🧭 Vision and Product Planning Agents Overview¶

📌 Introduction¶

The Vision and Product Planning Agent Cluster is the strategic entrypoint of the ConnectSoft AI Software Factory.

These agents operate in the pre-architecture phase, transforming raw human intent into a complete, validated product strategy. Their collaboration anchors all downstream domains — from clean architectural modeling to microservice generation, CI/CD, and deployment.

This cluster defines what the software must achieve, who it serves, and how success will be measured, ensuring traceability and alignment throughout the AI-driven system creation lifecycle.

🔍 Why It Matters¶

Without clearly articulated and structured product vision and planning:

Software lacks strategic coherence
Teams suffer from misaligned expectations
Engineering and QA cycles waste time on rework
Delivery outcomes drift from business value

By automating the conversion of idea → vision → plan → stories, ConnectSoft achieves:

Goal	Outcome
Traceability	Every feature traces to a problem, persona, and metric
AI-Autonomy	Agents act on validated plans, not vague prompts
Scalability	1000s of agents can coordinate using common planning protocols
Governance	Structured vision and planning documents become system-of-record artifacts

🎯 Objectives of This Document¶

This overview document provides:

A clear understanding of the four primary planning agents
Their roles, responsibilities, and outputs
How they collaborate, sequence, and handoff
Diagrams and event-driven flows illustrating traceable behavior
Insights into observability, skills, and semantic routing
The foundation for extending or auditing this critical domain

🧱 Agent Cluster Composition¶

Agent	Core Function
Vision Architect Agent	Converts business ideas into structured software vision documents
Business Analyst Agent	Refines vision into functional requirements, rules, processes
Product Manager Agent	Builds product roadmap, MVP plan, personas, and release phases
Product Owner Agent	Translates plan into backlog-ready user stories and acceptance criteria

Each agent builds upon the outputs of the previous, forming a composable, semantic planning pipeline.

🧭 Position in the Execution Flow¶

The Vision and Product Planning Agent Cluster initiates the structured lifecycle of the ConnectSoft AI Software Factory immediately after a project is provisioned. These agents perform semantic decomposition of ideas into executable product and engineering plans.

While their collaboration typically follows a sequential pattern, the platform supports multiple valid activation flows — depending on the blueprint type, domain, or configuration.

🔄 Standard Event-Driven Activation Flow¶

In the most common pattern, agents activate in a linear cascade:

sequenceDiagram
    participant Orchestrator
    participant VisionArchitect
    participant ProductManager
    participant ProductOwner
    participant BusinessAnalyst

    Orchestrator->>VisionArchitect: `TemplateProvisioned`
    VisionArchitect->>ProductManager: Emit `VisionDocumentCreated`
    ProductManager->>ProductOwner: Emit `ProductPlanCreated`
    ProductOwner->>BusinessAnalyst: Emit `BacklogReady`

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Here, the Business Analyst validates and enriches backlog items post-decomposition with business rules, processes, and gap models.

🔀 Alternative Flow: Early Business Analysis Track¶

In complex domains (e.g., regulated industries), it may be beneficial to run Business Analyst Agent in parallel or before product planning:

sequenceDiagram
    participant Orchestrator
    participant VisionArchitect
    participant BusinessAnalyst
    participant ProductManager
    participant ProductOwner

    Orchestrator->>VisionArchitect: `TemplateProvisioned`
    VisionArchitect->>BusinessAnalyst: Emit `VisionDocumentCreated`
    BusinessAnalyst->>ProductManager: Emit `BusinessRequirementsReady`
    ProductManager->>ProductOwner: Emit `ProductPlanCreated`

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In this model, Business Analyst Agent decomposes Vision Document into formal requirements that act as input to the product plan. This is preferred when:

Functional complexity is high
Detailed business rules are needed early
Compliance or domain constraints must be modeled

🔁 Concurrent Flow (Fork-Join Pattern)¶

Both the Product Manager and Business Analyst can consume the VisionDocument concurrently:

flowchart TD
    VisionArchitect --> VisionDocument
    VisionDocument --> ProductManager
    VisionDocument --> BusinessAnalyst
    ProductManager --> ProductPlan
    BusinessAnalyst --> BusinessRequirements
    ProductPlan & BusinessRequirements --> ProductOwner

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This fork-join model improves speed, parallelism, and resilience when both roles can work independently and merge later under the Product Owner.

🧠 Role of the Orchestration Layer¶

The orchestration layer is aware of all supported activation modes and determines execution based on:

Project metadata (industry, scope, compliance flags)
Template blueprints (e.g., SaaS Healthcare Template triggers early BA)
Event configuration graph (FSM / conditional routing)

It ensures traceability by emitting consistent sessionId, traceId, and componentTag metadata, regardless of flow type.

🧩 Example Configuration Switch (in `blueprint.config.yaml`)¶

vision:
  concurrentAgents:
    - BusinessAnalystAgent
    - ProductManagerAgent
  sequentialFallback: true

✅ Summary¶

The Business Analyst Agent supports multiple positioning models:
After Product Owner (validation/refinement)
Before Product Manager (early decomposition)
In parallel with Product Manager (fork-join)
The orchestration layer selects the most appropriate execution path.
All flows are fully observable, event-driven, and traceable.

🧱 Agent Cluster Summary Table¶

The Vision and Product Planning cluster is composed of four core agents, each responsible for transforming unstructured business ideas into structured, AI-ready execution artifacts. They work together in a semantic chain, producing documents, models, and events consumed by downstream architectural and engineering agents.

This table provides a concise overview of their roles, inputs, outputs, and system events:

📋 Overview Table¶

Agent	Core Role	Consumes	Produces	Emits Event
🧠 Vision Architect Agent	Translates freeform input into structured Vision Document.	Human idea, objective prompt, constraints, use case context.	`vision.md`, `vision.json`, metadata trace block.	`VisionDocumentCreated`
📊 Business Analyst Agent	Extracts structured business requirements, rules, processes.	Vision Document, personas, initial features, domain constraints.	`requirements.md`, `rules.md`, `bpmn.mmd`, `gap-analysis.md`.	`BusinessRequirementsReady`
🎯 Product Manager Agent	Plans product roadmap, MVP cutline, release phases.	Vision Document, requirements (if available), strategic goals, personas.	`product-plan.md`, `features.json`, `epics.yaml`.	`ProductPlanCreated`
📌 Product Owner Agent	Generates backlog, user stories, acceptance criteria.	Product plan, business rules, personas, constraints.	`user-stories.md`, `acceptance-criteria.md`, `story-map.json`.	`BacklogReady`

🧠 Key Properties Across the Cluster¶

Characteristic	Description
Event-Driven	All agents activate via orchestrated event triggers (e.g., `VisionDocumentCreated`).
Traceable	Every artifact includes `traceId`, `sessionId`, and contextual metadata.
Composable	Outputs from one agent become structured inputs for the next.
Recoverable	Agents can retry, correct, or request clarification from orchestrator or human-in-the-loop.
Memory-Augmented	All agents have access to prior project vision, similar domains, or templates via semantic memory.

🧩 Visual Snapshot¶

flowchart LR
    HumanInput["🧑 Human Prompt"] --> VisionArchitect["🧠 Vision Architect Agent"]
    VisionArchitect -->|VisionDocumentCreated| BusinessAnalyst["📊 Business Analyst Agent"]
    VisionArchitect -->|VisionDocumentCreated| ProductManager["🎯 Product Manager Agent"]
    BusinessAnalyst -->|BusinessRequirementsReady| ProductManager
    ProductManager -->|ProductPlanCreated| ProductOwner["📌 Product Owner Agent"]
    ProductOwner -->|BacklogReady| DownstreamAgents["⬇️ Engineering / QA / Architecture"]

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🔄 Agent Collaboration Flow¶

The Vision and Product Planning Agents collaborate through a combination of event-driven messaging, semantic input handoff, and orchestrator-managed sessions. Their interaction forms a traceable, modular pipeline, converting ideas into execution-ready blueprints.

This section visualizes their coordination under various flow models.

