI → emits:

`ApprovalGranted`¶

{
  "approvedBy": "user@connectsoft.ai",
  "timestamp": "...",
  "traceId": "notif-001"
}

🛡️ Approval Flow Diagram¶

sequenceDiagram
    participant Orchestrator
    participant Agent as IaC Coordinator
    participant Human as HumanReviewerAgent

    Agent->>Orchestrator: RequestApproval
    Orchestrator->>Human: ApprovalRequired
    Human-->>Orchestrator: ApprovalGranted
    Orchestrator->>Agent: ResumeProvisioning

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

📋 UI & UX Integration¶

Review dashboards with:
- Task details
- Blueprint fragment
- Action buttons: Approve, Reject, Edit
Built-in audit logs
Integrated with GitHub Checks, Azure DevOps gates, or Slack approvals

🔁 Retry & Resume After Approval¶

When approval granted:

Original coordinator resumes from saved FSM state
Commands regenerated with approved payload
All context preserved (traceId, sessionId, taskGroup)

✅ Benefits of Escalation Pathing¶

Benefit	Impact
🛑 Safe Boundaries	Humans intervene at risky junctions
🔁 Flow Continuity	Failed automation doesn’t stall full pipeline
🔐 Governance	Policy approval enforced without manual micromanagement
💬 Collaboration	Human + agent system for flexible delivery

📦 Cycle 20: Modularization and Packaging of Orchestration Components¶

To support independent evolution, versioning, and reuse at scale, the ConnectSoft orchestration layer is designed as a modular system. Coordinators, agents, FSMs, schemas, and blueprints are developed in separate repositories and solutions, versioned and released independently.

This final cycle describes the principles and practices for modularizing and distributing orchestration components.

🧱 Modular Component Types¶

Component	Description	Repo/Unit
Coordinator FSMs	MassTransit Sagas (e.g., `MicroserviceAssembly`, `Auth`, `Infra`)	`ConnectSoft.Orchestration.Coordinators.*`
Task & Event Schemas	Shared envelope, message contracts	`ConnectSoft.Contracts.Orchestration`
Task Handlers	Reusable AI agent handlers	`ConnectSoft.Agents.*`
Blueprint Templates	JSON/YAML fragments for coordinators	`ConnectSoft.Blueprints`
DSL Condition Rules	YAML or JSON conditions for conditional routing	`ConnectSoft.Orchestration.Rules`
Deployment Scripts	Bicep, Terraform IaC	`ConnectSoft.Infrastructure.*`

🛠 Packaging Format¶

Tooling	Purpose
.NET NuGet Packages	For coordinators, contracts, and orchestration libraries
JSON/YAML Blueprint Registry	For task plans and coordination specs
Docker Images	For coordinators running as durable workers
Helm Charts / Bicep Modules	For deploying orchestrators and agents to AKS

🧩 Coordinator Repo Structure (Example)¶

/ConnectSoft.Orchestration.Coordinators.MicroserviceAssembly/
│
├── StateMachines/
│   └── MicroserviceAssemblyState.cs
├── Contracts/
│   └── StartMicroserviceAssembly.cs
├── Events/
│   └── MicroserviceAssemblyCompleted.cs
├── Blueprints/
│   └── microservice-template.json
├── README.md
├── nuspec/
└── tests/

Versioned independently:

Semantic versioning (v2.1.0)
Compatibility matrix to platform SDK

🧪 Blueprint Template Example¶

{
  "name": "NotificationService",
  "useCases": ["Send", "MarkRead"],
  "auth": "JWT",
  "storage": "MongoDB",
  "infra": {
    "provision": true,
    "region": "westeurope"
  }
}

Used to:

Trigger coordinators
Define orchestrator routing
Control agent behavior

🧠 Runtime Composition¶

At runtime, orchestrator loads:

Coordinator assemblies via DI
Blueprint from artifact registry or user prompt
Routing rules from condition engine
Agents from discovery metadata registry

All combined into execution manifest per trace/session.

