🧠 Vision Architect Agent¶

🎯 Purpose¶

The Vision Architect Agent is the origin agent within the ConnectSoft AI Software Factory platform.
Its core purpose is to transform raw human intent, ideas, or business goals into clear, structured, and actionable software vision documents, regardless of the software type or industry.

It lays the foundational blueprint that enables the full autonomous software creation lifecycle —
from concept ➔ architecture ➔ engineering ➔ production deployment.

🚀 In short:
The Vision Architect converts ideas of any complexity into structured, AI-ready, and production-aligned software blueprints.

📋 Responsibilities¶

The Vision Architect Agent is responsible for producing high-fidelity, structured deliverables that define the target software system vision:

Responsibility	Description
Idea Structuring	Analyze freeform input (text, documents, discussions) and extract core objectives.
Problem Definition	Formalize the problem(s) the envisioned software is intended to solve.
Opportunity Framing	Identify strategic opportunities (market fit, internal optimizations, technical innovation).
Solution Conceptualization	Define the high-level system or product solution — not tied to any architecture yet.
Software Type Classification	Classify the software: SaaS, API, Mobile App, AI System, Enterprise Platform, etc.
Initial Feature Set Proposal	Draft a preliminary functional decomposition (features/modules/use cases).
Stakeholder and Persona Definition	Identify target users, administrators, external systems, and key stakeholders.
Success Criteria Formulation	Draft high-level success KPIs, metrics, or business outcomes.
Vision Artifact Creation	Generate a full Vision Document artifact, versioned and event-notified.
Traceability Anchoring	Embed unique identifiers for traceability across the AI Software Factory pipeline.
Downstream Enablement	Provide structured input for the Business Analyst Agent to derive detailed requirements and business models.

🏛️ Position in the Entire ConnectSoft AI Software Factory Process¶

flowchart TD
    HumanInput["Idea / Problem / Request / Goal"] --> VisionArchitectAgent
    VisionArchitectAgent --> VisionDocument["Structured Vision Document"]
    VisionDocument --> BusinessAnalystAgent
    BusinessAnalystAgent --> ProductManagerAgent
    VisionDocument --> EnterpriseArchitectAgent
    VisionDocument --> PlatformIntegratorAgent

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Upstream Input: Unstructured human communication.
Downstream Output: Structured vision document ➔ triggers multi-agent cascade across Product, Architecture, Engineering, DevOps tracks.

🏷️ ConnectSoft Platform Principles Alignment:¶

📚 Domain-Driven Design: Vision Document becomes a "Domain Vision Statement" or "Business Capability Intent" baseline.
🛜 Event-Driven Architecture: Emits VisionDocumentCreated event (with metadata: system type, domain, scope, urgency).
🧱 Cloud-Native and Multi-Modal: Vision output can serve cloud apps, edge computing, hybrid systems.
🤖 AI-First Modularization: Tailors vision to multi-agent decomposition, enabling scalable generation workflows.

🎯 Example Scenarios Supported¶

Scenario	Supported?	Notes
New SaaS Platform for Healthcare	✅	Yes, full medical app system analysis
Mobile App for Retail Loyalty	✅	Yes, mobile-first software
API for Payment Processing	✅	Yes, API-first platforms
Internal Enterprise Data Lake System	✅	Yes, pure infrastructure visions
Edge Device Firmware + Cloud Sync System	✅	Yes, cross-environment systems
AI Chatbot Platform	✅	Yes, multi-agent orchestration as output
DevTool CLI Utility	✅	Yes, minimalistic developer tool visions

📥 Inputs¶

The Vision Architect Agent consumes the following types of input data:

Input Type	Description	Example
Human-Provided Idea	Freeform descriptions (text, speech-to-text, documents) capturing problems, goals, ideas.	"I want a platform to help doctors manage patient records and schedule appointments."
Problem Statements	Clear definition of challenges or inefficiencies the client or user wants to address.	"Our onboarding process is too slow and manual."
Strategic Objectives	Business goals tied to the envisioned software.	"Reduce customer churn by 15% over the next 6 months."
Existing Assets	Documents, sketches, reports, process flows that relate to the envisioned system.	Uploaded PDFs, whiteboards, workflow diagrams.
Domain Context (optional)	Specific industries, regulatory contexts, markets the software must support.	"This platform must comply with HIPAA regulations."
Software Type (if available)	Optional hints about whether the user envisions a SaaS, API, app, system, tool, etc.	"A mobile app for field service technicians."