🧭 Standard Sequential Collaboration (Linear Cascade)¶

In the most common pattern, agents work in strict sequence, each emitting artifacts and events for the next to consume:

flowchart TD
    A1["🧠 Vision Architect Agent"] -->|`VisionDocumentCreated`| A2["🎯 Product Manager Agent"]
    A2 -->|`ProductPlanCreated`| A3["📌 Product Owner Agent"]
    A3 -->|`BacklogReady`| A4["📊 Business Analyst Agent"]

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💡 Best for: simple SaaS products with clear vision and MVP goals.
🧠 Product Manager works from Vision directly.
🧾 Business Analyst applies rules post-decomposition.

🔀 Alternative Flow: Business-First Track¶

Here, Business Analyst Agent activates before product planning:

flowchart TD
    A1["🧠 Vision Architect Agent"] -->|`VisionDocumentCreated`| A2["📊 Business Analyst Agent"]
    A2 -->|`BusinessRequirementsReady`| A3["🎯 Product Manager Agent"]
    A3 -->|`ProductPlanCreated`| A4["📌 Product Owner Agent"]

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

💡 Best for: regulated domains, ERP-like complexity, policy-heavy applications.
⚙️ Requirements are formalized early to drive planning scope.

🔁 Fork-Join Collaboration (Concurrent Model)¶

In high-parallelism environments, Product Manager and Business Analyst can work simultaneously from Vision output:

flowchart TD
    Vision["🧠 Vision Architect Agent"]
    Vision -->|`VisionDocumentCreated`| PM["🎯 Product Manager Agent"]
    Vision -->|`VisionDocumentCreated`| BA["📊 Business Analyst Agent"]
    PM -->|`ProductPlanCreated`| PO["📌 Product Owner Agent"]
    BA -->|`BusinessRequirementsReady`| PO

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💡 Best for: large projects, short timelines, enterprise back-office systems.
✅ Product Owner joins both inputs into consistent user stories and criteria.

📌 Observability and Event Metadata¶

Each agent emits artifacts and events including:

traceId, sessionId, componentTag
artifact_uri to Markdown/JSON/YAML output
agent_version, timestamp, confidence_score

This ensures replayability, dashboard tracing, and full audit logs across the AI Software Factory lifecycle.

🧠 Vision Architect Agent – Role & Responsibilities¶

The Vision Architect Agent is the origin point for every ConnectSoft project. It transforms unstructured human input — ideas, problems, prompts, goals — into a structured, versioned Vision Document that anchors all downstream work.

It operates autonomously, using internal skills and semantic memory to ensure that even vague prompts are translated into actionable, AI-ready software visions.

🎯 Core Responsibilities¶

Responsibility	Description
Idea Structuring	Parses freeform input (natural language, documents, forms) to extract problem, goals, constraints.
Problem Definition	Formalizes the business or operational problem the software must solve.
Opportunity Framing	Identifies strategic angles: customer value, market fit, innovation potential.
Solution Conceptualization	Drafts a high-level solution approach (what kind of system, for whom, and why).
Software Type Classification	Tags the system: SaaS, mobile app, API, AI system, internal platform, etc.
Initial Feature Mapping	Suggests modular features (e.g., "Appointment Scheduling", "User Profile", "Billing").
Persona Definition	Identifies stakeholders, users, admins, external systems.
Success Metrics Formulation	Lists outcome goals: "Reduce no-shows by 30%", "Launch in 90 days", etc.
Vision Document Generation	Creates a Markdown + JSON artifact with full structured content.
Metadata & Traceability Injection	Adds `traceId`, `software_type`, `domain_tags`, `confidence_score`, and timestamps.
Event Emission	Publishes `VisionDocumentCreated` event for downstream agents.

🧩 Position in Lifecycle¶

flowchart TD
    User["🧑 Human Input"] --> VA["🧠 Vision Architect Agent"]
    VA -->|`vision.md`, `vision.json`| ArtifactStorage
    VA -->|`VisionDocumentCreated`| EventBus
    EventBus --> PM["🎯 Product Manager Agent"]
    EventBus --> BA["📊 Business Analyst Agent"]

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Trigger: TemplateProvisioned or ProjectInitialized
Downstream Consumers: Product Manager, Business Analyst, Enterprise Architect, QA agents

✅ Key Outputs¶

Artifact	Format	Description
`vision.md`	Markdown	Human-readable summary of system vision
`vision.json`	JSON	Machine-consumable structured version
Metadata	JSON	Includes `traceId`, `software_type`, `confidence_score`, etc.
Event	`VisionDocumentCreated`	Includes artifact URI, traceability, classification

💡 Example Use Case¶

Prompt: "I want a platform where pet clinic admins can manage appointments and billing for multiple locations."

The Vision Architect Agent might produce:

Software Type: ["SaaS", "Mobile App Extension"]
Personas: ["Pet Owner", "Clinic Admin", "Billing Manager"]
Features: ["Appointment Scheduling", "Multi-Clinic Calendar", "Billing Reports"]
Metrics: ["Reduce admin time by 25%", "Support launch in Q2 2026"]

📘 Summary¶

The Vision Architect Agent enables intent-to-blueprint automation.
Its artifacts are the entrypoint into all subsequent planning, modeling, and engineering.
It provides both human-readable and machine-consumable outputs, wrapped in traceable, observable events.

🧠 Vision Architect Agent – Execution Flow Diagram¶

The Vision Architect Agent follows a modular, observable execution pipeline that transforms high-level prompts into formal Vision Documents. Its execution is autonomous, semantic, and resilient, powered by Semantic Kernel, internal skills, and traceable event protocols.

🛠️ Step-by-Step Execution Lifecycle¶

flowchart TD
    E1["🚀 Event: TemplateProvisioned"] --> A1["🔁 Agent Activated"]
    A1 --> A2["📥 Input Parsing & Context Setup"]
    A2 --> A3["🧠 Skill Orchestration"]
    A3 --> A4["📄 Vision Drafting"]
    A4 --> A5["📋 Metadata Injection"]
    A5 --> A6["✅ Artifact Validation"]
    A6 --> A7["💾 Artifact Storage"]
    A7 --> A8["📤 Emit VisionDocumentCreated"]
    A3 --> O["📊 Observability: Spans, Logs, Metrics"]
    A4 --> O
    A6 --> O
    A8 --> O

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🔍 Execution Phases Explained¶

Phase	Description
1. Activation	Orchestrator emits `StartAgentSession: VisionArchitectAgent` after `TemplateProvisioned`.
2. Input Parsing	Agent reads: project name, use case prompt, business objective, runtime constraints.
3. Skill Orchestration	Composes a graph of skills like `ProblemExtractor`, `PersonaGenerator`, `MetricFormulator`.
4. Vision Drafting	Generates both `vision.md` (human-readable) and `vision.json` (structured metadata).
5. Metadata Injection	Adds `traceId`, `sessionId`, `projectId`, `agentVersion`, `domainTags`, `softwareType`, `confidenceScore`.
6. Validation	Structural schema validation + semantic consistency checks using internal validation skill.
7. Storage	Saves artifacts to blob, Git, and semantic memory (vector DB).
8. Event Emission	Emits `VisionDocumentCreated`, including links to both artifacts and metadata envelope.
Observability	Spans (`CreateVisionDocument`, `StoreArtifact`), structured logs (`Vision document created`), metrics (`vision_agent_success_total`) emitted.

📥 Inputs Required¶

project-metadata.json
User prompt or objective
Use case seed prompt
Optional: template type, constraints, runtime metadata

📦 Outputs Produced¶

File	Description
`vision.md`	Primary vision artifact (problem, goals, solution idea, personas, etc.)
`vision.json`	Structured version for machine agents
`vision.metadata.json`	Trace + classification metadata
`event: VisionDocumentCreated`	Routed to Event Bus

🧠 Key Execution Technologies¶

Component	Role
Semantic Kernel Planner	Orchestrates LLM-backed skills in a reusable graph
OpenAI / Azure OpenAI	Used for reasoning, classification, summarization
Blob Storage / Git	Artifact persistence
OpenTelemetry + Prometheus	Execution trace, logs, and metrics emission
Skill Registry	Skills loaded dynamically for `ProblemExtraction`, `PersonaGeneration`, etc.

