🧠 Product Owner Agent Specification¶

🎯 Purpose¶

The Product Owner Agent acts as the custodian of the Product Backlog inside the ConnectSoft AI Software Factory.

Its core purpose is to:

Transform structured Product Plans (produced by the Product Manager Agent【previously designed】)
into a granular, prioritized, and ready-for-development Backlog.
Decompose Features into detailed, actionable User Stories.
Define Acceptance Criteria and Refinement Notes for each backlog item —
ensuring downstream agents (Architects, UX/UI Designers, Backend/Frontend/Mobile Developers) have everything needed to execute autonomously.
Prioritize backlog items intelligently (impact, urgency, dependencies).
Ensure traceability from Vision ➔ Product Plan ➔ User Stories ➔ Developed Services.

🚀 In Short:¶

The Product Owner Agent bridges the gap between product vision and technical execution —
by creating a refined, prioritized, traceable Backlog that feeds all engineering flows inside ConnectSoft Factory.

🏛️ Position in the ConnectSoft AI Software Factory Lifecycle¶

flowchart TD
    VisionArchitectAgent -->|VisionDocument| ProductManagerAgent
    ProductManagerAgent -->|ProductPlan, MVP, Editions| ProductOwnerAgent
    ProductOwnerAgent -->|UserStories, BacklogItems| UXDesignerAgent
    ProductOwnerAgent -->|UserStories, BacklogItems| EnterpriseArchitectAgent
    ProductOwnerAgent -->|UserStories, BacklogItems| BackendDeveloperAgent
    ProductOwnerAgent -->|UserStories, BacklogItems| FrontendDeveloperAgent

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Upstream Input: Structured Product Plan artifacts (Feature Catalog, MVP Definition, Editions, Roadmap).
Downstream Outputs:
- Detailed Product Backlogs (Stories, Tasks, Acceptance Criteria).
- Events: BacklogReady, StoryCreated, AcceptanceCriteriaDefined.

📚 Key Goals of the Product Owner Agent¶

Goal	Description
Granular Decomposition	Break features into user-focused, independent, testable user stories (INVEST principle).
Acceptance Definition	Define what success means for each story (Acceptance Criteria).
Prioritized Workflows	Organize stories based on business value, urgency, and technical dependencies.
Traceable Lineage	Maintain direct traceability back to the originating Vision Document and Product Plan.
Ready for Development	Ensure user stories are detailed, scoped, and clear enough for downstream agents to pick up without clarification loops.

📜 Example Flows the Product Owner Agent Enables¶

Scenario	Supported?	Notes
Decomposing "Appointment Scheduling" into 8 user stories	✅	Patient selects date, Doctor confirms, Admin reschedules, etc.
Mapping stories to MVP scope	✅	Only critical flows included in MVP iteration.
Assigning Acceptance Criteria to each story	✅	"Appointment must sync to calendar within 30 seconds."
Prioritizing based on dependencies	✅	"User registration must precede appointment scheduling."
Preparing backlog across multiple editions (Free, Pro, Enterprise)	✅	Tagging backlog items accordingly.

🧩 ConnectSoft Platform Principles Alignment¶

Principle	Alignment
Domain-Driven Decomposition	Stories map cleanly to bounded contexts, aggregates, and entities.
Event-Driven Readiness	Story readiness events trigger UX/Architecture/Dev flows.
Observability and Traceability	Each story is linked to a Vision + Product Plan lineage.
Multi-Tenant SaaS Readiness	Backlog is edition-aware if applicable (different stories for Free vs Enterprise editions).
Resilience and Scalability	Supports large backlog creation across multi-product portfolios.

📋 Responsibilities¶

The Product Owner Agent is responsible for turning high-level Product Plans into fine-grained, executable artifacts —
guaranteeing smooth and autonomous execution across ConnectSoft engineering flows.

Each responsibility ensures that the Backlog is:

Clear (user-focused language)
Structured (INVEST-aligned stories)
Traceable (linked to Vision and Product Plan)
Prioritized (based on business value and technical feasibility)
Ready for Engineering (no clarification cycles needed)

🛠️ Primary Responsibilities¶

Responsibility	Description
Feature Decomposition	Break each feature from Product Plan into independent, small, testable User Stories (INVEST principle: Independent, Negotiable, Valuable, Estimable, Small, Testable).
User Story Authoring	Write each User Story in user-centric, clear language: "As a [persona], I want [goal], so that [benefit]".
Acceptance Criteria Definition	Define 2–5 acceptance criteria per User Story — clear, testable success conditions.
Backlog Structuring	Organize User Stories into a structured Product Backlog, scoped by MVP/Phase/Roadmap alignment.
Backlog Prioritization	Assign priority scores using MoSCoW (Must have, Should have, Could have, Won't have) or RICE models.
Traceability Embedding	Every User Story must include traceability back to the original Vision Document, Product Plan feature, and strategic goal.
Edition Awareness	Identify if specific backlog items apply to Free, Pro, or Enterprise editions.
Backlog Readiness Validation	Validate the completeness, clarity, and testability of all User Stories before marking backlog "ready".
Artifact Storage	Store the generated Backlog in artifact storage (Blob, Database, Git, Azure DevOps, or other) — backend pluggable【your recent clarification】.
Event Emission	Emit structured events: `BacklogReady`, `StoryCreated`, `AcceptanceCriteriaDefined`.

🧩 Responsibilities to Artifact Mapping¶

Artifact	Responsibility
User Story Document	Feature Decomposition, Story Authoring, Traceability
Acceptance Criteria Document	Acceptance Criteria Definition
Prioritized Backlog Artifact	Backlog Structuring and Prioritization
Traceability Map	Embedding Vision ➔ Product Plan ➔ Story lineage
Edition Mapping Table (Optional)	Edition Awareness tagging

🏛️ Visual: Responsibilities Flow¶

flowchart TD
    ProductPlanIntake --> FeatureDecomposition
    FeatureDecomposition --> UserStoryAuthoring
    UserStoryAuthoring --> AcceptanceCriteriaDefinition
    AcceptanceCriteriaDefinition --> BacklogStructuring
    BacklogStructuring --> Prioritization
    Prioritization --> ValidationAndCorrection
    ValidationAndCorrection --> StorageAndEmission

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📚 Example Responsibilities in Action¶

Responsibility	Example Output
Feature Decomposition	"Appointment Scheduling" becomes 8 independent User Stories.
User Story Authoring	"As a Patient, I want to select an appointment date, so that I can schedule my visit conveniently."
Acceptance Criteria	- "Patient must select available date slots." - "Doctor must confirm within 24 hours."
Prioritization	MVP stories marked "Must Have", reporting features marked "Could Have".
Edition Awareness	"Multi-clinic Management" story tagged as Enterprise-only.

📈 Critical Success Metrics (Agent Internal KPIs)¶

KPI	Target
Story Completeness Rate	100% of MVP features decomposed into INVEST-compliant stories.
Traceability Coverage	100% of stories linked to Vision and Product Plan.
Backlog Readiness Time	Produce full MVP backlog within agent time budget (configurable).
Validation Pass Rate	> 95% stories pass structural and semantic validation on first attempt.
Event Emission Success Rate	100% backlog-related events emitted without error.

🧩 Alignment to ConnectSoft Platform Principles¶

Principle	Responsibility Reflection
Domain-Driven Modeling	Stories map to bounded contexts and aggregates.
Cloud-Native Artifact Management	Backlog artifacts pluggable to different storage backends【your clarified requirement】.
Event-Driven Architecture	Readiness notifications and story publications emitted via events.
Observability and Traceability	Story lineage fully observable and linkable.
SaaS Editions Support	Backlogs can vary by product edition (Free/Pro/Enterprise).

📥 Inputs¶

The Product Owner Agent requires a structured, event-driven, and artifact-backed set of inputs
to accurately decompose product features into actionable user stories.

Each input ensures that story authoring, prioritization, and acceptance criteria definition
remain aligned with the original business vision, strategic goals, and feature plans.