📤 Outputs¶

The Vision Architect Agent produces structured, machine-consumable artifacts:

Output Type	Description	Format/Example
Vision Document	Full structured description of the target software idea.	Markdown / JSON / YAML
Software Type Classification	Type(s) of software determined (SaaS, mobile app, API, system, etc.).	`"type": "Mobile App"`
Problem and Opportunity Mapping	Structured problem statement + opportunity framing.	Structured text, bullet lists.
Stakeholder Personas	Initial draft of key user roles and system stakeholders.	`"personas": ["Customer", "Admin", "Billing Manager"]`
Initial Feature Decomposition	Draft list of modules/features for future microservice, API, or component decomposition.	"Appointments", "Billing", "Patient Profiles"
Success Metrics	Initial KPIs or outcome goals for the system.	"Reduce appointment scheduling time by 30%"
Metadata & Traceability	Unique IDs, timestamps, origin tags for traceability across all agents.	`"trace_id": "vision-abc-2025-04-27"`
Events Emitted	`VisionDocumentCreated` event for downstream agents to react to.	MessageBus event: JSON payload

✅ The Vision Document serves as the foundation for business analysis and product planning —
it is not consumed directly by the Product Manager, but instead by the Business Analyst Agent, who refines it into detailed business artifacts.

✍️ Example System Prompts Used by Vision Architect Agent¶

System Prompt (Initialization Prompt):

"You are a Vision Architect Agent.
Your job is to analyze user ideas, problems, and goals, and produce a structured software vision document.
You must classify the software type, extract problem statements, identify opportunities, define stakeholders, propose an initial feature map, and define success metrics.
Output must be structured, complete, and AI-ready for downstream agents."

🧩 Example Input Prompt Template¶

Human input:
"{freeform_description}"

You must respond with:
1. Problem Definition
2. Opportunity Framing
3. Proposed Solution (high-level)
4. Software Type Classification
5. Initial Feature Set
6. Target Personas and Stakeholders
7. Success Metrics
8. Vision Document (Structured)

Respond in Markdown first, and additionally generate a compact JSON version.

🧾 Expected Output Format Example¶

# Software Vision Document

## Problem Definition
Current patient management systems are fragmented and slow, resulting in administrative inefficiencies.

## Opportunity
Unified SaaS platform for patient records, scheduling, and billing optimized for small clinics.

## Proposed Solution
A cloud-based SaaS application enabling doctors to manage patient profiles, appointments, and billing with mobile-first design.

## Software Type
- SaaS Platform
- Mobile App Extension

## Initial Feature Set
- Patient Profiles Management
- Appointment Scheduling
- Billing and Payments
- Notifications System
- Reporting Dashboard

## Stakeholders
- Doctors
- Administrative Staff
- Patients

## Success Metrics
- 30% reduction in missed appointments
- 20% faster billing cycle
- 90% adoption rate among clinic staff

{
  "problem": "...",
  "opportunity": "...",
  "solution": "...",
  "softwareType": ["SaaS", "Mobile App"],
  "features": ["Patient Management", "Appointments", "Billing"],
  "personas": ["Doctor", "Admin Staff", "Patient"],
  "successMetrics": ["Reduce missed appointments by 30%"]
}

🏛️ Visual: High-Level Data Flow for Inputs and Outputs¶

flowchart TD
    HumanIdea -->|Freeform Input| VisionArchitectAgent
    VisionArchitectAgent -->|Structured Vision Document| OutputDocument
    VisionArchitectAgent -->|Emit Event| EventBus["Event Bus"]
    EventBus --> ProductManagerAgent
    EventBus --> EnterpriseArchitectAgent

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📚 Knowledge Base¶

The Vision Architect Agent operates on a rich, structured internal knowledge base to contextualize user inputs and produce high-quality outputs.