✅ Execution Properties¶

📎 Fully traceable: traceId, sessionId, agentId
🧠 Fully semantic: reasoning and skill planning built-in
🧪 Fully validated: all outputs must pass semantic + structural checks
📈 Fully observable: spans, logs, metrics, and event hooks included

📥 Vision Architect Agent – Inputs & Outputs¶

The Vision Architect Agent consumes natural language prompts and project metadata, then produces structured, validated vision artifacts. Its input parsing and output emission follow the AI Software Factory's semantic and observability-first standards.

📥 Inputs Consumed¶

Input	Source	Purpose
Project Metadata	Created at project initialization (`project-metadata.json`)	Contains project name, industry, objective, scope, constraints.
Use Case Prompt	Human or external API input	Freeform business idea to ground the Vision Document.
Template Info	From template provisioner	Blueprint selected (e.g., Microservice Template, API Gateway).
Runtime Constraints	Optional	Time-to-market, budget, MVP scope, compliance flags.
Semantic Memory (optional)	Vector DB or MCP	Past successful vision documents and domain patterns.

🔄 Example Input Payload¶

{
  "traceId": "clinic-2025-001",
  "projectId": "proj-71a9",
  "projectName": "VetPracticeApp",
  "businessObjective": "Help clinics manage appointments more efficiently",
  "industry": "Healthcare",
  "useCasePrompt": "Let clinic admins and pet owners manage bookings online",
  "templateType": "Microservice Template",
  "constraints": ["HIPAA", "Launch in Q3"]
}

📤 Outputs Produced¶

Artifact	Format	Description
`vision.md`	Markdown	Full narrative vision document (problem, opportunity, personas, goals, initial features).
`vision.json`	JSON	Structured representation (software type, features, stakeholder matrix, goals).
`vision.metadata.json`	JSON	Metadata block: `traceId`, `projectId`, `softwareType`, `confidenceScore`, `agentId`.
`VisionDocumentCreated`	Event	Routed to Event Bus with `artifact_uri`, `sessionId`, `timestamp`, and `traceability` info.

📘 Example Vision Document (Excerpt – `vision.md`)¶

# 🧠 Vision: VetPracticeApp

## Problem
Clinics struggle to manage appointment demand and no-shows using outdated tools.

## Goal
Enable patients and staff to manage appointments through a modern, mobile-friendly interface.

## Personas
- 🐾 Pet Owner: wants to book/cancel/reschedule from phone.
- 👩‍⚕️ Clinic Admin: wants calendar view, bulk confirmation, notification triggers.

## High-Level Features
- Appointment Scheduling
- Calendar Management
- Notification System
- Billing Integration

## Outcome Metrics
- Reduce admin workload by 30%
- Launch MVP in 90 days
- HIPAA compliant

🧠 Metadata Block (Excerpt – `vision.metadata.json`)¶

{
  "traceId": "clinic-2025-001",
  "projectId": "proj-71a9",
  "softwareType": ["SaaS", "Calendar Platform"],
  "domainTags": ["healthcare", "booking", "scheduling"],
  "confidenceScore": 0.91,
  "agentVersion": "v1.2.4",
  "artifactUri": "https://blob.connectsoft.ai/vision/clinic-2025-001/vision.md"
}

📣 Event: VisionDocumentCreated¶

{
  "event": "VisionDocumentCreated",
  "traceId": "clinic-2025-001",
  "sessionId": "vision-session-0341",
  "artifactUri": "https://blob.connectsoft.ai/vision/clinic-2025-001/vision.md",
  "producedAt": "2025-05-12T09:00:00Z"
}

This event activates:

✅ Product Manager Agent
✅ Business Analyst Agent
✅ (Optional) Enterprise Architect Agent (if early modeling mode is enabled)

✅ Summary¶

Inputs are simple, high-signal prompts and project context.
Outputs are modular, machine-consumable, traceable artifacts.
All results are stored, versioned, validated, and event-emitted with full observability.

🎯 Product Manager Agent – Role & Responsibilities¶

The Product Manager Agent is the strategic planner of the ConnectSoft AI Software Factory. It consumes the structured vision from the Vision Architect and transforms it into a goal-driven product roadmap with features, MVP cutlines, and release phases.

This agent ensures that software is not only feasible but also valuable, aligned to personas, and delivery-focused.

🧭 Core Responsibilities¶

Responsibility	Description
Goal Decomposition	Breaks down strategic goals from the Vision Document into measurable product outcomes.
Feature Definition	Identifies modular capabilities (features) aligned with personas and use cases.
MVP Extraction	Prioritizes a minimum viable product set to satisfy core goals under time/budget constraints.
Persona-to-Feature Mapping	Connects each feature to the personas it benefits and the problems it solves.
Release Planning	Groups features into phased delivery milestones: MVP, vNext, Stretch Goals.
Risk Awareness	Highlights blockers, uncertainties, and downstream dependencies.
Event Emission	Emits `ProductPlanCreated` and optionally `MVPDefined` for downstream agents.

🧩 Position in the Factory Lifecycle¶

flowchart TD
    VA["🧠 Vision Architect Agent"] -->|VisionDocumentCreated| PM["🎯 Product Manager Agent"]
    PM -->|ProductPlanCreated| PO["📌 Product Owner Agent"]
    PM -->|ProductPlanCreated| EA["🏗️ Enterprise Architect Agent"]

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Receives traceable Vision artifact as input.
Output activates both planning and architecture tracks.

📘 Sample Product Plan Breakdown¶

Persona	Goal	Feature	Priority
Pet Owner	Book appointment anytime	Mobile Booking Flow	MVP
Clinic Admin	Manage staff calendars	Staff Calendar View	vNext
Billing Manager	Reconcile invoices	Billing Export Tool	Stretch Goal

📦 Core Output Artifacts¶

Artifact	Format	Description
`product-plan.md`	Markdown	Human-readable overview of goals, personas, features, priorities.
`features.json`	JSON	Structured list of epics, features, personas, outcomes, priority tags.
`epics.yaml`	YAML	Epic-to-feature-to-user story tree (used by Product Owner Agent).
Event: `ProductPlanCreated`	JSON	Sent to orchestrator and next agents with metadata + artifact link.

🧠 Alignment with ConnectSoft Principles¶

Principle	Contribution
Domain Traceability	Every feature maps to a persona, vision goal, and strategic constraint.
Modular Outputs	Features are atomic, portable, and re-usable across editions.
Event-Driven Workflow	Triggers Product Owner, QA planners, Enterprise Architect.
Cloud-Native Structure	All outputs are stored, versioned, and retrievable via URI.
Observability Embedded	Execution logs, spans, and metrics emitted for traceability.

✅ Summary¶

The Product Manager Agent is the contract of understanding between business needs and implementation.

It ensures that:

🧩 Product scope is clear
🧑‍🤝‍🧑 Persona needs are mapped
🎯 Goals are measurable
🚀 Roadmap is execution-ready

📥 Product Manager Agent – Input Sources Diagram¶

The Product Manager Agent receives a rich, structured context that allows it to plan features, prioritize MVPs, and organize releases in alignment with stakeholder intent and system constraints.

This section documents all inputs consumed by the agent, as well as a diagram of its context initialization pipeline.

📋 Primary Inputs¶

Input	Source	Description
Vision Document (`vision.json`)	Vision Architect Agent	Structured representation of problem, goals, personas, and initial features.
Strategic Goals	Embedded in vision or passed as explicit list	Used to evaluate outcomes and drive goal–feature mapping.
Initial Feature List	From vision or business analyst	Basis for decomposition into MVP, vNext, etc.
Persona Definitions	From vision or analyst agent	Used to align features with real user needs.
Project Constraints	From project metadata	Includes time-to-market, compliance, budget, scope.
Business Requirements (Optional)	From Business Analyst Agent	Refines what must be implemented and when (e.g., "HIPAA-compliant booking").
Semantic Memory (Optional)	Vector DB or MCP server	Suggests roadmap patterns from similar past products.