📋 List of Primary Inputs¶

Input Type	Description	Example
Product Plan Document	Produced by Product Manager Agent; includes Feature Catalog, MVP definition, Editions mapping, and Roadmap【previous spec】.	Markdown/JSON artifact link.
ProductPlanCreated Event	Event notifying that Product Plan is available, with traceability metadata.	`event_type: ProductPlanCreated`, `artifact_uri`, `trace_id`.
Feature Catalog Artifact	Structured list of all features, with mappings to personas and strategic goals.	JSON array of features.
MVP Feature List	List of features that must be included in the initial product release (MVP).	JSON object with feature names.
Editions Mapping Artifact	Mapping of features to SaaS editions (Free, Pro, Enterprise).	JSON object linking features to editions.
Vision Document (Reference Only)	Original Vision Document produced by Vision Architect Agent, used for traceability and context.	URI to stored artifact.
Semantic Memory (Optional)	Previously created Backlogs or Story Decompositions from similar domains retrieved via semantic search (vector DBs or MCP Servers【your clarified model – backend flexible】).	Similar Healthcare SaaS MVP backlog from past project.
Strategic Objectives List	Business goals extracted from Vision and Product Plan — used to align and prioritize stories.	"Reduce churn", "Improve engagement", "Launch by Q2 2026".
Edition Constraints (Optional)	Specific instructions if features or user stories must vary by edition.	"Multi-clinic management only available in Enterprise Edition."
Urgency/Delivery Constraints (Optional)	Deadlines, phases from Roadmap (MVP, Phase 2, Phase 3 targets).	"MVP features must be ready within 4 months."

🏗️ Visual: Input Sources¶

flowchart TD
    ProductManagerAgent -->|ProductPlanCreatedEvent| EventBus
    EventBus --> ProductOwnerAgent
    ArtifactStorage -->|Product Plan, Feature Catalog| ProductOwnerAgent
    SemanticMemory -->|Retrieve Past Story Patterns| ProductOwnerAgent
    VisionStorage -->|Vision Document (Reference)| ProductOwnerAgent

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✅ Inputs are multisource, but traceable and cloud-native.
✅ Artifact storage flexible — could be Blob Storage, Git, Azure DevOps, Semantic DBs, MCP Servers【as per your instruction】.

📚 Example Event Payload for Activation¶

{
  "event_type": "ProductPlanCreated",
  "trace_id": "vision-2025-04-27-001",
  "artifact_uri": "https://storage.connectsoft.ai/products/product-plan-2025-04-27-001.md",
  "version": "1.0",
  "timestamp": "2025-04-27T23:00:00Z"
}

✅ This activates the Product Owner Agent with full metadata context.

📖 Example Extracted Input from Product Plan¶

Field	Example
Feature	"Appointment Scheduling"
Personas	"Patient", "Doctor", "Admin"
Strategic Goals	"Improve appointment engagement by 25%"
MVP Inclusion	"Must be delivered in MVP"
Edition Target	"Available for Free and Pro editions"

🧩 Input-to-Output Mapping¶

Input	Drives Output
Feature Catalog	User Story Decomposition
MVP Feature List	MVP-Tagged Stories
Editions Mapping	Edition-tagged Backlog Items
Strategic Objectives	Business-aligned Story Prioritization
Roadmap Phases	Sprint Planning Readiness
Semantic Memory	Enrichment of story patterns, acceptance criteria samples

🧠 Smart Behaviors Based on Inputs¶

Behavior	Input Trigger
Semantic Enrichment	Similar past projects found in semantic memory.
MVP Filtering	Use MVP Feature List to focus only on core flows.
Edition-Specific Story Branching	If editions mapping suggests differentiated feature access.
Story Prioritization Adjustment	Strategic objective flags: higher priority for launch-critical goals.

🧩 ConnectSoft Platform Principles Alignment¶

Principle	Input Behavior Alignment
Event-Driven Activation	Listens for ProductPlanCreated events.
Multi-Backend Artifact Flexibility	Artifacts retrieved from storage layers (Blob, Git, SQL, Semantic DBs)【your clarified model】.
Semantic Memory Augmentation	Similar plans enhance decomposition and story authoring.
Traceability and Observability	Inputs embedded with full lineage tracking (trace_id, project_id).

📤 Outputs¶

The Product Owner Agent is responsible for producing clear, traceable, and engineering-ready outputs,
enabling autonomous execution by downstream agents like UX Designers, Solution Architects, Backend and Frontend Developers.

Every output is modular, semantically linked, traceable to the originating Vision,
and validated for completeness, testability, and prioritization readiness.

📋 Main Outputs Produced¶

Output Type	Description	Format
Product Backlog Artifact	Full structured backlog containing User Stories, Priorities, Acceptance Criteria, Traceability.	Markdown Table + JSON Structured Artifact
Individual User Story Artifacts	For each story: description, acceptance criteria, traceability metadata, edition tags (if applicable).	Markdown + JSON per story
Acceptance Criteria Document	Aggregated acceptance conditions for all MVP and Phase 1 stories.	Markdown and/or JSON structure
Backlog Prioritization Matrix	Table or JSON indicating MoSCoW or RICE scoring for backlog items.	JSON Array
Traceability Map	Matrix linking: Vision ➔ Product Plan Feature ➔ User Story ➔ Edition/Persona.	Markdown Table + JSON
Edition Mapping Tags (Optional)	If different editions (Free, Pro, Enterprise) require story tagging.	JSON field inside stories
Event Emissions	Structured events emitted to Event Bus notifying system of backlog readiness and story creation.	JSON Event Payloads

🏗️ Visual: Output Artifact Generation Flow¶

flowchart TD
    ProductPlanIntake --> DecomposeFeatures
    DecomposeFeatures --> UserStories
    UserStories --> AcceptanceCriteria
    AcceptanceCriteria --> PrioritizationScoring
    PrioritizationScoring --> BacklogAssembly
    BacklogAssembly --> ArtifactStorage
    BacklogAssembly --> EventEmission

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✅ Outputs are progressively enriched and self-validated before being finalized.

📚 Example: Product Backlog Artifact (Markdown Structure)¶

# 📋 Product Backlog - Appointment Management SaaS

## Traceability Metadata
- Project ID: healthcare-saas-2025
- Trace ID: vision-2025-04-27-001
- Product Plan Version: 1.0

## Backlog Items

| Story ID | Story | Priority | MVP | Edition | Acceptance Criteria |
|:---------|:------|:---------|:----|:--------|:--------------------|
| US-001 | As a Patient, I want to select an available appointment date, so that I can schedule easily. | Must Have | ✅ | Free, Pro | - Select available slot <br> - Confirm appointment <br> - Receive confirmation |
| US-002 | As an Admin, I want to reschedule appointments, so that no-shows can be minimized. | Should Have | ❌ | Pro, Enterprise | - Update appointment <br> - Notify Patient |

✅ MVP indicator
✅ Edition mapping
✅ Acceptance Criteria embedded

📦 Example: Single User Story JSON Artifact¶

{
  "story_id": "US-001",
  "description": "As a Patient, I want to select an available appointment date, so that I can schedule easily.",
  "trace_id": "vision-2025-04-27-001",
  "linked_feature": "Appointment Scheduling",
  "personas": ["Patient"],
  "strategic_goals": ["Improve appointment engagement"],
  "priority": "Must Have",
  "mvp_scope": true,
  "edition_tags": ["Free", "Pro"],
  "acceptance_criteria": [
    "Select available appointment slot",
    "Confirm appointment",
    "Receive notification of confirmation"
  ]
}

✅ Downstream-ready
✅ Traceable
✅ Business-aligned

📣 Example: Event Emissions¶

Event	Payload Contents
`BacklogReady`	Artifact URI, project ID, trace ID, backlog version.
`StoryCreated`	Story ID, traceability metadata, artifact URI.
`AcceptanceCriteriaDefined`	Story ID, acceptance criteria JSON.

Example: BacklogReady Event

{
  "event_type": "BacklogReady",
  "trace_id": "vision-2025-04-27-001",
  "artifact_uri": "https://storage.connectsoft.ai/backlogs/backlog-2025-04-27-001.md",
  "version": "1.0",
  "timestamp": "2025-04-28T01:00:00Z"
}

✅ Triggers downstream UX, Architect, Engineering Agents to pick up stories autonomously.

🧩 Output Quality Standards¶

Attribute	Validation Rule
Traceability	Every artifact must include `trace_id`, `linked_feature`, `personas`, `strategic_goals`.
Prioritization	Every backlog item must have MoSCoW or RICE score.
Testability	Every User Story must have 2–5 Acceptance Criteria.
Readiness	MVP-scope stories must be explicitly marked.
Edition Awareness	If editions mapping exists, stories must be tagged accordingly.

🧩 ConnectSoft Platform Principles Alignment¶

Principle	Output Reflection
Clean Modularization	Every story/artifact is modular and reusable.
Event-Driven Collaboration	Events drive downstream agent activations.
Observability	Events, storage actions, validation results are traced and logged.
Cloud-Native Scalability	Artifacts stored in Blob Storage, Git, SQL, or semantic stores【your flexibility requirement】.
Multi-Edition Support	Edition mappings handled naturally for SaaS scaling.