Knowledge Area	Description
Software Industry Domains	Knowledge of SaaS, APIs, mobile apps, enterprise platforms, cloud systems, embedded software, AI systems, IoT, developer tools, etc.
Business Problem Taxonomy	Understanding of common business problems, bottlenecks, inefficiencies, digital transformation needs across industries.
Opportunity Mapping Models	Techniques to identify solution opportunities, product-market fit, innovation vectors.
User Persona Modeling	Frameworks for defining user types, stakeholders, and their goals/pain points.
Feature Decomposition Principles	How to break down a system into modules, services, APIs, workflows.
Software Lifecycle Fundamentals	Awareness of idea → architecture → engineering → deployment flows.
Strategic KPIs and Metrics	Typical success measures: adoption rates, revenue targets, efficiency gains, cost reduction, etc.
ConnectSoft Software Factory Ontology	Familiarity with ConnectSoft's internal standards for templates, agents, modular components, event-driven flows.

🛠️ Technologies¶

The Vision Architect Agent is powered by a blend of AI frameworks, cloud-native systems, and ConnectSoft infrastructure.

Technology	Purpose
Semantic Kernel (.NET)	Core orchestration framework for agents, skills, and plugins.
OpenAI API (GPT Models)	Natural language understanding and generation engine.
Azure OpenAI Service	Enterprise-grade hosted models for secure and scalable inference.
Azure Cognitive Search (optional)	To retrieve relevant domain-specific knowledge if context expansion needed.
Azure Storage	For storing long-term artifacts (e.g., Vision Documents, source prompts, session data).
Vector Database (e.g., Azure Cognitive Search, Pinecone)	For semantic memory, long-term vision retention, similarity search of previous visions.
Azure Event Grid or Kafka	For emitting `VisionDocumentCreated` events to other agents in event-driven pipelines.
ConnectSoft Observability Stack (OpenTelemetry, Serilog)	Full tracing, logging, metrics for the agent's executions.

🏗️ Visual: Technology Stack for Vision Architect Agent¶

flowchart TD
    SemanticKernel -->|Skill Orchestration| OpenAIService
    SemanticKernel -->|Data Storage| AzureBlobStorage
    SemanticKernel -->|Memory Embedding| VectorDB
    OpenAIService -->|LLM Responses| SemanticKernel
    SemanticKernel -->|Event Emission| EventBus
    SemanticKernel -->|Observability| OpenTelemetry
    EventBus --> ProductManagerAgent
    EventBus --> SolutionArchitectAgent

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📜 Example Technology Callouts¶

Info

Semantic Kernel enables building modular skills like "Problem Extraction Skill", "Opportunity Mapping Skill", "Persona Definition Skill" that Vision Architect chains together.

Tip

Vector Database ensures long-term memory, enabling the Vision Architect to reference previous visions for consistency or innovation prompts (e.g., "Find similar visions in FinTech from the past 3 months").

Info

OpenTelemetry hooks are automatically injected for every vision generation step, ensuring traceability, latency monitoring, and error tracking.

🔄 Internal Process Flow¶

The Vision Architect Agent operates as a multi-step, modular pipeline,
combining prompt engineering, skill invocation, reasoning validation, and artifact generation.

Here’s the high-level step-by-step process:

Step	Description
1. Input Intake	Receive initial human input (idea, problem statement, objectives, documents).
2. Preprocessing and Context Expansion	Normalize input, expand context if necessary (e.g., semantic enrichment using domain knowledge, vector memory retrieval).
3. Problem Extraction	Parse and structure the key problem(s) the user is trying to solve.
4. Opportunity Framing	Identify strategic angles, benefits, differentiation potential for the solution.
5. Solution Conceptualization	Draft the high-level product or system idea — without yet deep technical details.
6. Software Type Classification	Determine the category: SaaS, mobile app, API, platform, embedded system, etc.
7. Stakeholder and Persona Identification	Define the key user groups and system stakeholders.
8. Initial Feature Set Decomposition	Propose a preliminary breakdown of core features or modules.
9. Success Metrics Formulation	Draft measurable outcomes for evaluating solution success.
10. Vision Document Assembly	Synthesize all outputs into a structured, modular Vision Document (Markdown + JSON).
11. Traceability and Metadata Injection	Embed unique identifiers, timestamps, session info for downstream agents.
12. Emission of Vision Created Event	Publish `VisionDocumentCreated` event into event bus with all metadata.
13. Observability and Logging	Log execution trace, timing, errors, generated outputs for monitoring.
14. Human Intervention Checkpoint (Optional)	Offer a review option for human-in-the-loop validation before moving forward.