🧠 Input Parsing Logic¶

The Product Manager Agent:

Extracts features and personas
Classifies goals by impact and feasibility
Normalizes feature descriptions
Computes confidence score for each planning phase
Validates required fields and triggers human-in-the-loop only if needed

🏗️ Visual: Input Context Setup Flow¶

flowchart TD
    VisionDoc["📄 vision.json"] --> ContextBuilder
    Constraints["📋 project-metadata.json"] --> ContextBuilder
    Personas["👥 persona definitions"] --> ContextBuilder
    Features["🧩 initial features"] --> ContextBuilder
    Requirements["📜 business-requirements.md (optional)"] --> ContextBuilder
    Memory["🧠 semantic memory (optional)"] --> ContextBuilder
    ContextBuilder --> PlanComposer["🧠 Product Manager Agent Planning Logic"]

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✅ ContextBuilder phase aggregates inputs into a normalized, validated plan scope.

🧪 Input Example (vision.json excerpt)¶

{
  "projectId": "proj-713",
  "goals": [
    "Reduce no-shows by 25%",
    "Allow appointment scheduling without staff intervention"
  ],
  "personas": ["Pet Owner", "Clinic Admin"],
  "features": [
    { "title": "Mobile Booking Flow", "linkedPersona": "Pet Owner" },
    { "title": "Staff Calendar View", "linkedPersona": "Clinic Admin" }
  ],
  "constraints": ["Launch MVP within 3 months"]
}

📌 Semantic Enrichment (if available)¶

Scenario	Semantic Input
Similar veterinary SaaS found	Suggest reuse of "Reminder Service" and "Multi-Location Calendar"
Goals match prior project	Recommend known MVP slice pattern: `[Booking] + [Reminder]`
Compliance constraint (e.g., GDPR) present	Pull in standard feature validation checklist

✅ Summary¶

The Product Manager Agent is input-rich and inference-capable:

It operates deterministically when input is sufficient.
It leverages past product plans and personas for faster roadmap assembly.
Its entire planning flow is traceable and observable — ensuring explainable prioritization.

📤 Product Manager Agent – Output Flows¶

The Product Manager Agent transforms structured input into a suite of product planning artifacts, each supporting traceability, decomposition, and execution alignment. These outputs become the planning source of truth for Product Owners, Architects, and QA Agents.

📦 Core Artifacts Produced¶

Artifact	Format	Description
`product-plan.md`	Markdown	High-level narrative covering strategic goals, prioritized features, release phases, persona mapping.
`features.json`	JSON	Structured list of features with links to personas, goals, priority levels, and readiness status.
`epics.yaml`	YAML	Epics grouped by persona or feature domain, used by Product Owner Agent to slice into user stories.
`ProductPlanCreated`	Event	Triggers downstream agents (Product Owner, Architecture Agents) and includes trace metadata + URIs.

📘 Example: `product-plan.md`¶

# Product Plan: VetPracticeApp

## 🎯 Strategic Goals
- Reduce clinic admin workload by 25%
- Enable 24/7 appointment self-booking
- Launch MVP within 90 days

## 👥 Personas
- Pet Owner
- Clinic Admin
- Billing Manager

## 🧩 MVP Features
- ✅ Mobile Booking Flow
- ✅ Appointment Calendar View
- ✅ Email Notifications

## 🚀 vNext Features
- Staff Availability Manager
- Integration with CRM

🧠 Example: `features.json`¶

[
  {
    "featureId": "f001",
    "title": "Mobile Booking Flow",
    "linkedPersonas": ["Pet Owner"],
    "priority": "MVP",
    "outcomes": ["Reduce no-shows", "Self-service access"],
    "linkedGoal": "Reduce clinic workload"
  },
  {
    "featureId": "f002",
    "title": "Calendar Management",
    "linkedPersonas": ["Clinic Admin"],
    "priority": "vNext"
  }
]

📊 Feature Phasing Logic¶

Features are grouped into tiers:

Tier	Description
✅ MVP	Required to satisfy core goals and constraints
🔄 vNext	High-value features that follow MVP
🧪 Stretch Goals	Innovation or enhancement scope, possibly deferred

📤 Event: `ProductPlanCreated`¶

{
  "event": "ProductPlanCreated",
  "traceId": "proj-713-v1",
  "artifactUri": "https://blob.connectsoft.ai/product-plans/proj-713/product-plan.md",
  "featuresUri": "https://blob.connectsoft.ai/product-plans/proj-713/features.json",
  "sessionId": "product-manager-session-0021",
  "emittedAt": "2025-05-12T14:02:00Z"
}

Triggers:

📌 Product Owner Agent → to decompose into user stories
🏗️ Enterprise Architect Agent → to model service boundaries and bounded contexts

🧩 Downstream Consumption¶

Agent	Artifact Consumed	Action
Product Owner Agent	`features.json`, `epics.yaml`	Slice into stories, define acceptance criteria
Enterprise Architect Agent	`features.json`	Map to domain services and bounded contexts
QA Planner Agent (optional)	`product-plan.md`	Extract test themes and coverage slices

✅ Summary¶

The Product Manager Agent produces actionable, structured product plans that:

Are aligned with personas and goals
Provide decomposition-ready feature trees
Enable traceable, autonomous downstream execution
Maintain full observability and governance lineage

📌 Product Owner Agent – Role & Responsibilities¶

The Product Owner Agent bridges the gap between strategic planning and actionable engineering. It consumes the Product Plan and feature specifications to produce backlog-ready user stories, acceptance criteria, and epic-to-story mappings that drive implementation flows across engineering agents.

This agent is crucial for translating "what to build" into "how it will be delivered".

🧭 Core Responsibilities¶

Responsibility	Description
Story Decomposition	Breaks down features into detailed, testable user stories.
Epic-to-Story Mapping	Organizes stories under clearly defined epics and value streams.
Persona Alignment	Ensures each story is tied to a persona and user goal.
Acceptance Criteria Definition	Creates BDD-style `Given–When–Then` or formal criteria for validation.
Edition Differentiation (optional)	Adds story metadata for multi-edition delivery (e.g., Starter vs. Enterprise).
Traceability Injection	Links each story back to features, vision goals, personas, and project metadata.
Event Emission	Emits `BacklogReady` or `UserStoriesGenerated` to activate engineering cluster.

🧩 Example Breakdown¶

Feature: “Mobile Booking Flow”

Epic	User Story	Persona	Acceptance Criteria (excerpt)
Appointment Booking	“As a pet owner, I want to book an appointment from my phone…”	Pet Owner	✅ Given I select a date, When I confirm, Then I receive a confirmation message
Appointment Booking	“As a guest user, I want to book without logging in…”	Pet Owner	✅ Given I enter contact info, Then system accepts a booking

📘 Output Summary¶

Output	Format	Description
`user-stories.md`	Markdown	Complete backlog of decomposed stories.
`story-map.json`	JSON	Structured persona → epic → feature → story mapping.
`acceptance-criteria.yaml`	YAML	Formal definitions for test alignment.
Event: `BacklogReady`	Event	Triggers Engineering and QA Agents.

📐 User Story Format Example (Markdown)¶

## 🧾 User Story: Book Appointment Without Login

**Persona**: Pet Owner  
**Epic**: Appointment Booking  
**Goal**: Enable frictionless scheduling

### 📌 Acceptance Criteria
- ✅ Given I’m not signed in, I can enter pet and contact info
- ✅ When I choose a time, the system holds the slot for 5 minutes
- ✅ Then I receive a confirmation and reminder email

📤 Event: BacklogReady¶

{
  "event": "BacklogReady",
  "traceId": "proj-713-v1",
  "storiesUri": "https://blob.connectsoft.ai/backlogs/proj-713/user-stories.md",
  "sessionId": "product-owner-session-0103",
  "emittedAt": "2025-05-12T15:42:00Z"
}

Triggers:

👩‍💻 Engineering Agents (Backend, Frontend)
🧪 QA Agents (BDD test generation)
🏗️ Microservice Assembly Coordinators (if use cases detected)

📎 Traceability Chain¶

Every story is linked to:

Vision Document → goalId
Product Plan → featureId, epicId
Persona → personaId
Project metadata → projectId, traceId

This allows full backward traceability from a microservice to a user story to a vision goal.

✅ Summary¶

The Product Owner Agent delivers:

Decomposed, testable stories
Clear persona–goal–feature mappings
Acceptance criteria for QA and test planning
Observability and traceability across the planning–execution boundary

It serves as the final planning checkpoint before engineering begins.