📚 Knowledge Base¶

The Product Owner Agent is empowered by a rich, modular internal knowledge base
that allows it to:

Write clear, user-centered User Stories
Define rigorous, testable Acceptance Criteria
Prioritize backlog items intelligently
Align outputs to ConnectSoft SaaS standards
Maintain semantic continuity and traceability

This ensures the agent operates predictably, autonomously, and scalably across the factory lifecycle.

📋 Core Areas of the Knowledge Base¶

Knowledge Area	Description
User Story Templates	Standardized formats for writing clear, INVEST-aligned User Stories (Independent, Negotiable, Valuable, Estimable, Small, Testable).
Acceptance Criteria Frameworks	Techniques for defining 2–5 clear, binary acceptance conditions per story.
MoSCoW Prioritization Model	Framework for Must Have, Should Have, Could Have, Won't Have story scoring.
RICE Prioritization Model	Reach × Impact × Confidence ÷ Effort scoring method for feature/stories.
Traceability Embedding Rules	How to embed project ID, vision trace ID, product plan version into every story artifact.
Edition-Awareness Patterns	Best practices for tagging backlog items to Free, Pro, Enterprise editions.
Persona-Based Story Decomposition	Models for mapping stories tightly to identified personas.
Strategic Goal Alignment Techniques	Ensures every story supports at least one strategic objective (extracted from Vision).
Story Granularity Guidelines	How to split stories to fit within 1–3 day delivery window (ready for downstream agile teams).
Semantic Memory Access	Retrieval of prior backlog artifacts for similar domains/products (via Semantic DBs, Git, SQL, or MCP Servers if needed)【your clarified multi-backend support】.

🏗️ Knowledge Base Diagram¶

flowchart TD
    KnowledgeBase --> StoryTemplates
    KnowledgeBase --> AcceptanceCriteriaModels
    KnowledgeBase --> PrioritizationFrameworks
    KnowledgeBase --> TraceabilityStandards
    KnowledgeBase --> EditionTaggingPatterns
    KnowledgeBase --> StrategicGoalAlignmentModels
    KnowledgeBase --> SemanticMemoryRetriever

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✅ Ensures modular reasoning and best-practice adherence across all outputs.

📚 Example Knowledge Base Assets¶

Asset	Example
`standard-user-story-template.md`	"As a [persona], I want [goal], so that [benefit]"
`acceptance-criteria-cheatsheet.md`	Clear, binary, testable — avoid subjective language.
`moscow-prioritization-rules.json`	Definitions for Must/Should/Could/Won't tags.
`rice-scoring-weights.json`	Default weightings for Reach, Impact, Confidence, Effort.
`edition-tagging-guidelines.md`	How to annotate backlog items by SaaS edition.
`persona-decomposition-mapping.json`	Expected behavior mappings for common personas (Patient, Doctor, Admin, Customer, etc.).
`semantic-memory-query-patterns.md`	How to query "past backlogs similar to Healthcare SaaS MVP" from memory stores.

🧠 Semantic Memory (Optional Boost)¶

If backlog creation complexity increases (e.g., 100+ stories or cross-domain SaaS),
the Product Owner Agent optionally enriches itself by querying:

Previous ConnectSoft SaaS Product Backlogs
Related User Story patterns
Acceptance Criteria examples
Edition-aware decompositions

✅ Stored in Semantic Memory across MCP Servers, Azure Cognitive Search, SQL, or Blob/Git as per backend configuration【your clarified model】.

📋 Example: Enrichment Behavior¶

Scenario	Semantic Memory Role
Healthcare SaaS Product	Retrieve similar "Patient Appointment Booking" stories and criteria.
E-Commerce Loyalty App	Retrieve similar "Reward Points Management" user stories.
API-first Backend	Retrieve "API Authentication", "Rate Limiting" backlog examples.

🧩 ConnectSoft Platform Principles Alignment¶

Principle	Knowledge Base Alignment
Domain Expertise	User stories match domain needs, personas, and strategic goals.
Resilient Execution	Prioritization and story decomposition standards enforced.
Traceability-First	Every artifact linkable via IDs, project lineage.
Cloud-Native and Pluggable	Semantic memories fetched flexibly from available backends (Blob, Git, SQL, MCP, etc.).
Edition-Based SaaS Readiness	Story tagging for Free, Pro, Enterprise editions.

🔄 Process Flow¶

The Product Owner Agent follows a structured, modular, and observable execution pipeline
to transform Product Plans into fully structured Backlogs.

This process ensures:

High-quality decomposition
Prioritized, acceptance-ready stories
Full traceability and semantic alignment
Observability and recoverability at every step

📋 High-Level Phases of the Process Flow¶

Phase	Description
1. Task Assignment	Agent activated via `ProductPlanCreated` event. Receives Product Plan URI, Trace ID, metadata.
2. Information Intake	Downloads Product Plan, Feature Catalog, MVP Features, Editions Mapping; fetches semantic memory (optional).
3. Decomposition	Breaks features into detailed User Stories (applying INVEST principle).
4. Acceptance Criteria Definition	Drafts 2–5 acceptance criteria per story — clear, testable conditions.
5. Prioritization	Applies MoSCoW or RICE scoring to backlog items based on business objectives and constraints.
6. Edition Tagging	If editions mapping exists, tag stories accordingly (Free, Pro, Enterprise).
7. Validation	Structural, semantic, traceability validation of all backlog items.
8. Correction (if needed)	Auto-correct missing sections, retrigger validation.
9. Artifact Storage	Store backlog and stories in flexible backends (Blob Storage, Git Repos, SQL DBs, etc.)【multi-backend support as clarified】.
10. Event Emission	Emit structured events: `BacklogReady`, `StoryCreated`, `AcceptanceCriteriaDefined`.
11. Observability Logging	Emit traces, structured logs, and Prometheus-compatible metrics for each critical step.

🏗️ Visual: Product Owner Agent Process Flow Diagram¶

flowchart TD
    TaskAssignment["Task Assignment (ProductPlanCreated Event)"]
    --> InformationIntake["Download Artifacts + Semantic Enrichment"]
    --> Decomposition["Decompose Features into User Stories"]
    --> AcceptanceCriteria["Define Acceptance Criteria"]
    --> Prioritization["Apply MoSCoW / RICE Prioritization"]
    --> EditionTagging["Tag Stories per SaaS Editions (Optional)"]
    --> Validation["Validate Artifacts"]
    Validation -->|Pass| Storage["Store Artifacts"]
    Validation -->|Fail| Correction["Auto-Correct and Retry"]
    Correction --> RetryValidation["Re-Validate After Correction"]
    Storage --> EventEmission["Emit Events to EventBus"]
    EventEmission --> Observability["Emit Logs, Metrics, Traces"]

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✅ Supports corrections, retries, and full observability across steps.

📜 Detailed Activities Inside Each Phase¶

📥 1. Task Assignment¶

Listen for ProductPlanCreated event.
Extract artifact URIs, trace_id, version metadata.

📚 2. Information Intake¶

Download Product Plan (Markdown + JSON).
Parse Feature Catalog, MVP List, Editions Mapping.
Retrieve relevant semantic memory examples (optional).

🧩 3. Decomposition¶

Decompose each feature into multiple INVEST-compliant User Stories.

🧠 4. Acceptance Criteria Definition¶

Attach 2–5 binary, testable conditions to each User Story.

🔢 5. Prioritization¶

Apply MoSCoW (Must, Should, Could, Won’t) tags or RICE scores.

🏷️ 6. Edition Tagging¶

Tag stories if different per SaaS edition.

✅ 7. Validation¶

Ensure completeness, business alignment, persona linking, strategic goal alignment, traceability metadata.

🔁 8. Correction (if needed)¶

Auto-fix missing priorities, missing trace_ids, missing acceptance criteria.

💾 9. Artifact Storage¶

Save backlogs and individual stories in configured storage backends.

📣 10. Event Emission¶

Emit BacklogReady, StoryCreated, and AcceptanceCriteriaDefined events with traceable payloads.

📈 11. Observability Logging¶

Emit OpenTelemetry traces, Prometheus metrics, and Serilog structured logs.

📈 Example Traceability Maintained¶

Artifact	Linked Metadata
User Story	Story ID, Linked Feature ID, Trace ID, Project ID, MVP Flag, Edition Tags
Acceptance Criteria	Story ID, Acceptance Condition ID, Trace ID
Events	Artifact URI, Version, Trace ID, Timestamp

✅ Maintains full lineage across the Factory.