🧩 Modular Skills Invoked in Process¶

Skill	Purpose
Problem Extraction Skill	Parse input to structured problem definition.
Constraint Awareness Skill	Detect compliance, regulatory, or legal constraints that may impact solution (e.g., HIPAA, GDPR).
Opportunity Mapping Skill	Frame opportunities and strategic angles.
Solution Ideation Skill	Propose high-level solution structure.
Persona Identification Skill	Detect personas and stakeholders.
Feature Decomposition Skill	Suggest features/modules/services.
Metric Formulation Skill	Draft measurable success indicators.
Vision Assembly Skill	Compose structured output document.
Metadata Injection Skill	Add traceability and metadata headers.

🏗️ Visual: Vision Architect Agent Internal Process Flow¶

flowchart TD
    HumanInput["Human Idea / Problem"] --> InputPreprocessing
    InputPreprocessing --> ProblemExtraction
    ProblemExtraction --> OpportunityFraming
    OpportunityFraming --> SolutionConceptualization
    SolutionConceptualization --> SoftwareTypeClassification
    SoftwareTypeClassification --> PersonaIdentification
    PersonaIdentification --> FeatureDecomposition
    FeatureDecomposition --> SuccessMetricFormulation
    SuccessMetricFormulation --> VisionDocumentAssembly
    VisionDocumentAssembly --> MetadataInjection
    MetadataInjection --> EventEmission["Emit VisionDocumentCreated Event"]
    MetadataInjection --> ObservabilityLogging["Trace, Log, Metrics"]
    EventEmission -->|Next Step| ProductManagerAgent
    EventEmission -->|Next Step| SolutionArchitectAgent

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🛠️ Key Principles in Process Flow¶

Principle	How Applied
Agentic Composability	Each step is modular and could later be broken into sub-agents or skills.
Recoverability	If a step fails (e.g., missing personas), Vision Architect can retry or fallback gracefully.
Observability-First	Every step emits traces, metrics, and logs (structured with correlation IDs).
Human-Optional, not Human-Required	Human validation is a checkpoint, not a bottleneck.

📣 Special Callouts¶

Tip

Short-term memory (context window) holds the entire in-progress Vision Document as it evolves between steps.

Warning

Human review hook is triggered if agent confidence drops below a threshold (e.g., ambiguity detected, missing data patterns).

Info

Skill chaining between Problem Extraction ➔ Opportunity Mapping ➔ Solution Ideation is optimized for low-latency execution.

🛠️ System Prompt (Initialization)¶

The System Prompt is the bootstrap instruction provided when the Vision Architect Agent is initialized within a session.
It defines the agent's persona, responsibilities, boundaries, tone, and output expectations.

🔹 ConnectSoft System Prompt: Vision Architect Agent

You are the **Vision Architect Agent** in the **ConnectSoft AI Software Factory**.

Your mission is to transform any user-provided idea, business goal, or problem into a **structured, detailed, production-ready Software Vision Document**.

You must:

- Extract the core problem(s) and opportunities.
- Conceptualize a high-level solution without prescribing deep technical details.
- Classify the software type (SaaS, API, Mobile App, Platform, System, Embedded, AI System, etc.).
- Identify target users, system stakeholders, and personas.
- Propose a preliminary decomposition of key features or modules.
- Define high-level success criteria (KPIs, business outcomes).
- Output a structured, modular Vision Document in Markdown and JSON formats.
- Maintain professional, strategic, and clean business-technical language aligned with ConnectSoft standards.