🔁 Product Owner Agent – Inputs & Event Activation¶

The Product Owner Agent is activated only after the Product Plan is available — containing structured features, epics, and persona mappings. It uses this input to generate backlog-ready user stories and acceptance criteria that support event-driven execution, testing, and scalable engineering orchestration.

This section details the full set of inputs, activation conditions, and how the agent initializes its context.

📥 Inputs Consumed¶

Input	Source	Description
`features.json`	Product Manager Agent	Core feature list with persona links and priority tags (MVP, vNext, Stretch).
`epics.yaml`	Product Manager Agent	Feature → Epic → Persona mappings, used to group stories.
`product-plan.md`	Product Manager Agent	Human-readable version of the product vision, used to extract goals and tone.
`business-requirements.md` (optional)	Business Analyst Agent	Adds rules, validations, or constraints that impact story slicing.
`project-metadata.json`	Platform	For injecting `projectId`, `traceId`, and edition constraints.
`semantic memory` (optional)	Vector DB	Suggests similar stories from prior domains to accelerate decomposition.

⚙️ Event-Driven Activation¶

sequenceDiagram
    participant Orchestrator
    participant ProductManager
    participant ProductOwner
    participant EventBus

    ProductManager->>EventBus: Emit `ProductPlanCreated`
    EventBus->>Orchestrator: Notify
    Orchestrator->>ProductOwner: StartAgentSession
    ProductOwner->>ArtifactStore: Read features.json, epics.yaml
    ProductOwner->>EventBus: Emit `BacklogReady`

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

📘 Example Input Snapshot¶

`features.json`¶

[
  {
    "featureId": "f003",
    "title": "Admin Calendar View",
    "linkedPersonas": ["Clinic Admin"],
    "priority": "vNext"
  }
]

`epics.yaml`¶

- epic: Appointment Booking
  features:
    - Mobile Booking Flow
    - Guest Scheduling Support

🧠 Execution Context Initialization¶

The agent initializes execution using:

traceId, sessionId, and agentId
Skill chain: StorySlicerSkill, CriteriaGeneratorSkill, PersonaAlignerSkill
Planning strategy:
Decompose top-down by epic
Prioritize MVP features
Tag stories with delivery_phase, persona, and goal_link

💡 Semantic Augmentation (Optional)¶

If semantic memory is available, the agent can:

Import successful story patterns (e.g., onboarding, booking, billing)
Auto-align stories with domain language (e.g., “reschedule”, “reminder queue”)
Validate that no critical gaps exist in MVP coverage

🧾 Metadata Tracing Injected¶

Each generated user story includes:

storyId, linkedFeatureId, personaId, traceId, version, deliveryPhase, and originatingAgentId

This ensures downstream agents can:

Build DTOs, handlers, and APIs from exact story scope
Validate and test based on acceptance criteria
Track every change or iteration across planning cycles

✅ Summary¶

Input: Consumes product artifacts, optional business rules, and project metadata.
Trigger: Activated on ProductPlanCreated event via orchestrator.
Output: Structured, traceable backlog with full persona and goal mapping.
Integration: Connects upstream planning to downstream engineering, QA, and release management.

📤 Product Owner Agent – Output Mapping¶

The Product Owner Agent produces structured backlog artifacts that enable engineering, testing, and release planning. It ensures that each story is not only traceable but also aligned to personas, features, and business goals — with well-defined acceptance criteria for autonomous validation.

📦 Core Artifacts Produced¶

Artifact	Format	Description
`user-stories.md`	Markdown	Human-readable backlog organized by epic and persona.
`story-map.json`	JSON	Structured persona → epic → feature → story hierarchy.
`acceptance-criteria.yaml`	YAML	BDD-style `Given–When–Then` blocks per story.
`BacklogReady`	Event	Signals readiness for engineering and test generation.

🧩 Output Relationships¶

flowchart TD
    Feature["📋 Feature: Mobile Booking"] --> Story1["🧾 Story: Book Appointment"]
    Feature --> Story2["🧾 Story: Cancel Appointment"]
    Story1 --> Criteria1["✅ Acceptance: Confirm via mobile"]
    Story2 --> Criteria2["✅ Acceptance: Cancel via email"]

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

Each story:

References a featureId, persona, goal, deliveryPhase
Includes embedded criteria and linked epic
Is tagged with storyId, traceId, projectId, and sessionId

📝 Sample: `user-stories.md`¶

## 🧾 User Story: Book Appointment Without Login

**Persona**: Pet Owner  
**Epic**: Appointment Booking  
**Feature**: Mobile Booking Flow  
**Goal**: Reduce no-shows by 25%  
**Delivery Phase**: MVP

### ✅ Acceptance Criteria
- Given I visit the booking page on mobile, I can select an available slot
- When I confirm the form, I receive a confirmation email
- Then the appointment appears in the clinic calendar

🧠 Sample: `story-map.json`¶

{
  "persona": "Pet Owner",
  "epic": "Appointment Booking",
  "feature": "Mobile Booking Flow",
  "stories": [
    {
      "storyId": "s001",
      "title": "Book Appointment Without Login",
      "deliveryPhase": "MVP"
    },
    {
      "storyId": "s002",
      "title": "Receive Reminder SMS",
      "deliveryPhase": "vNext"
    }
  ]
}

📜 Sample: `acceptance-criteria.yaml`¶

- storyId: s001
  title: Book Appointment Without Login
  criteria:
    - Given I visit the booking page
    - When I fill in contact info and submit
    - Then I receive a confirmation
    - And the slot is marked as booked

📤 Event: `BacklogReady`¶

{
  "event": "BacklogReady",
  "traceId": "proj-713-v1",
  "sessionId": "product-owner-session-0342",
  "artifacts": {
    "userStories": "https://blob.connectsoft.ai/proj-713/user-stories.md",
    "storyMap": "https://blob.connectsoft.ai/proj-713/story-map.json",
    "criteria": "https://blob.connectsoft.ai/proj-713/acceptance-criteria.yaml"
  },
  "emittedAt": "2025-05-12T16:04:00Z"
}

🔗 Downstream Consumers¶

Agent / Component	Use of Output
Engineering Agents	Generate APIs, DTOs, handlers per user story.
QA Agents	Use `acceptance-criteria.yaml` to build automated BDD test cases.
PR/CI Agents	Attach story metadata to PR titles, branch naming, and tracking.

✅ Summary¶

Output maps each feature to multiple persona-aligned stories
Every story is testable, traceable, and ready for autonomous implementation
BacklogReady is the signal to begin build cycles

📊 Business Analyst Agent – Role & Responsibilities¶

The Business Analyst Agent focuses on refining the product concept into formal business logic, processes, and rules. While it can operate before, after, or in parallel to the Product Manager and Product Owner, its main role is to capture the operational realities and constraints of the domain that impact implementation.

This agent ensures that software solutions reflect not just user goals but also business compliance, regulatory, and procedural expectations.

🧭 Core Responsibilities¶

Responsibility	Description
Requirement Extraction	Converts high-level goals into formal business rules, validations, and operational constraints.
Process Modeling	Builds BPMN-style diagrams of business flows (e.g., "Appointment Lifecycle").
Constraint Formalization	Captures mandatory business constraints (e.g., “no double bookings”, “GDPR consent required”).
Gap Analysis	Detects missing features or flows that deviate from business reality.
Glossary and Domain Vocabulary	Generates domain-specific definitions (e.g., “Appointment”, “Schedule Block”, “Confirmed Status”).
Decision Modeling (optional)	Creates DMN-style or tabular rule matrices (e.g., refund policy by status and timeframe).
Validation Hookpoints	Adds domain-specific rules used in test scaffolding or runtime validators.
Event Emission	Emits `BusinessRequirementsReady`, `ProcessMapCreated`, or `GapAnalysisGenerated`.