🧩 ConnectSoft Platform Principles Alignment¶

Principle	Process Step Alignment
Event-Driven Activation	Listens for upstream ProductPlanCreated events.
Cloud-Native and Pluggable Storage	Flexible backend artifact storage models【your clarified model】.
Resilience and Recoverability	Validation + Auto-Correction before escalation.
Observability and Traceability	Full lifecycle instrumentation (logs, traces, metrics).
Multi-Edition SaaS Readiness	Edition-aware decomposition and tagging.

🛠️ Technologies¶

The Product Owner Agent is designed with a modular, cloud-native, event-driven, and AI-augmented stack,
allowing it to autonomously process, structure, validate, and emit product backlog artifacts at industrial scale.

Key Architectural Goals:

Semantic reasoning orchestration
Multi-backend artifact storage flexibility (not MCP-only — per your clarification!)
Event-driven lifecycle
Full observability instrumentation
Scalable, resilient, cloud-ready by default

📋 Core Technology Stack¶

Technology	Purpose	Example Usage
Semantic Kernel (.NET)	Modular skill orchestration, function chaining, reasoning execution.	Decomposes features into stories, generates acceptance criteria, validates outputs.
OpenAI Models (Azure OpenAI or OpenAI API)	LLM-based reasoning and natural language generation.	Generates user stories, drafts acceptance criteria, enhances prioritization reasoning.
Flexible Artifact Storage (Blob Storage, Git Repositories, SQL DBs, Documentation Systems, etc.)	Store generated Backlogs, Stories, Traceability Maps.	Azure Blob, PostgreSQL DB, GitHub Repo, Azure DevOps Wiki, depending on project config.
Azure Event Grid / Kafka / RabbitMQ	Event-driven orchestration: listens for ProductPlanCreated, emits BacklogReady, StoryCreated, AcceptanceCriteriaDefined.	Event Bus integration for modular scaling.
OpenTelemetry + Serilog	Observability for traces, structured JSON logs, and metrics.	Track every skill execution, artifact validation, event emission.
Prometheus or Azure Monitor	Metric collection, backlog agent health monitoring.	Exposes story creation rates, validation failures, event emission successes.
Semantic Memory Retrieval (Optional, Configurable)	Retrieve relevant past backlogs, stories, acceptance criteria from vector DBs or other semantic stores.	Azure Cognitive Search, Pinecone, Redis Vector, or custom vector-enabled storage.

🏗️ Technologies Component Diagram¶

flowchart TD
    EventBus -->|Event: ProductPlanCreated| TaskIntakeController
    TaskIntakeController --> SemanticKernelOrchestrator
    SemanticKernelOrchestrator -->|Invoke| DecompositionSkills
    SemanticKernelOrchestrator -->|Invoke| ValidationSkills
    SemanticKernelOrchestrator -->|Invoke| CorrectionSkills
    DecompositionSkills --> ArtifactStorageManager
    ValidationSkills --> ArtifactStorageManager
    CorrectionSkills --> ArtifactStorageManager
    ArtifactStorageManager -->|Save Artifacts| StorageBackend[(Blob, Git, SQL, Doc Systems)]
    ArtifactStorageManager --> EventEmitter
    EventEmitter --> EventBus
    AllModules -.-> ObservabilityStack(OpenTelemetry + Serilog + Prometheus)

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✅ Full event-driven, modular, observable pipeline.

📦 Artifact Storage Strategy¶

Storage Option	Supported?	Notes
Azure Blob Storage	✅	Default for blob artifacts.
Git Repository (Azure DevOps, GitHub)	✅	For markdown story documents and backlog repositories.
PostgreSQL DB	✅	For structured JSON backlog storage.
Azure DevOps Wiki	✅	For backlog publication directly into documentation systems.
MCP Servers (optional)	✅	If cross-platform semantic access needed — optional, not mandatory.

✅ Storage backend is pluggable and configurable per project — no lock-in to MCP only!

📚 Example Integration Behaviors¶

Integration	Example
Semantic Kernel Skills	Feature Decomposition, Story Creation, Acceptance Criteria Definition, Prioritization.
OpenAI Completion Models	Drafting high-quality user-centered stories and testable acceptance criteria.
Event Bus Publishing	`BacklogReady`, `StoryCreated`, `AcceptanceCriteriaDefined`.
Artifact Upload	Backlog artifact to Azure Blob ➔ Story artifacts to Git Repo ➔ Traceability map to SQL DB.
Telemetry Emission	Every major execution phase logged and traced (OpenTelemetry spans, Serilog logs).
Semantic Memory Search	Retrieve previous similar healthcare SaaS backlog examples for enrichment.

🧩 ConnectSoft Platform Principles Alignment¶

Principle	Technologies Supporting It
Cloud-Native Scalability	Azure Blob, Event Grid, OpenTelemetry, Git, SQL, Semantic Stores.
Event-Driven Architecture	Azure Event Grid, Kafka, RabbitMQ for modular triggering.
Observability First	Full OpenTelemetry and Prometheus metrics for internal and external monitoring.
Semantic Continuity	Optional semantic enrichment via flexible memory backends.
Multi-Edition SaaS Support	Tags, fields, and traceability for Free, Pro, Enterprise versions.

📝 System Prompt (Initialization Instruction)¶

When the Product Owner Agent is bootstrapped,
it receives a structured system prompt defining:

Its role and domain boundaries
Expectations for modular outputs
Quality standards it must enforce
ConnectSoft-specific principles it must follow (traceability, SaaS-edition awareness, business alignment, etc.)

This prompt is critical to guarantee predictable, scalable, resilient autonomous behavior inside the AI Software Factory.

📋 Full System Prompt Text¶

🧠 You are a Product Owner Agent inside the ConnectSoft AI Software Factory.

Your mission is to transform a structured Product Plan into a ready-for-development Product Backlog, composed of clear, traceable, prioritized User Stories and testable Acceptance Criteria.

You must: - Decompose each feature into multiple INVEST-compliant User Stories. - Write User Stories following:
"As a [persona], I want [goal], so that [business benefit]." - Define 2–5 clear, binary Acceptance Criteria per story. - Tag stories by MVP Phase, Edition Mapping (Free/Pro/Enterprise) if applicable. - Apply MoSCoW or RICE prioritization to backlog items. - Embed traceability metadata (trace_id, linked_feature, personas, strategic_goals) in every output. - Structure outputs in Markdown and compact JSON formats.

📋 Rules and Expectations: - Every User Story must link back to its parent feature and strategic business objective. - Every artifact must be fully self-validating and testable. - Outputs must be ready for immediate downstream consumption by UX, Architecture, Backend, Frontend, and Mobile Developer Agents. - Observability is mandatory: emit logs, metrics, and traces during your execution. - Operate assuming a cloud-native, SaaS-first environment (Free/Pro/Enterprise models by default). - Flexible Artifact Storage: use configured Blob Storage, Git, SQL, Doc Systems, or MCP Servers if available.

🚀 Operational Philosophy: - Maximize business alignment at every decision point. - Minimize ambiguity. - Always optimize for modularity, traceability, and continuous flow across the Factory.

🧠 Purpose of System Prompt¶

Objective	Why It's Critical
Define Clear Role Boundaries	Ensures agent doesn't drift into UX design, architecture, or coding.
Enforce ConnectSoft Principles	Clean Modularization, Traceability, Cloud-Native, SaaS awareness.
Guarantee Observability	Logging, tracing, and metric exposure embedded by default.
Ensure Downstream Readiness	Outputs are directly usable without human clarifications.
Support Multi-Edition SaaS	Artifacts tagged accordingly if feature access varies per tier.
Flexible Backend Awareness	Not tied to MCP only — supports Blob, Git, SQL, Document Stores【your flexibility note】.

📚 Key Behavioral Directives Embedded¶

Directive	Impact
INVEST Story Standards	Independent, small, testable user stories created.
Traceability Enforcement	Stories mapped to Vision ➔ Product Plan ➔ Feature ➔ Persona ➔ Strategic Goal.
Business Alignment	All decomposition decisions tie back to business KPIs.
Prioritization Application	MVP-focused, goal-oriented prioritization of backlog items.
Edition Mapping Sensitivity	SaaS Edition tagging applied if needed.
Self-Validation First	No unvalidated artifacts allowed into downstream flows.
Event Emission Readiness	Emitting BacklogReady, StoryCreated, AcceptanceCriteriaDefined events properly.

🏗️ Visual: System Prompt Behavioral Model¶

flowchart TD
    ProductPlanInput --> FeatureDecomposition
    FeatureDecomposition --> UserStoryWriting
    UserStoryWriting --> AcceptanceCriteriaDrafting
    AcceptanceCriteriaDrafting --> PrioritizationScoring
    PrioritizationScoring --> TraceabilityEnrichment
    TraceabilityEnrichment --> Validation
    Validation --> EventEmission

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

✅ All behaviors flow systematically from the System Prompt instructions.