🧰 Development & CI Practices¶

Practice	Description
✅ Unit tests	FSM transitions, message contract compliance
✅ Integration tests	Agent+Coordinator E2E
✅ Versioning	Coordinators are independently released via Git tags
✅ Reusability	Coordinators can be embedded into other orchestrations
✅ Canary Support	Multiple coordinator versions co-exist for testing

📜 GitOps & Deployment Patterns¶

Orchestrator core deployed once (canary/stable)
Coordinators packaged as:
- .nupkg for internal SDK
- .dll plugins for runtime loading
- docker image for long-running durable workers
Coordinators declare compatibility via:

{
  "sdk": "Orchestrator v3",
  "contracts": "v1.5",
  "blueprintSchema": "v2.2"
}

✅ Benefits of Modularization¶

Benefit	Outcome
🔁 Replaceable	Coordinators or agents can be swapped per domain
🔄 Upgradable	Coordinators evolve independently (e.g., v1 vs v2 for Infra)
♻️ Reusable	Same InfraCoordinator used across 30+ microservices
🔬 Testable	Each module has clear CI contract tests
🧩 Composable	Multiple orchestrators can share agent libraries and flow logic

Runtime & Execution¶

Runtime & Control Plane Overview — Operational view of Factory runtime
Control Plane — Control plane vs data plane separation
Execution Engine — How runs and jobs are executed through the queueing model
Failure & Recovery — Failure handling and recovery patterns

Architecture & Design¶

Overall Platform Architecture — High-level Factory architecture
Agentic System Design — Multi-agent system architecture
Knowledge and Memory System — Knowledge storage and retrieval

🧠 Orchestration Layer¶

Overview¶

Role in the ConnectSoft AI Software Factory¶

Core Capabilities¶

Why It Matters¶

🧠 Introduction to the Orchestration Layer¶

🛠 Key Capabilities¶

🧠 What It Is Not¶

✅ Example: From Prompt to System¶

🔁 Interfaces¶

🔍 Key Design Principles¶

🧭 Core Responsibilities of the Orchestration Layer¶

🧩 High-Level Responsibility Areas¶

🛠 Functional Responsibilities by Category¶

1. 🧠 Session & Identity Management¶

2. ⚙️ Task Planning & Breakdown¶

3. 🛰️ Agent Routing¶

4. 📩 Event Subscription & Dispatch¶

5. 🔍 Observability & Telemetry¶

6. 🔄 Failure Detection & Recovery¶

🧱 Modular Structure¶

✅ Example: NotificationService¶

🔍 Design Rules Recap¶

🤖 Agent-Aware Orchestration¶

🧠 What Makes Orchestration “Agent-Aware”?¶

🧩 Agent Metadata Model¶

🔁 Orchestration Task → Agent Flow¶

Example Task: GenerateHandler: SendNotificationHandler¶

🎯 Agent Selection Rules¶

📚 Skill Registry Example (Internal Dictionary)¶

🛠 Example Orchestration Logic¶

✅ Benefits of Agent-Aware Orchestration¶

🔍 Real-World Example: WellnessTrack¶

🧾 Sessions and Traces¶

🧩 Core Concepts¶

🧠 Session Lifecycle¶

Step-by-Step¶

🧾 Message Metadata Format¶

🔍 Tracing Across Boundaries¶

1. Across Agents¶

2. Across Coordinators¶

3. Across Systems¶

📊 Observability Injection¶

✅ Real-World Example¶

🧱 Best Practices¶

🧭 Orchestrator vs Coordinator — Division of Responsibility¶

🧩 High-Level Comparison¶

🛠 Real Responsibilities¶

Orchestration Layer¶

Microservice Assembly Coordinator (MAC)¶

📊 Visual: Division of Responsibility¶

🧠 Why This Separation Matters¶

✅ Real-World Trace Example: NotificationService¶

📡 Cycle 6: Event-Driven Architecture in Orchestration¶

⚙️ Event-Driven Design Principles¶

🛠 Core Message Types¶

📥 Commands¶

📤 Events¶

🔄 Orchestrator Event Flow Example¶

🛰️ Event Bus Integration¶

📦 Standard Envelope Format¶

✅ Advantages of Event-Driven Orchestration¶

🔁 Retry & Fallback¶

🧠 Cycle 7: Task Intake & Intent Decomposition¶

🎯 Input Sources¶

📩 Entry Message¶

ProductIntentSubmitted¶

🔍 Step 1: Blueprint Inference (Optional)¶

→ Vision Architect Agent¶

StrategicBlueprintGenerated¶

🧠 Step 2: Semantic Task Planning¶

🛠 Step 3: Generate Task Plan¶

🧾 Task Dispatch Format¶

🧱 Planner Logic (Orchestration Subsystem)¶

✅ Benefits of This Model¶

🧪 Real Example Trace¶

🧭 Cycle 8: Semantic Routing of Tasks¶

🧠 Semantic Routing Model¶

Routing Inputs:¶

Routing Output:¶

Example Task: `GenerateHandler: SendNotificationHandler`¶

✅ Real-World Trace Example: `NotificationService`¶

`ProductIntentSubmitted`¶

→ `Vision Architect Agent`¶

`StrategicBlueprintGenerated`¶

`RouteTaskToAgent`¶

`taskGroupId`: booking-ui-stack¶