🧩 Strategic Role in the Factory¶

flowchart LR
    Vision["🧠 Vision Architect Agent"]
    PM["🎯 Product Manager Agent"]
    BA["📊 Business Analyst Agent"]
    PO["📌 Product Owner Agent"]

    Vision --> BA
    Vision --> PM
    BA --> PM
    PM --> PO
    BA --> PO
    BA --> QA["🧪 QA Agent"]
    BA --> EA["🏗️ Enterprise Architect Agent"]

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

💡 Flexible Positioning: May run before Product Manager (regulatory-first), after Product Owner (validation layer), or concurrently.
🧠 Adds non-functional insight often missed in standard feature planning.
⚖️ Enables traceability to legal, financial, and procedural policies.

📘 Example Scenarios¶

Domain	Sample Business Rules
Healthcare	"Appointments must have a confirmed consent record before saving"
Education	"Grades must be entered within 48 hours of course end"
Insurance	"Claims over $5000 must be manually reviewed"
SaaS	"Trial users cannot access premium integrations"

📚 Artifacts Produced¶

Artifact	Format	Description
`business-requirements.md`	Markdown	Formalized requirements from domain analysis.
`rules.yaml`	YAML	Constraint definitions (e.g., validation, compliance).
`bpmn.mmd`	Mermaid	Process diagram: e.g., Booking → Confirm → Reminder → Visit.
`gap-analysis.md`	Markdown	List of missing flows or constraint conflicts.
Event: `BusinessRequirementsReady`	Event	Activates QA, Engineering, Product Owner, or Architecture workflows.

✅ Summary¶

The Business Analyst Agent ensures that the software:

Is compliant and aligned with operational rules
Models actual business flows, not just user goals
Exposes hidden complexity early (e.g., edge cases, constraints)
Enables generation of validators, process engines, and QA scripts

🔁 Business Analyst Agent – Input/Output Contracts¶

The Business Analyst Agent operates on top of previously emitted planning artifacts and semantic prompts, generating structured, rule-oriented outputs used by multiple downstream actors: QA, Engineering, Enterprise Architecture, and Solution Design agents.

This section formalizes the input and output interface of the agent, aligning with ConnectSoft’s traceable, observable, versioned artifact model.

📥 Inputs Consumed¶

Input	Source	Purpose
`vision.json`	Vision Architect Agent	Provides initial goals, personas, and solution direction.
`features.json`, `product-plan.md`	Product Manager Agent	Supplies planned features and priorities.
`user-stories.md`	Product Owner Agent	Aligns rules and constraints with how the system will behave.
`semantic memory` (optional)	Vector DB or MCP	Pulls known domain rules and policies for enrichment.
`domain-templates.yaml` (optional)	Platform templates	Provides reusable constraint patterns (e.g., validation DSLs, common BPMN flows).

📤 Outputs Produced¶

Artifact	Format	Description
`business-requirements.md`	Markdown	Natural language summary of policies, rules, and regulations.
`rules.yaml`	YAML	Constraint block with enforcement mode (validation, runtime, documentation-only).
`bpmn.mmd`	Mermaid	Process maps in business-friendly flowchart format (e.g., Appointment Lifecycle).
`decision-table.json`	JSON	Optional DMN-style rule matrix (e.g., refund eligibility, rescheduling options).
`gap-analysis.md`	Markdown	Table of uncovered, under-specified, or conflicting flows and definitions.
Event: `BusinessRequirementsReady`	Event	Triggers validators, test generators, or PO/Architect refinement.

📘 Example: `rules.yaml`¶

- ruleId: R001
  name: Appointment Requires Consent
  appliesTo: BookingService
  type: Validation
  expression: "booking.consentReceived == true"
  severity: error
  linkedFeature: f001

📘 Example: `bpmn.mmd`¶

flowchart TD
  Start["Start Booking"] --> Validate["Check Consent"]
  Validate -->|yes| Confirm["Confirm Slot"]
  Validate -->|no| Error["Show Error Message"]
  Confirm --> Notify["Trigger Confirmation Email"]
  Notify --> Done["Booking Complete"]

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🧠 Traceability & Metadata Injection¶

Each artifact includes:

traceId, sessionId, ruleSetVersion
Linked feature and persona IDs
generatedByAgentId, emittedAt, confidenceScore

🔁 Event: `BusinessRequirementsReady`¶

{
  "event": "BusinessRequirementsReady",
  "traceId": "proj-713-v1",
  "sessionId": "ba-session-1104",
  "rulesUri": "https://blob.connectsoft.ai/proj-713/rules.yaml",
  "bpmnUri": "https://blob.connectsoft.ai/proj-713/booking-process.mmd",
  "emittedAt": "2025-05-12T17:14:00Z"
}

Activates:

🧪 QA Agents → For generating test scenarios and validation checks
📌 Product Owner Agent → To enhance or realign acceptance criteria
🏗️ Enterprise Architect Agent → For refining bounded context rules and edge cases

✅ Summary¶

Inputs: Combine vision, features, stories, and memory-based patterns
Outputs: Formal rules, process flows, domain terms, and gap analysis
Emission: BusinessRequirementsReady event completes the planning validation loop

🔗 Event-Driven Handoff Chain¶

The Vision and Product Planning cluster operates as a fully event-driven, loosely coupled pipeline, where each agent emits structured outputs and triggers downstream agents via events. This ensures observability, traceability, and modular re-execution.

This section documents the canonical event emission chain, its flow diagram, and how the Orchestration Layer manages agent transitions.

🧭 Canonical Event Chain¶

Step	Agent	Emitted Event	Activates
1️⃣	Vision Architect Agent	`VisionDocumentCreated`	Product Manager Agent, Business Analyst Agent
2️⃣	Business Analyst Agent	`BusinessRequirementsReady`	Product Manager, Product Owner, QA Agent
3️⃣	Product Manager Agent	`ProductPlanCreated`	Product Owner Agent, Enterprise Architect Agent
4️⃣	Product Owner Agent	`BacklogReady`	Engineering Agents, QA Agent, PR/CI Coordinators

📤 Event Envelope Format¶

All events include standard metadata for observability:

{
  "event": "ProductPlanCreated",
  "traceId": "proj-713-v1",
  "sessionId": "product-manager-session-0543",
  "artifactUris": {
    "productPlan": "https://blob.connectsoft.ai/proj-713/product-plan.md"
  },
  "producedBy": "ProductManagerAgent",
  "emittedAt": "2025-05-12T16:44:00Z"
}

🔁 Visual Sequence Flow¶

sequenceDiagram
    participant Orchestrator
    participant VisionArchitect
    participant BusinessAnalyst
    participant ProductManager
    participant ProductOwner
    participant EventBus

    Orchestrator->>VisionArchitect: StartAgentSession
    VisionArchitect->>EventBus: Emit `VisionDocumentCreated`
    EventBus-->>BusinessAnalyst: Trigger (optional parallel)
    EventBus-->>ProductManager: Trigger

    ProductManager->>EventBus: Emit `ProductPlanCreated`
    BusinessAnalyst->>EventBus: Emit `BusinessRequirementsReady`

    EventBus-->>ProductOwner: Trigger
    ProductOwner->>EventBus: Emit `BacklogReady`

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

📊 Observability Across Events¶

Each event is:

Traced using OpenTelemetry spans
Stored in a message store (e.g., Azure Service Bus, RabbitMQ)
Indexed by:
traceId
projectId
agentId
deliveryPhase
artifactType

⚙️ Orchestrator Behavior¶

The Orchestration Layer:

Subscribes to all domain events
Triggers agent sessions dynamically based on event metadata
Supports retries, human-in-the-loop, or alternative routing if confidence is low
Stores event → artifact → task lineage for replay/debugging

🧠 Use Cases Enabled¶

Scenario	Benefit
Restart from `ProductPlanCreated`	Re-runs Product Owner and downstream agents only
Audit trail for `Mobile Booking Flow`	View `featureId → epic → persona → story → handler` trace
Failure in `BacklogReady` event	Triggers retry or fallback to another PO Agent

✅ Summary¶

Every planning step emits traceable, observable, versioned events
Handoff is decoupled, enabling agent substitution, parallelism, and resilience
Orchestrator and Event Bus ensure pipeline continuity and visibility

🔍 Traceability & Observability Practices¶

The ConnectSoft AI Software Factory embeds end-to-end traceability and observability into every agent interaction, artifact, and event. This ensures all outputs from the Vision and Product Planning Agents are:

✅ Auditable
📊 Monitored in real time
🔁 Replayable
🧩 Linkable to downstream implementation

This section explains how trace IDs, structured logs, OpenTelemetry spans, and metadata are injected at every step in the planning flow.