🧩 ConnectSoft Platform Principles Alignment¶

Principle	Embedded In System Prompt
Event-Driven Activation and Handoff	Mandatory event emissions.
Modular Outputs	Stories and artifacts structured independently and clearly.
Observability-First Execution	OpenTelemetry, Metrics, Structured Logging.
Semantic Continuity	Stories maintain semantic links to business goals, features, personas.
Cloud-Native Pluggable Storage	Configurable backend support (Blob, Git, SQL, Docs, MCP)【your flexibility model】.

📥 Input Prompt Template¶

Upon receiving an assigned task (usually triggered by a ProductPlanCreated event),
the Product Owner Agent builds a structured internal input prompt that:

Injects all critical context
Establishes task goals clearly
Prepares the agent’s semantic reasoning path
Guarantees consistent, modular, and traceable story decomposition

This template ensures that every decomposition is business-driven, persona-centered, and cloud-native SaaS-ready.

📋 Standard Input Prompt Template¶

## Input Information

Vision Summary:
{vision_summary}

Strategic Objectives:
{strategic_objectives_list}

Personas Identified:
{persona_list}

Feature Catalog:
{feature_catalog_list}

MVP Features:
{mvp_feature_list}

Edition Mapping (Optional):
{edition_mapping}

Project Metadata:
- Project ID: {project_id}
- Vision Trace ID: {vision_trace_id}
- Product Plan Version: {product_plan_version}
- MVP Deadline: {mvp_deadline} (Optional)

---

## Task for Product Owner Agent

Using the provided context:

1. **Decompose** each feature into one or more **INVEST-compliant User Stories**.
2. **Write User Stories** using the format:  
   "_As a [persona], I want [goal], so that [business benefit]._"
3. **Define Acceptance Criteria** (2–5) for each User Story — must be binary and testable.
4. **Prioritize** each User Story using MoSCoW (Must, Should, Could, Won't) or RICE model.
5. **Tag** stories appropriately if they belong to MVP Scope.
6. **Tag** stories by Edition (Free, Pro, Enterprise) if editions mapping is applicable.
7. **Embed Traceability Metadata**:
   - Linked Feature ID
   - Linked Strategic Goal(s)
   - Trace ID
   - Persona(s)
8. **Structure Outputs** in:
   - Markdown format (primary artifact)
   - JSON format (secondary artifact)
9. **Emit** the following events:
   - `BacklogReady`
   - `StoryCreated` (for each story)
   - `AcceptanceCriteriaDefined` (for each story)

---

## Style Guide

- Clear, modular sections.
- Business benefit clearly articulated in User Stories.
- Acceptance Criteria must be measurable and binary (no vague language).
- Outputs must be directly consumable by downstream agents.
- Maintain cloud-native SaaS orientation by default.
- Maintain observability-first execution: log key actions, emit traces and metrics.

🧠 Example Runtime Parameterized Values¶

Placeholder	Example
`{vision_summary}`	"A SaaS platform to manage healthcare appointment scheduling."
`{strategic_objectives_list}`	"Reduce no-shows by 25%; Launch MVP in 4 months."
`{persona_list}`	"Patient", "Doctor", "Admin Staff"
`{feature_catalog_list}`	"Appointment Scheduling", "Billing Management", "Notifications System"
`{mvp_feature_list}`	"Appointment Scheduling", "Basic Notifications"
`{edition_mapping}`	"Free: Basic Scheduling; Pro: Advanced Reminders; Enterprise: Analytics Dashboard"
`{project_id}`	"healthcare-saas-2025"
`{vision_trace_id}`	"vision-2025-04-27-001"
`{product_plan_version}`	"1.0"
`{mvp_deadline}`	"2025-08-31"

🏗️ Visual: Input Prompt Construction Flow¶

flowchart TD
    ProductPlanIntake --> VisionParsing
    VisionParsing --> ContextAssembly
    FeatureCatalogParsing --> ContextAssembly
    MVPParsing --> ContextAssembly
    EditionsMappingParsing --> ContextAssembly
    ContextAssembly --> StructuredInputPrompt

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

✅ All inputs come together into a standardized structured internal prompt.

📚 Input Prompt Directives Summary¶

Directive	Purpose
Story Decomposition	Ensure each feature becomes multiple detailed user stories.
MVP Awareness	Stories that belong to MVP are marked.
Prioritization Enforcement	Prioritize backlog items consistently (MoSCoW or RICE).
Traceability	Every story linked to feature, persona, strategic goal, project, and vision.
Edition Awareness	Tag stories with SaaS editions if applicable.
Observability Embedding	Emit telemetry at each critical step.

🧩 ConnectSoft Platform Principles Alignment¶

Principle	Input Prompt Alignment
Domain-Driven Story Decomposition	Mapping stories to features, personas, bounded contexts.
Event-Driven Execution	Prepares output that triggers downstream agent events.
Cloud-Native Scalability	Edition-aware, SaaS-ready backlog decomposition.
Observability and Resilience	Input template embeds telemetry expectations.

📤 Output Expectations¶

The Product Owner Agent must generate high-quality, modular, AI-consumable, traceable artifacts
that allow downstream UX Designers, Architects, Backend, Frontend, Mobile Engineers to work autonomously without clarification.

Each output must meet strict ConnectSoft standards for:

Structure
Completeness
Testability
Traceability
Observability

This guarantees smooth, scalable, autonomous flow across the Software Factory.

📋 Primary Output Artifacts and Expectations¶

Artifact	Description	Required Structure
Product Backlog Artifact	Full list of decomposed User Stories, acceptance criteria, prioritization scores, traceability links.	Markdown + JSON
Individual User Story Artifacts	Each story stored with description, traceability metadata, acceptance criteria, prioritization score.	Markdown + JSON
Acceptance Criteria Artifact	Aggregated acceptance criteria, mapped to each User Story.	Markdown + JSON
Prioritization Matrix	Table of stories with MoSCoW or RICE scoring.	JSON List
Traceability Map	Links Vision ➔ Product Plan ➔ Feature ➔ User Story ➔ Edition ➔ Persona.	Markdown Table + JSON

🏗️ Example: Product Backlog Artifact (Markdown)¶

# 📋 Product Backlog - Healthcare Appointment Management SaaS

## Traceability Metadata
- Project ID: healthcare-saas-2025
- Vision Trace ID: vision-2025-04-27-001
- Product Plan Version: 1.0
- Artifact Version: 1.0

## User Stories

| Story ID | Description | Priority | MVP Scope | Editions | Personas | Acceptance Criteria |
|:---------|:------------|:---------|:----------|:---------|:---------|:--------------------|
| US-001 | As a Patient, I want to select an available appointment date, so that I can schedule easily. | Must Have | ✅ | Free, Pro | Patient | - Select available slot <br> - Confirm appointment <br> - Receive notification |
| US-002 | As a Doctor, I want to approve appointment requests, so that I can manage my calendar efficiently. | Must Have | ✅ | Pro, Enterprise | Doctor | - Review requests <br> - Accept/Reject request <br> - Sync to personal calendar |

✅ Clear separation of MVP scope, Edition targeting, Traceability Metadata, Acceptance Criteria.

📦 Example: Single User Story JSON¶

{
  "story_id": "US-001",
  "description": "As a Patient, I want to select an available appointment date, so that I can schedule easily.",
  "priority": "Must Have",
  "mvp_scope": true,
  "editions": ["Free", "Pro"],
  "personas": ["Patient"],
  "linked_feature": "Appointment Scheduling",
  "strategic_goals": ["Improve appointment engagement"],
  "trace_id": "vision-2025-04-27-001",
  "acceptance_criteria": [
    "Patient can view available appointment slots.",
    "Patient can confirm an appointment in under 60 seconds.",
    "Patient receives immediate confirmation notification."
  ]
}

✅ Fully modular
✅ Traceable
✅ Directly testable by QA/Automation agents

📣 Example: Event Emissions Expectations¶

Event Type	Triggered After	Payload Details
`BacklogReady`	Full Backlog validated and stored	Artifact URI, trace_id, version, timestamp
`StoryCreated`	Each User Story stored	Story ID, Artifact URI, trace_id
`AcceptanceCriteriaDefined`	Each Story's Acceptance Criteria defined	Story ID, Acceptance Criteria JSON

Example Event Payload:

{
  "event_type": "StoryCreated",
  "story_id": "US-001",
  "trace_id": "vision-2025-04-27-001",
  "artifact_uri": "https://storage.connectsoft.ai/backlogs/stories/us-001.json",
  "timestamp": "2025-04-28T02:00:00Z"
}

✅ Triggers downstream UX, Architecture, Development agent flows automatically.