You must:

- Always embed traceability metadata (session ID, timestamps, classifications).
- Always be ready for human-in-the-loop validation checkpoints if ambiguity detected.
- Log critical steps internally (traceable via observability hooks).

Your outputs must be machine-consumable by downstream agents and formatted precisely according to ConnectSoft factory standards.

📝 Input Prompt Template (Per Session)¶

The Input Prompt Template is dynamically created based on what the user provides.
It formats the user's input into a structured, focused request that the Vision Architect Agent can reason over optimally.

📄 Template Structure¶

🔹 ConnectSoft Vision Architect Input Template

Human Input:

{freeform_description}

Additional Context (if available):

- Problem Statement: {optional_problem_statement}
- Business Objectives: {optional_business_objectives}
- Target Market / Domain: {optional_domain_context}
- Constraints or Compliance Requirements: {optional_constraints}

Instructions:

- Analyze the human input carefully.
- Identify missing or unclear aspects if possible (ask clarifying questions if configured to do so).
- Structure the output as a full Vision Document.
- Use Markdown and JSON outputs.
- Embed traceability metadata.
- Follow ConnectSoft standards.

Respond only with:
- Problem Definition
- Opportunity Framing
- Proposed Solution
- Software Type Classification
- Initial Feature Set
- Stakeholder Personas
- Success Metrics
- Metadata Block (trace_id, session_time, software_type, domain_tags)

📜 Example Filled Input Prompt¶

Human Input:

"I want a tool for field workers to collect equipment status updates and sync them to our company servers in real-time."

Additional Context:

- Problem Statement: Field data is collected manually and delays occur.
- Business Objectives: Increase real-time visibility, reduce manual errors.
- Target Market / Domain: Utilities sector.
- Constraints: Must operate offline when needed.

Instructions: (Same as above.)

📄 Expected Raw Output Fragments¶

Markdown Vision Document (ready for Business Analyst / Product Manager / Architect agents).
JSON vision summary block.
Metadata block including:
- trace_id
- session_timestamp
- software_type
- classification_tags
- confidence_score

🏛️ Diagram: Prompt Handling Pipeline for Vision Architect Agent¶

flowchart TD
    UserInput --> Preprocessor
    Preprocessor -->|Input Prompt Template| SemanticKernel
    SemanticKernel -->|System Prompt + Input| LLM
    LLM -->|Structured Outputs| Postprocessor
    Postprocessor -->|Markdown + JSON + Metadata| VisionDocument
    VisionDocument --> EventEmitter

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📣 Key Points:¶

Tip

The System Prompt must always be loaded and active for the Vision Architect to maintain its role during multi-turn conversations.

Info

The Input Template dynamically adjusts: if no business objectives provided, the agent tries to infer based on best practices.

Warning

If constraints (e.g., "must be HIPAA compliant") are detected, they must be explicitly reflected in the Vision Document.

📤 Output Expectations¶

The Vision Architect Agent must always produce outputs that are structured, clean, complete, and modular.
All outputs must be immediately machine-consumable by downstream agents (Product Manager, Enterprise Architect, etc.).

Output Type	Format	Description
Vision Document	Markdown	Detailed structured description of the envisioned software product.
Compact Vision Summary	JSON	Concise structured object with key fields for fast parsing and indexing.
Metadata Block	JSON	Traceability data: session ID, timestamps, software type, confidence score, domain tags.
Event Emission	EventBus Payload (JSON)	Publish `VisionDocumentCreated` event to the system event bus.

📄 Output Structure (Markdown)¶

Example structure the Vision Architect must always follow:

# Software Vision Document

## Problem Definition
...

## Opportunity
...

## Proposed Solution
...

## Software Type
...

## Initial Feature Set
- Feature 1
- Feature 2
- Feature 3

## Stakeholder Personas
- Persona 1
- Persona 2
- Persona 3

## Success Metrics
- Metric 1
- Metric 2

## Constraints (if any)
...

## Metadata
- trace_id: {trace_id}
- version: 1
- session_timestamp: {timestamp}
- software_type: {type}
- domain_tags: {tags}
- confidence_score: {score}

📦 Output Structure (JSON)¶

Example:

{
  "problem": "Fragmented field data collection.",
  "opportunity": "Real-time visibility and reduced errors.",
  "solution": "Mobile app with offline syncing capabilities.",
  "softwareType": ["Mobile App", "Cloud API Backend"],
  "features": ["Offline Data Collection", "Real-Time Sync", "Reporting Dashboard"],
  "personas": ["Field Worker", "Operations Manager", "IT Administrator"],
  "successMetrics": ["90% data availability within 5 minutes", "20% reduction in error rates"],
  "constraints": ["Offline mode support"],
  "metadata": {
    "trace_id": "vision-20250427-xyz",
    "session_timestamp": "2025-04-27T09:00:00Z",
    "software_type": "Mobile App",
    "domain_tags": ["Utilities", "FieldOps"],
    "confidence_score": 0.93
  }
}

🧠 Memory Management¶

The Vision Architect Agent must manage short-term memory and long-term memory efficiently to support multi-turn conversations, reusability, and context retention.

Memory Type	Purpose	Storage Mechanism
Short-Term Memory (Session Context Window)	Keep track of current in-progress vision, conversation, extracted details.	In-memory context/session (within Semantic Kernel).
Long-Term Memory (Persistent Semantic Memory)	Store completed visions, summaries, classifications for future retrieval, comparisons, corrections.	Vector Database (e.g., Azure Cognitive Search, Pinecone, Redis Vector Embedding).

🧠 Short-Term Memory Behavior¶

Holds entire in-progress document (markdown + json) between skill calls.
Supports multi-turn elaborations (e.g., user refining vision after first draft).
Holds conversation history for human validation intervention.

🧠 Long-Term Memory Behavior¶

Once a Vision Document is finalized, persist embeddings of:
- Problem Statement
- Solution Overview
- Software Type
- Domain Tags
Enables later similarity search, inspiration from previous visions, cross-project awareness.

🏛️ Diagram: Memory Flow of Vision Architect Agent¶

flowchart TD
    HumanSessionInput --> ShortTermMemory["Short-Term Memory (Context Window)"]
    ShortTermMemory --> SkillsExecution
    SkillsExecution --> OutputDraft
    OutputDraft -->|Persist| LongTermMemory["Long-Term Memory (Vector DB)"]
    OutputDraft -->|Emit Event| EventBus
    LongTermMemory -->|Future Retrieval| InspirationForOtherAgents

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📣 Key Constraints¶

Info

Short-term memory must be flushed after vision handoff to avoid context pollution across multiple visions.

Warning

Long-term memory must embed vision outputs using secure, multi-tenant, and domain-isolated models (respecting ConnectSoft multi-tenancy standards).

Tip

Every output Vision Document must have its trace_id as the primary semantic vector reference key.

✅ Validation Logic¶

After generating its output, the Vision Architect Agent must self-validate to ensure the output is complete, coherent, and compliant with ConnectSoft Software Factory expectations.

Validation Step	Description
Structural Validation	Check that all mandatory sections (problem, opportunity, solution, software type, features, personas, success metrics, metadata) are present and non-empty.
Semantic Coherence Check	Ensure the proposed solution logically addresses the defined problem and aligns with the opportunity framing.
Software Type Validation	Confirm that the software classification (e.g., SaaS, mobile app) matches solution context.
Feature-Problem Alignment Check	Validate that suggested features map clearly back to solving the primary problem(s).
Persona-Solution Match	Confirm that identified personas are logically connected to the solution scope.
Success Metrics Quality Check	Ensure KPIs are measurable, specific, and achievable — not vague or undefined.
Metadata Completeness	Validate presence of `trace_id`, `timestamp`, `software_type`, `tags`, `confidence_score`.
Output Formatting Validation	Verify Markdown and JSON are properly formatted and parsable (lint JSON if needed).
Confidence Score Evaluation	Check if confidence score is above a minimum threshold (e.g., 0.85) to allow auto-publishing.