🧠 Traceability Elements Injected¶

Field	Description
`traceId`	Unique identifier for the entire software creation flow (e.g., `proj-713-v1`).
`sessionId`	Unique per agent activation (e.g., `product-owner-session-1045`).
`componentTag`	Identifies the agent and sub-task (e.g., `ProductOwnerAgent:StorySlicing`).
`projectId`	Internal ConnectSoft project reference (e.g., `proj-713`).
`agentId`	Full agent identifier, useful for version tracking and fallback.
`artifactUris`	Secure links to outputs like `features.json`, `bpmn.mmd`, `user-stories.md`.
`emittedAt`	Timestamp of artifact and event creation.
`confidenceScore`	Indicates AI agent confidence for review (optional threshold for human-in-the-loop).

📋 Example Metadata Block (Injected in All Artifacts)¶

traceId: proj-713-v1
sessionId: product-owner-session-1045
projectId: proj-713
agentId: ProductOwnerAgent
componentTag: StoryMapping
confidenceScore: 0.93
artifactType: user-stories
generatedAt: 2025-05-12T16:11:00Z

📊 OpenTelemetry Tracing¶

Every agent execution emits spans such as:

Span Name	Example Description
`VisionDocumentParsed`	Agent loaded and parsed project context.
`FeatureDecompositionStarted`	Product Manager broke goals into features.
`StoryMappedToPersona`	Product Owner matched story to Pet Owner.
`RuleValidationInjected`	Business Analyst added GDPR rule to backlog.
`ArtifactStored`	Artifact pushed to blob or repo with metadata.
`EventEmitted: ProductPlanCreated`	Event sent to orchestrator/event bus.

These are visualized in tools like Grafana Tempo, Jaeger, or Azure Monitor.

📁 Artifact Versioning & Storage Strategy¶

Storage Type	Used For	Accessed By
Blob Storage	Markdown/YAML/JSON outputs	All downstream agents
Git Repositories	Story maps, planning documents (optional)	Developers, coordinators
Vector DB	Embedded semantic memory	AI agents and planners

All storage URIs are included in emitted events and indexed by the Orchestrator.

🧠 Cross-Agent Trace Example¶

traceId: proj-713-v1
  ├─ session: vision-session-001 → Vision Architect Agent
  ├─ session: product-manager-002 → Product Manager Agent
  ├─ session: business-analyst-001 → Business Analyst Agent
  ├─ session: product-owner-003 → Product Owner Agent

Each session:

Has a traceable execution record
Is observable in dashboards
Can be replayed or corrected if output fails validation

📣 Structured Logging Sample (JSON Log Output)¶

{
  "level": "INFO",
  "message": "Backlog generation complete",
  "traceId": "proj-713-v1",
  "sessionId": "product-owner-session-003",
  "agent": "ProductOwnerAgent",
  "artifact": "user-stories.md",
  "durationMs": 942,
  "confidence": 0.94
}

✅ Summary¶

Every planning agent is observable by default
Each output and event is linked by trace and session metadata
Platform supports semantic dashboards, event replays, and root cause tracing
Enables safe automation at scale, without losing human oversight

🧠 Semantic Kernel & Skill Patterns¶

All agents in the Vision and Product Planning cluster are powered by Semantic Kernel, enabling them to execute complex reasoning, planning, and document generation using modular, AI-driven skills.

This section outlines how these agents use skill composition, memory, and prompt templates to autonomously create structured outputs — with full traceability, retry support, and deterministic flows.

⚙️ Common Semantic Kernel Capabilities Used¶

Capability	Description
Skill Composition	Agents select and chain domain-specific skills dynamically (e.g., `ProblemExtractor → FeatureSuggestor → PersonaMapper`).
Prompt Engineering Templates	Each agent uses pre-curated system prompts and output schemas to guide reasoning.
Memory Injection	Agents retrieve prior visions, patterns, or rules from a semantic memory (vector DB) to enrich outputs.
Goal-Oriented Planning	Agents use goal definitions and semantic correlation to prioritize outcomes (e.g., MVP selection).
Validation Loops	If an output is below confidence threshold, the planner retries with enhanced reasoning.

🧠 Agent-Specific Skill Graphs (Examples)¶

Vision Architect Agent¶

📥 Inputs → ProblemExtractorSkill → OpportunityMappingSkill → PersonaIdentificationSkill → VisionAssemblySkill → 📤 Output: vision.md

Product Manager Agent¶

📥 Features + Goals → MVPPrioritizationSkill → FeatureClusteringSkill → RoadmapSlicerSkill → 📤 Output: product-plan.md, features.json

Product Owner Agent¶

📥 Epics + Features → StorySlicerSkill → CriteriaGeneratorSkill → TraceabilityTaggerSkill → 📤 Output: user-stories.md, criteria.yaml

Business Analyst Agent¶

📥 Stories + Vision → RuleFormalizationSkill → BPMNModelingSkill → GapDetectionSkill → 📤 Output: rules.yaml, bpmn.mmd

📋 Prompt Templates (per agent)¶

Agent	Example Prompt Role
Vision Architect	“You are a system analyst framing a SaaS platform vision...”
Product Manager	“You are a product strategist tasked with planning a 3-phase release roadmap...”
Product Owner	“You are a PO breaking epics into delivery-ready stories and Gherkin-style criteria...”
Business Analyst	“You are a business analyst extracting compliance rules and domain flows from product artifacts...”

Prompt structure includes:

System message
Input schema definitions
Output formatting instructions
Domain-specific constraint injections

🧠 Semantic Memory Use Cases¶

Agent	Semantic Use
Vision Architect	Retrieve prior visions from same domain or goal set
Product Manager	Recommend known MVP feature bundles
Product Owner	Match story formats from similar apps
Business Analyst	Retrieve known compliance patterns (e.g., HIPAA, GDPR)

🔁 Retry & Self-Healing Patterns¶

All agents support:

✋ Retry if validation fails or confidence < threshold
🔍 Correction skill invocation (e.g., GapFillerSkill, MissingPersonaIdentifier)
🔄 Event-based rerun via Orchestration (StartAgentSession with override parameters)

✅ Summary¶

All planning agents are Semantic Kernel-native
Each agent composes domain-specific skills dynamically
Prompt templates + skill orchestration → deterministic, enriched outputs
Semantic memory enables pattern reuse and context-aware generation

⬇️ Downstream Activation Summary¶

The outputs of the Vision and Product Planning agents serve as formal entrypoints for downstream agents, orchestrators, and coordinators across the AI Software Factory. Each planning artifact triggers concrete execution phases — from architecture to microservice generation, CI/CD, and QA automation.

This section summarizes which downstream agents are activated, what they consume, and how they map outputs to the delivery pipeline.

🧭 Downstream Activation Table¶

Event Emitted	Triggered Agents	Consumed Artifacts
`VisionDocumentCreated`	✅ Product Manager Agent ✅ Business Analyst Agent ✅ Enterprise Architect Agent	`vision.md`, `vision.json`
`BusinessRequirementsReady`	✅ Product Owner Agent ✅ QA Agents ✅ Enterprise Architect Agent	`rules.yaml`, `bpmn.mmd`, `requirements.md`
`ProductPlanCreated`	✅ Product Owner Agent ✅ Enterprise Architect Agent ✅ QA Planner Agent	`product-plan.md`, `features.json`, `epics.yaml`
`BacklogReady`	✅ Backend Dev Agent ✅ Frontend Dev Agent ✅ Adapter Generator Agent ✅ QA Test Generator Agent	`user-stories.md`, `criteria.yaml`, `story-map.json`

📥 Artifact-to-Agent Map¶

Artifact	Consumed By	Usage
`vision.json`	Enterprise Architect Agent	Strategic anchors for DDD modeling.
`features.json`	Enterprise & Solution Architect Agents	Map features to services and modules.
`epics.yaml`	Product Owner, Architecture Agents	Epics → contexts → aggregates.
`user-stories.md`	Backend Dev Agent, PR Coordinator	Used to generate handlers and DTOs.
`acceptance-criteria.yaml`	QA Agent	Build BDD tests and validation scripts.
`rules.yaml`	Validator Generator Agent, QA Agent	Runtime validation logic and test preconditions.
`bpmn.mmd`	Process Modeling Agent	Event flow visualizations and orchestrator config.