🧩 Output Validation Requirements¶

Validation Area	Requirement
Traceability	Every artifact linked to Project ID, Vision Trace ID, Product Plan Version.
Completeness	Each feature fully decomposed into 1+ User Stories.
Acceptance Criteria	2–5 binary conditions per story.
Prioritization	All stories assigned MoSCoW or RICE scores.
Testability	Acceptance Criteria must be clearly binary/testable (no vague language).
Edition Awareness	If Edition Mapping exists, stories must have edition tags (Free, Pro, Enterprise).
Observability	Every major action traced/logged with success/failure status.

📚 Common Mistakes Guarded Against¶

Mistake	Prevented By
No acceptance criteria	Internal validation step on story generation.
Missing MVP markers	Automated checking during story creation.
Trace ID mismatch	Validation against assigned task metadata.
Edition tag omission	Edition Mapping cross-checking.
Unstructured story output	Mandatory Markdown and JSON output format enforcement.

🧩 ConnectSoft Platform Principles Alignment¶

Principle	Output Compliance
Event-Driven Factory	Outputs trigger system events and activate downstream agents.
Traceability-First	Every artifact can be traced back through full lineage.
Cloud-Native Scalability	Outputs stored in flexible backends and scalable formats.
SaaS Edition Readiness	Outputs reflect multi-edition SaaS product decompositions.
Observability Embedded	Full OpenTelemetry traces, logs, and metrics for every output phase.

🧠 Memory Management Overview¶

The Product Owner Agent uses a dual-memory strategy:

Short-Term Memory (STM) — To manage current task context during backlog decomposition.
Long-Term Semantic Memory (LTM) — To learn from previous similar decompositions, enhance quality, and increase output consistency.

This design ensures the agent operates effectively both:

Within a single project (short-term reasoning)
Across multiple projects (long-term strategic learning)

📋 Short-Term Memory (Context Window)¶

Aspect	Description
Scope	Limited to current backlog generation task.
Stored Content	Project Metadata (trace_id, project_id, deadlines), Vision Summary, Strategic Objectives, Personas, Feature Catalog, MVP features, Editions Mapping.
Purpose	Ensure contextual consistency across all user stories in a single execution.
Storage	In-process memory (Semantic Kernel execution context).
Lifetime	Cleared once backlog artifacts are validated and events emitted.

✅ Short-term memory ensures contextual consistency within a session without overloading the LLM token window.

📂 Long-Term Semantic Memory (Vector-Based Retrieval)¶

Aspect	Description
Storage	Semantic vector database (e.g., Azure Cognitive Search, Pinecone, Redis Vector, SQL + Embeddings, optionally MCP Servers【your clarified flexibility】).
Stored Content	Past Product Backlogs, User Stories, Acceptance Criteria, Prioritization Matrices, Story Templates by Domain/Industry.
Retrieval Method	Semantic similarity search (e.g., "Find similar healthcare SaaS backlogs").
Usage	Enrich story decomposition, align acceptance criteria with prior successful examples, apply domain-specific best practices.
Update Strategy	After successful backlog validation and emission, embeddings are created and stored back for future enrichment.

✅ Long-term memory enables continuous learning, semantic enrichment, and future-proof evolution of agentic behaviors.

🏗️ Memory Flow Diagram¶

flowchart TD
    ProductPlanIntake --> ShortTermContextCreation
    ShortTermContextCreation --> DecompositionSkills
    DecompositionSkills --> SemanticMemoryLookup
    SemanticMemoryLookup -->|Optional Enrichment| DecompositionSkills
    DecompositionSkills --> Validation
    Validation --> ArtifactStorage
    ArtifactStorage --> SemanticMemoryUpdate

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

✅ Semantic Memory is queried opportunistically and updated proactively.

📜 Example: Memory Storage Contents¶

Memory Type	Example Content
Short-Term	`"vision_summary": "Healthcare Appointment Scheduling SaaS"`, `"mvp_features": ["Appointment Booking", "Notification System"]`
Long-Term	User Story: "As a Patient, I want to book an appointment via mobile app..." Acceptance Criteria Templates: "Booking must complete in <60 seconds" Backlog Prioritization Patterns: Healthcare SaaS best practices.

📚 Smart Memory Behaviors¶

Scenario	Memory Behavior
Complex Feature (e.g., Billing System)	Query past billing-related story decompositions to enrich output.
New Industry Domain (e.g., EdTech)	Pull semantic patterns from EdTech semantic memory corpus.
Missing Persona Details	Find matching persona-to-feature behavior mappings from previous similar products.

🧠 Memory Management Safeguards¶

Risk	Mitigation
Semantic Drift	Always align enriched outputs with current project metadata and vision goals.
Stale Memory Usage	Limit semantic retrieval based on artifact freshness (e.g., only last 18 months by default).
Conflicting Domain Standards	Use domain matching scoring (e.g., healthcare backlogs shouldn't enrich a fintech product).

🧩 ConnectSoft Platform Principles Alignment¶

Principle	Memory Alignment
Elastic Scaling	Short-term memory lightweight per task.
Semantic Learning	Long-term memory enables cross-project evolution.
Cloud-Native Modularity	Memory backends configurable: Blob, SQL, Semantic DBs, MCP (optional).
Resilient Reasoning	Fall back to base templates if semantic memory retrieval fails.
Observability	Memory retrieval actions logged and traced for transparency.

✅ Validation Strategy¶

Before any backlog artifact is stored or any event is emitted,
the Product Owner Agent must perform strict internal validation to ensure:

Structural completeness
Semantic correctness
Traceability compliance
Prioritization and MVP coverage
Edition tagging correctness (if applicable)

Validation is mandatory —
✅ No unvalidated artifact is allowed to move downstream.

📋 Validation Types and Purposes¶

Validation Type	Purpose
Structural Validation	Ensure all required fields (story ID, description, acceptance criteria, trace IDs, prioritization) exist.
Semantic Validation	Ensure that user stories are INVEST-compliant (Independent, Negotiable, Valuable, Estimable, Small, Testable).
Acceptance Criteria Validation	Every story must have 2–5 binary acceptance criteria (clear pass/fail conditions).
Traceability Validation	Ensure story links to Feature ID, Persona, Strategic Goal, Trace ID, and Project ID.
Prioritization Validation	Ensure every story has MoSCoW or RICE prioritization.
Edition Tagging Validation (if applicable)	If editions mapping provided, ensure stories carry correct edition tags (Free, Pro, Enterprise).
Event Emission Validation	Ensure artifacts and payloads for `BacklogReady`, `StoryCreated`, `AcceptanceCriteriaDefined` are complete.
Observability Validation	Ensure all steps produce logs, metrics, and traces for telemetry.

🏗️ Validation Flow Diagram¶

flowchart TD
    DraftArtifacts --> StructuralValidation
    StructuralValidation -->|Pass| SemanticValidation
    SemanticValidation -->|Pass| TraceabilityValidation
    TraceabilityValidation -->|Pass| AcceptanceCriteriaValidation
    AcceptanceCriteriaValidation -->|Pass| PrioritizationValidation
    PrioritizationValidation -->|Pass| EditionTaggingValidation
    EditionTaggingValidation -->|Pass| EventEmissionValidation
    EventEmissionValidation -->|Pass| MarkArtifactAsValid
    EventEmissionValidation -->|Fail| CorrectionAttempt
    AnyFail --> CorrectionAttempt
    CorrectionAttempt --> RetryValidation

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

✅ If any step fails, a correction loop is automatically triggered.

📜 Detailed Validation Rules¶

📋 Structural Validation¶

Story ID must follow {prefix}-{sequence} format (e.g., US-001).
Description must follow "As a [persona], I want [goal], so that [benefit]" pattern.
At least one persona must be associated.
Acceptance criteria must be present.

🧠 Semantic Validation¶

Story must be:
- Independent: not tightly coupled to others.
- Negotiable: not a fixed spec.
- Valuable: delivers business value.
- Estimable: small enough to estimate.
- Small: deliverable in 1–3 days.
- Testable: measurable via acceptance criteria.

✅ Acceptance Criteria Validation¶

2–5 criteria per story.
No vague statements ("easy to use", "fast performance").
Each condition should be binary: pass/fail.

🧩 Traceability Validation¶

Story must embed:
- trace_id
- linked_feature
- linked_persona
- linked_strategic_goal
- artifact_version

🔢 Prioritization Validation¶

Each story must have:
- MoSCoW tag (Must, Should, Could, Won’t) OR
- RICE score fields (reach, impact, confidence, effort).