🧩 Self-Validation Skill Chain¶

The agent runs modular validation skills in order:

1. Structure Validator ➔
2. Semantic Validator ➔
3. Alignment Validator ➔
4. Metric Validator ➔
5. Metadata Completeness Validator ➔
6. Final Sanity Check

Each validator skill either:

Passes ✔️
Fails and provides hints for auto-correction ➡️

🔁 Retry/Correction Flow¶

If validation fails, the Vision Architect follows an auto-correction and retry loop:

flowchart TD
    GenerateVision --> ValidateOutput
    ValidateOutput -->|Pass| EmitEvent
    ValidateOutput -->|Fail| CorrectionStep
    CorrectionStep -->|Modified Draft| Revalidate
    Revalidate -->|Pass| EmitEvent
    Revalidate -->|Fail| HumanInterventionCheck
    HumanInterventionCheck -->|Require Manual Edit| HumanEditor

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Step	Action
Initial Validation	Attempt automatic validation after generation.
Auto-Correction Attempt	If minor issues (missing field, unclear phrasing), auto-correct and retry validation.
Re-Validation	After correction, validate again.
Escalation to Human	If repeated failures (2 retries max) or critical gaps (e.g., no solution framed), escalate to Human-in-the-Loop Intervention Mode.

🚦 Retry Policies¶

Scenario	Auto-Correct?	Max Retries	Human Intervention?
Missing mandatory sections	✅	2	✅ if still missing
Incoherent solution framing	✅ (attempt reframe)	1	✅
Software type mismatch	✅ (reclassify)	1	✅
Broken JSON structure	✅ (auto-lint)	2	✅
Confidence score too low (< 0.85)	🚫 (cannot auto-increase)	0	✅

📣 Special Handling¶

Info

If ambiguity is detected (e.g., multiple possible software types inferred), the agent must generate a clarification request prompt for a human (if configured).

Warning

Vision Architect must never proceed to emit a VisionDocumentCreated event if any mandatory section is missing or if confidence is critically low.

Tip

Observability system must log each retry, correction attempt, and escalation event for later auditing.

📑 Correction Example (Auto-Fix)¶

Problem:

Missing "Stakeholder Personas" section.

Auto-Fix Action:

Re-analyze solution description and infer likely personas (e.g., Field Worker, Supervisor, Admin).

Reinsertion:

Populate "Stakeholder Personas" section and mark with confidence annotation.

🏛️ Diagram: Validation and Correction Lifecycle¶

flowchart TD
    GeneratedOutput --> StructuralValidation
    StructuralValidation --> SemanticValidation
    SemanticValidation --> CoherenceCheck
    CoherenceCheck --> FeatureMappingCheck
    FeatureMappingCheck --> MetricCheck
    MetricCheck --> MetadataValidation
    MetadataValidation -->|All Pass| Finalization
    MetadataValidation -->|Any Fail| AutoCorrectionAttempt
    AutoCorrectionAttempt -->|Revalidate| ValidationRetry
    ValidationRetry -->|Fail Again| HumanReviewMode

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🔗 Agent Communication Overview¶

The Vision Architect Agent is not an isolated agent —
it must initiate, coordinate, and transfer knowledge seamlessly to other agents and system components after completing its vision generation.

It collaborates primarily via event-driven, API-based, and memory-based communication patterns.

📡 Direct Collaboration Targets¶

Target Agent/System	Purpose of Collaboration	Interaction Mode
Business Analyst Agent	Consumes Vision Document to derive Business Requirements Document (BRD), business rules, personas, workflows.	Event-driven + Shared Artifact Access
Product Manager Agent	Receives validated Business Requirements (via BRD) to produce product plans, MVPs, and roadmaps.	Event-driven + Shared Storage (Blob, Vector DB)
Enterprise Architect Agent	Provide vision as input for system architecture modeling, DDD context design, bounded contexts definition.	Event-driven + Direct API call
Solution Architect Agent	Supply preliminary solution concept to refine into a concrete technical solution (Clean Architecture mapping).	Event-driven
Knowledge Management Agent	Persist vision artifacts in long-term memory (knowledge base), ensuring traceable retrieval for future refinements.	Vector embedding push
Growth Strategist Agent (optional)	Offer early visibility into market potential or positioning opportunities.	Event-driven, optional
Event Bus (Azure Event Grid / Kafka)	Publish `VisionDocumentCreated` event to the AI Factory Event Mesh for decoupled downstream consumption.	Event emission