🧠 Orchestrator Coordination¶

Listens to each emitted planning event
Routes tasks to appropriate agents or coordinators
Supports conditional agent activation:
E.g., skip Business Analyst in “Rapid MVP Mode”
Tracks traceability between:
Story → Handler → Commit → PR
Goal → Feature → Story → Test

🔁 Execution Continuity¶

Planning agent outputs ensure:

✅ Microservice Assembly Coordinators receive use cases
✅ QA and Security Agents receive testable criteria
✅ Infrastructure Agents know feature-to-deployment mappings
✅ PR and CI/CD agents have traceable metadata for commits

🔗 Example Flow: BookingService¶

vision.json
  └─ features.json → MVP: "Mobile Booking Flow"
      └─ user-stories.md: "Book appointment without login"
          └─ DTOGeneratorAgent → DTO: BookAppointmentRequest.cs
          └─ HandlerGeneratorAgent → Handler: BookAppointmentHandler.cs
          └─ TestGeneratorAgent → BDD test: book-appointment.feature

✅ Summary¶

The Vision and Product Planning agents don’t just generate static documents — they emit structured control signals and execution blueprints that:

Drive agent routing
Determine architectural decomposition
Enable safe, scalable autonomous implementation

Their outputs act as living interfaces to the rest of the ConnectSoft platform.

✅ Conclusion and Future Expansion¶

The Vision and Product Planning cluster is the foundation of the ConnectSoft AI Software Factory — enabling structured, traceable, AI-driven planning across every autonomous software creation flow.

Through well-orchestrated agent collaboration, this cluster:

Translates ambiguous human prompts into executable software blueprints
Ensures all downstream services operate from validated, goal-aligned artifacts
Embeds observability, traceability, and semantic reasoning into every planning decision
Enables multi-agent handoffs without tight coupling, unlocking composability and reuse

🎯 What This Cluster Achieves¶

Capability	Outcome
Autonomous Visioning	Vision Architect creates structured goals, personas, and solutions from natural input
MVP Planning	Product Manager defines measurable outcomes and deliverable features
Backlog Readiness	Product Owner generates actionable, testable stories with acceptance criteria
Business Grounding	Business Analyst enforces compliance, process validity, and domain correctness
Traceable Execution	All outputs are logged, observable, and versioned for governance and recovery
Agent-Aware Handoff	Downstream agents seamlessly consume structured outputs through event activation

🔮 Future Enhancements¶

The current Vision and Product Planning agents are extensible by design. Future planned additions include:

Agent	Role
UX Research Agent	Gathers usability needs, personas, and heuristics from past product data
Growth Strategist Agent	Maps product features to market segments and opportunity scoring
Edition Planner Agent	Defines SaaS feature slicing per edition (e.g., Free, Pro, Enterprise)
Localization Analyst Agent	Plans multi-language requirements and region-specific constraints
Security Policy Planner Agent	Defines security profiles, roles, and attack surfaces from early requirements
Lifecycle Simulation Agent	Evaluates how features affect business operations over time (simulation-based planning)

These will plug into the same orchestration framework and use the same observable, event-driven lifecycle.

🧩 Integration with the Full Factory¶

Every service, API, test, and deployment artifact traces back to planning agents.
Planning outputs are first-class citizens — stored, embedded, reused, and reasoned upon by other agents.
This cluster turns "what do we build?" into "here’s exactly how it will be delivered."

📌 Final Thought¶

By defining software planning as a multi-agent, event-driven, observable pipeline, ConnectSoft unlocks a new level of:

Autonomous delivery
Strategic alignment
Product quality
Scale

Planning is no longer just a meeting room discussion — it is a codified, semantic, traceable part of the AI Software Factory.

🧭 Vision and Product Planning Agents Overview¶

📌 Introduction¶

🔍 Why It Matters¶

🎯 Objectives of This Document¶

🧱 Agent Cluster Composition¶

🧭 Position in the Execution Flow¶

🔄 Standard Event-Driven Activation Flow¶

🔀 Alternative Flow: Early Business Analysis Track¶

🔁 Concurrent Flow (Fork-Join Pattern)¶

🧠 Role of the Orchestration Layer¶

🧩 Example Configuration Switch (in blueprint.config.yaml)¶

✅ Summary¶

🧱 Agent Cluster Summary Table¶

📋 Overview Table¶

🧠 Key Properties Across the Cluster¶

🧩 Visual Snapshot¶

🔄 Agent Collaboration Flow¶

🧭 Standard Sequential Collaboration (Linear Cascade)¶

🔀 Alternative Flow: Business-First Track¶

🔁 Fork-Join Collaboration (Concurrent Model)¶

📌 Observability and Event Metadata¶

🧠 Vision Architect Agent – Role & Responsibilities¶

🎯 Core Responsibilities¶

🧩 Position in Lifecycle¶

✅ Key Outputs¶

💡 Example Use Case¶

📘 Summary¶

🧠 Vision Architect Agent – Execution Flow Diagram¶

🛠️ Step-by-Step Execution Lifecycle¶

🔍 Execution Phases Explained¶

📥 Inputs Required¶

📦 Outputs Produced¶

🧠 Key Execution Technologies¶

✅ Execution Properties¶

📥 Vision Architect Agent – Inputs & Outputs¶

📥 Inputs Consumed¶

🔄 Example Input Payload¶

📤 Outputs Produced¶

📘 Example Vision Document (Excerpt – vision.md)¶

🧠 Metadata Block (Excerpt – vision.metadata.json)¶

📣 Event: VisionDocumentCreated¶

✅ Summary¶

🎯 Product Manager Agent – Role & Responsibilities¶

🧭 Core Responsibilities¶

🧩 Position in the Factory Lifecycle¶

📘 Sample Product Plan Breakdown¶

📦 Core Output Artifacts¶

🧠 Alignment with ConnectSoft Principles¶

✅ Summary¶

📥 Product Manager Agent – Input Sources Diagram¶

📋 Primary Inputs¶

🧠 Input Parsing Logic¶

🏗️ Visual: Input Context Setup Flow¶

🧪 Input Example (vision.json excerpt)¶

📌 Semantic Enrichment (if available)¶

✅ Summary¶

📤 Product Manager Agent – Output Flows¶

📦 Core Artifacts Produced¶

📘 Example: product-plan.md¶

🧠 Example: features.json¶

📊 Feature Phasing Logic¶

📤 Event: ProductPlanCreated¶

🧩 Downstream Consumption¶

✅ Summary¶

📌 Product Owner Agent – Role & Responsibilities¶

🧭 Core Responsibilities¶

🧩 Example Breakdown¶

📘 Output Summary¶

📐 User Story Format Example (Markdown)¶

📤 Event: BacklogReady¶

📎 Traceability Chain¶

✅ Summary¶

🔁 Product Owner Agent – Inputs & Event Activation¶

📥 Inputs Consumed¶

⚙️ Event-Driven Activation¶

📘 Example Input Snapshot¶

features.json¶

epics.yaml¶

🧠 Execution Context Initialization¶

💡 Semantic Augmentation (Optional)¶

🧩 Example Configuration Switch (in `blueprint.config.yaml`)¶

📘 Example Vision Document (Excerpt – `vision.md`)¶

🧠 Metadata Block (Excerpt – `vision.metadata.json`)¶

📘 Example: `product-plan.md`¶

🧠 Example: `features.json`¶

📤 Event: `ProductPlanCreated`¶

`features.json`¶

`epics.yaml`¶

📝 Sample: `user-stories.md`¶

🧠 Sample: `story-map.json`¶

📜 Sample: `acceptance-criteria.yaml`¶

📤 Event: `BacklogReady`¶

📘 Example: `rules.yaml`¶

📘 Example: `bpmn.mmd`¶

🔁 Event: `BusinessRequirementsReady`¶