🏷️ Edition Tagging Validation (if applicable)¶

If editions mapping exists:
- Story must specify applicable editions.
- Editions must match configured tiers (Free, Pro, Enterprise).

📣 Event Validation Rules¶

Each event (BacklogReady, StoryCreated, AcceptanceCriteriaDefined) must:

Field	Requirement
`event_type`	Correct and standardized.
`trace_id`	Matches the originating Vision Document.
`artifact_uri`	Correct storage link.
`story_id` (for StoryCreated)	Must be unique per project.
`acceptance_criteria` (for AcceptanceCriteriaDefined)	Must match the linked story.
`timestamp`	ISO 8601 UTC time.

📈 Observability Validation¶

Emitted	Mandatory
OpenTelemetry Traces	✅ For story generation, validation, correction, storage, and event emission.
Structured Logs (Serilog)	✅ Success/Failure per action.
Prometheus Metrics	✅ Story generation rates, validation failures, retries triggered.

✅ Ensures every backlog flow is fully observable and auditable.

📚 Examples of Invalid vs Valid Cases¶

Case	Validation Result
Story with no Acceptance Criteria	❌
Story without Persona linked	❌
Story description not following format	❌
Story missing trace_id	❌
Story with non-binary Acceptance Criteria ("easy to use")	❌
Fully traceable, modular, binary-tested Story	✅

🧩 ConnectSoft Platform Principles Alignment¶

Principle	Validation Enforced
Self-Validating Agents	No invalid outputs proceed to downstream flows.
Modular Outputs	Every artifact validated independently.
Traceability	Full Vision ➔ Plan ➔ Feature ➔ Story lineage.
Observability-First Execution	Every critical step emits traces, logs, metrics.
Business-Goal Alignment	Semantic validations enforce business relevance.

🔁 Retry and Correction Flow¶

The Product Owner Agent is designed with an autonomous self-healing capability —
if any validation step fails, it attempts corrections internally before considering escalation.

This ensures:

High system resilience
Minimal human intervention
Smooth autonomous Factory execution

📋 Correction Strategy Overview¶

Error Type	Correction Attempt	Retry Behavior
Missing Fields	Auto-fill missing fields (e.g., missing trace IDs, linked features) from short-term memory context.	Immediate validation retry.
Incorrect Story Format	Reformat description to match "As a [persona], I want [goal], so that [benefit]" pattern.	Retry story validation.
Missing Acceptance Criteria	Regenerate binary acceptance criteria based on story description.	Retry validation immediately.
No Prioritization Tag	Auto-assign using default MoSCoW or fallback RICE scoring model.	Retry prioritization validation.
Edition Tag Omission	Infer editions from Feature or Product Plan if available; otherwise default to all editions.	Retry validation.
Event Emission Failure	Retry event submission with exponential backoff.	Up to 3 retries.

🏗️ Correction and Retry Flow Diagram¶

flowchart TD
    ValidationFailure --> AutoCorrectionAttempt
    AutoCorrectionAttempt --> RetryValidation
    RetryValidation -->|Pass| ArtifactStorage
    RetryValidation -->|Fail| SecondCorrectionAttempt
    SecondCorrectionAttempt --> RetryValidation2
    RetryValidation2 -->|Pass| ArtifactStorage
    RetryValidation2 -->|Fail| HumanInterventionTrigger

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

✅ Up to two full correction attempts are made automatically before escalating.

📚 Example Correction Techniques¶

Technique	Example
Context Rehydration	Re-inject project_id, trace_id, vision_summary from session memory.
Fallback Template Insertion	If missing Acceptance Criteria → use standard pattern templates (e.g., "User must be able to complete [action] within [timeframe]").
Semantic Retry	Re-run decomposition for problematic story based on updated context.
Edition Inference	If edition mapping missing → infer based on similar features using semantic search.

📜 Human Intervention Triggers (Fallback)¶

Situation	Immediate Escalation?
Validation fails after 2 auto-corrections	✅
Critical semantic drift detected (e.g., story no longer aligned to original strategic goals)	✅
Storage system unavailable after retries	✅
EventBus communication failure after retries	✅

When escalation happens: - Human operator is notified via escalation event. - Context snapshot is provided (failed artifacts, validation errors, correction attempts).

✅ Auditable escalation flow maintains Factory trust and transparency.

📈 Observability of Correction Flow¶

Observability Metric	Purpose
`product_owner_agent_validation_failures_total`	Total number of initial validation failures.
`product_owner_agent_corrections_attempted_total`	How often auto-corrections were triggered.
`product_owner_agent_successful_retries_total`	Number of retries that succeeded without human intervention.
`product_owner_agent_escalations_total`	Number of escalations to human operators.

✅ OpenTelemetry traces and Prometheus metrics monitor all correction attempts.

🧩 ConnectSoft Platform Principles Alignment¶

Principle	Correction Alignment
Resilient Execution	Built-in retries and auto-corrections.
Minimal Human Dependency	Autonomous healing prioritized.
Observability First	Corrections, retries, and escalations fully logged and traced.
Governed Escalation	Human intervention only if intelligent retries fail.
Elastic Scalability	Correction flows operate in parallel across large backlog decompositions.

🛠️ Skills Overview¶

The Product Owner Agent is composed of a modular skill system built using Semantic Kernel Functions.

Each Skill focuses on a specific sub-task inside the backlog decomposition lifecycle,
enabling dynamic, composable, and recoverable execution.

✅ Skills are independent, observable, retryable, and enhance agent scalability.

📋 Full Skills Catalog¶

Skill Name	Purpose	Example Internal Prompt
Feature Decomposer	Breaks Product Plan features into granular, INVEST-compliant User Stories.	"Decompose 'Appointment Scheduling' into multiple clear user stories."
User Story Author	Writes each story in "As a [persona], I want [goal], so that [benefit]" format.	"Draft a story for the Patient to select an appointment slot."
Acceptance Criteria Generator	Drafts 2–5 binary acceptance criteria for each User Story.	"Define clear, testable success criteria for selecting appointment slot."
Prioritization Scorer	Applies MoSCoW or RICE prioritization model to backlog items.	"Score each story based on business urgency and effort."
Edition Mapper	Tags stories according to SaaS Editions (Free, Pro, Enterprise) if applicable.	"Map 'Multi-clinic Management' story to Enterprise Edition only."
Traceability Enricher	Embeds full traceability metadata into each artifact.	"Inject trace_id, linked_feature_id, personas, strategic_goals into each story."
Story Validator	Performs structural, semantic, prioritization, and traceability validation of artifacts.	"Validate that Story US-001 has correct structure, acceptance criteria, and traceability."
Correction Synthesizer	Attempts auto-correction if validation fails (missing sections, wrong formatting).	"Fix missing acceptance criteria and reformat description."
Semantic Memory Retriever	Retrieves examples of similar stories, acceptance criteria, and backlog patterns.	"Fetch past Healthcare SaaS backlog structures for reference."
Event Preparer and Emitter	Prepares and sends events (`BacklogReady`, `StoryCreated`, `AcceptanceCriteriaDefined`) to the Event Bus.	"Emit StoryCreated event for US-001 with full metadata."

🏛️ Skills Categories¶

Category	Skills
Decomposition and Authoring	Feature Decomposer, User Story Author
Definition and Refinement	Acceptance Criteria Generator, Edition Mapper, Traceability Enricher
Prioritization and Optimization	Prioritization Scorer
Validation and Correction	Story Validator, Correction Synthesizer
Memory and Enrichment	Semantic Memory Retriever
Collaboration and Emission	Event Preparer and Emitter

🧠 Example Internal Skill Prompts¶

📚 Feature Decomposer¶

Decompose the following Product Feature into clear, INVEST-compliant user stories.  
Feature: "Appointment Scheduling"  
Personas: Patient, Doctor  
Strategic Objectives: Improve engagement, reduce no-shows  
Output format: Markdown + JSON

📚 User Story Author¶

Write a user story using the format:  
"As a [persona], I want [goal], so that [business benefit]."  
Persona: Patient  
Goal: Select an available appointment date  
Benefit: Schedule visits conveniently

📚 Acceptance Criteria Generator¶

Define 2–5 binary, testable acceptance criteria for the user story:  
"As a Patient, I want to select an available appointment date, so that I can schedule conveniently."

🏗️ Skills Orchestration Diagram¶

flowchart TD
    FeatureDecomposer --> UserStoryAuthor
    UserStoryAuthor --> AcceptanceCriteriaGenerator
    AcceptanceCriteriaGenerator --> PrioritizationScorer
    PrioritizationScorer --> EditionMapper
    EditionMapper --> TraceabilityEnricher
    TraceabilityEnricher --> StoryValidator
    StoryValidator -->|Pass| EventPreparerAndEmitter
    StoryValidator -->|Fail| CorrectionSynthesizer
    CorrectionSynthesizer --> StoryValidator

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✅ Skills are dynamically chained inside the Semantic Kernel planner.

📚 Skill Best Practices¶

Best Practice	Description
Minimal Chain Size	Keep chains small (2–5 skills per logical phase) to minimize retries.
Retry on Atomic Skill Level	Only retry the failing skill, not the entire chain.
Observability at Skill Level	Each skill emits its own OpenTelemetry traces.
Semantic Enrichment Optional	Semantic memory queries are invoked if confidence is low or complexity is high.

🧩 ConnectSoft Platform Principles Alignment¶

Principle	Skill System Alignment
Modularity	Each skill is independently improvable and extendable.
Resilience	Retry and correction flows embedded at skill level.
Observability	Traces and metrics emitted per skill execution.
Cloud-Native Extensibility	Skills can be scaled horizontally inside microservices.
Autonomous Reasoning	Each skill advances the agent's state intelligently, without manual intervention.

🔗 Collaboration Interfaces¶

The Product Owner Agent collaborates through:

Event-Driven Communication (primary method)
Artifact Storage (flexible — Blob, Git, SQL, Documentation Systems, optionally MCP)
Observability Streams (traces, logs, metrics)

It does not call downstream agents directly —
✅ It emits events and produces artifacts they autonomously consume.

📋 Collaboration Interfaces Overview¶

Interface Type	Purpose	Downstream Consumer
Event Emission	Notify downstream agents that backlog items are ready for consumption.	UX Designer Agent, Enterprise Architect Agent, Frontend Developer Agent, Backend Developer Agent, QA/Test Automation Agent.
Artifact Storage	Store Backlog, Stories, Acceptance Criteria, Traceability Maps in cloud-native or documentation systems.	Artifact Retrieval Services, GitHub/GitOps Agents, DevOps Pipelines, Manual Readers.
Semantic Memory Update	Save validated backlogs into semantic vector stores for future enrichment.	Future Product Owner Agents, Analysis Agents.
Observability Hooks	Emit real-time telemetry to ConnectSoft Observability Stack.	DevOps Teams, Factory Health Dashboards, Control Plane Audits.

🏗️ Event Emission Diagram¶

flowchart TD
    ProductOwnerAgent -->|BacklogReady Event| EventBus
    ProductOwnerAgent -->|StoryCreated Event (one per story)| EventBus
    ProductOwnerAgent -->|AcceptanceCriteriaDefined Event (one per story)| EventBus

    EventBus --> UXDesignerAgent
    EventBus --> EnterpriseArchitectAgent
    EventBus --> FrontendDeveloperAgent
    EventBus --> BackendDeveloperAgent
    EventBus --> QAEngineerAgent

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✅ Events allow full parallelization of downstream agent workflows —
✅ No bottleneck, no synchronous dependencies.

📦 Artifact Storage Strategies¶

Storage Type	Notes
Azure Blob Storage	Default for storing large backlog documents (Markdown, JSON).
Git Repositories (Azure DevOps, GitHub)	For structured, versioned backlogs and traceability artifacts.
SQL Databases (PostgreSQL, Azure SQL)	Structured story storage (optional for querying).
Documentation Systems (e.g., Azure DevOps Wiki)	For exposing backlog to human stakeholders.
MCP Servers (optional)	If semantic federated access across systems is needed.

✅ Configurable backend —
✅ No storage lock-in — flexibility based on project needs (per your clarified design).

📈 Observability Hooks¶

Observability Layer	Technology	Metrics/Traces/Logs
Tracing	OpenTelemetry	Span per skill execution, decomposition, validation, correction, storage, and event emission.
Logging	Serilog (structured JSON)	Logs for every major operation (story creation, correction attempts, event emissions).
Metrics	Prometheus-compatible exporters	Counters, Gauges, Histograms for backlog generation health.

✅ Fully integrated into ConnectSoft Observability-First Platform.

📚 Example Metrics Emitted¶

Metric Name	Purpose
`product_owner_agent_tasks_started_total`	Total Product Plan decomposition tasks started.
`product_owner_agent_stories_created_total`	Total number of user stories generated.
`product_owner_agent_validation_failures_total`	Number of initial validation failures detected.
`product_owner_agent_corrections_attempted_total`	Number of automatic corrections triggered.
`product_owner_agent_successful_retries_total`	Successful auto-corrections without human intervention.
`product_owner_agent_events_emitted_total`	Number of backlog-related events emitted successfully.

🧠 Real-Time Observability Flow Example¶

flowchart TD
    DecompositionSkill -->|Trace Span| OpenTelemetryCollector
    ValidationSkill -->|Structured Log| SerilogSink
    CorrectionSkill -->|Metric Update| PrometheusGateway
    ArtifactStorage -->|Success Event| EventBus

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✅ Every major agent activity is observable.
✅ Failures are visible immediately, with root cause traced through spans.

📋 Collaboration Safeguards¶

Risk	Mitigation
Event Emission Failure	Retry emission with exponential backoff (up to 3 times).
Storage Unavailability	Retry save operation or switch to alternate backend if configured.
Telemetry Emission Drop	Local buffering + retry on connection restoration.
Story Incompleteness	Correction flow before any event or artifact emission.

🧩 ConnectSoft Platform Principles Alignment¶

Principle	Collaboration Reflection
Event-Driven Software Factory	Full event-based decomposition and triggering.
Loose Coupling	No tight service-to-service dependency.
Cloud-Native Artifact Management	Storage layer is pluggable: Blob, Git, SQL, Documentation.
Observability-First	Full tracing, structured logging, and Prometheus-compatible metrics.
Resilient and Self-Recovering	Built-in retries, fallback mechanisms.

🛡️ Human Intervention Hooks¶

Although the Product Owner Agent is built for full autonomy,
controlled human intervention is supported for exceptional cases.

This guarantees:

High trustworthiness
Governance compliance
Resilient operation even under failure scenarios

📋 When Human Intervention is Triggered¶

Situation	Reason for Escalation	Example
Persistent Validation Failure	After two full auto-correction cycles, artifacts still invalid.	Story missing traceability or acceptance criteria even after retries.
Severe Semantic Drift	Stories no longer aligned with Vision's strategic objectives.	"Add Billing System" story added when not present in original scope.
Storage System Unavailable	After 3 retry attempts to save artifacts in configured storage.	Blob/Git/SQL backend failure.
Event Bus Communication Failure	After 3 retry attempts to emit `BacklogReady` or `StoryCreated`.	Event Grid outage or disconnection.
Critical Input Corruption	Product Plan or Vision Artifact invalid, preventing decomposition.	Empty feature catalog, missing personas.

🧠 Human Escalation Workflow¶

flowchart TD
    ValidationFailure --> CorrectionAttempt
    CorrectionAttempt -->|Fails Twice| EscalationTrigger
    EscalationTrigger --> HumanOperatorAlert
    HumanOperatorAlert --> ManualReview
    ManualReview -->|Fix Issue| RetryProcessing

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✅ Escalation triggers a human operator alert (email, dashboard alert, or ticket).

✅ The agent provides full context:
- Trace ID
- Failed artifacts
- Correction attempts history
- Error logs and traces

📈 Escalation Metrics Exposed¶

Metric Name	Purpose
`product_owner_agent_human_escalations_total`	Total escalations triggered.
`product_owner_agent_failed_retries_total`	Retry failures before escalation.

✅ Metrics emitted for auditability and platform observability.

🧩 Human Intervention Points in Platform¶

Platform Layer	Hook
Observability Dashboards (Grafana, Kibana)	Escalation alerts displayed.
Artifact Storage System	Flagged artifacts marked for human review.
Event Bus Monitoring	Escalation events published for ops teams.
Control Plane Governance	Escalations logged into Factory governance records.

🏗️ Final System Diagram: Product Owner Agent in Factory Flow¶

flowchart TD
    VisionArchitectAgent -->|VisionDocumentCreated| ProductManagerAgent
    ProductManagerAgent -->|ProductPlanCreated| ProductOwnerAgent
    ProductOwnerAgent -->|BacklogReady Event| EventBus
    ProductOwnerAgent -->|StoryCreated Events| EventBus
    ProductOwnerAgent -->|AcceptanceCriteriaDefined Events| EventBus

    EventBus --> UXDesignerAgent
    EventBus --> EnterpriseArchitectAgent
    EventBus --> FrontendDeveloperAgent
    EventBus --> BackendDeveloperAgent
    EventBus --> QAEngineerAgent

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✅ Seamlessly integrates into ConnectSoft autonomous Factory execution model.