🧩 Collaboration Interfaces Design¶

Type	Mechanism	Notes
Event Emission	Standardized `VisionDocumentCreated` event schema, versioned payloads.	Fully structured JSON with traceability metadata.
Shared Artifact Storage	Upload full Vision Document (Markdown + JSON) into ConnectSoft Storage.	URI attached in event payload for retrieval.
Vector Memory Embedding	Push semantic summary to Vector DB with project-specific trace_id.	Enables cross-agent semantic search.
Direct REST API (internal)	(Optional) Vision Architect can POST vision payload directly to Product Manager or Enterprise Architect HTTP endpoints.	For tightly coupled agent orchestration if needed.

📜 Example: VisionDocumentCreated Event Schema¶

{
  "eventType": "VisionDocumentCreated",
  "eventTime": "2025-04-27T10:15:00Z",
  "data": {
    "trace_id": "vision-20250427-xyz",
    "software_type": "Mobile App",
    "domain_tags": ["Field Operations", "Utilities"],
    "vision_document_uri": "https://connectsoft.blob.core.windows.net/visions/vision-20250427-xyz.md",
    "summary": {
      "problem": "...",
      "opportunity": "...",
      "solution": "..."
    },
    "confidence_score": 0.92
  },
  "metadataVersion": "1.0"
}

🛠️ Example Collaboration Flow¶

sequenceDiagram
    participant VisionArchitect
    participant EventBus
    participant ProductManager
    participant EnterpriseArchitect
    participant KnowledgeManager

    VisionArchitect->>EventBus: Emit VisionDocumentCreated
    EventBus-->>ProductManager: Notify of new Vision
    EventBus-->>EnterpriseArchitect: Notify of new Vision
    VisionArchitect->>KnowledgeManager: Persist Vision Summary in Vector DB

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📣 Best Practices for Collaboration¶

Tip

Always include traceability metadata (trace_id, domain, project id) in every communication to ensure complete cross-agent auditability.

Warning

Vision Architect Agent must never push incomplete visions to other agents — strict validation is a prerequisite to emission.

Info

Event mesh must guarantee at-least-once delivery, and agents consuming VisionDocumentCreated must be idempotent.

📑 Collaboration Responsibilities by Interface¶

Interface	Vision Architect Responsibility
Event Bus	Emit vision event once validation passes.
Shared Storage	Save full vision artifacts with correct foldering (e.g., per project, per date).
Vector Database	Embed concise semantic memory immediately after vision finalization.
REST API (Optional)	Notify critical agents via direct HTTP if configured for faster workflows.

📊 Observability Hooks¶

In ConnectSoft AI Software Factory, every agent — including Vision Architect — must be fully observable by default:
Logs, traces, metrics must be automatically collected, structured, and exportable.

Observability Type	Description	Tooling
Structured Logging	Log every major action (input received, output generated, validation passed/failed, retries, events emitted).	Serilog (JSON logs)
Distributed Tracing	Track the full execution path across skills, retries, corrections, event emission.	OpenTelemetry SDK
Custom Metrics	Emit Prometheus-compatible metrics (counters, histograms, gauges) for health and performance monitoring.	OpenTelemetry Metrics + Prometheus
Event Auditing	Log every VisionDocumentCreated event and correlate with session IDs and trace IDs.	Internal Event Audit Log

📜 Key Logs Captured¶

Log Entry	Example Fields
Session Start	`session_id`, `human_input_summary`, `start_timestamp`
Vision Generated	`trace_id`, `software_type`, `confidence_score`, `feature_count`
Validation Result	`validation_status`, `errors_detected`, `auto_correction_attempts`
Retry Attempt	`retry_number`, `reason`, `correction_action`
Event Emitted	`event_type`, `target_agents`, `vision_uri`
Human Intervention Triggered	`reason_for_escalation`, `missing_fields`, `confidence_score`

📈 Key Metrics Exposed¶

Metric	Description
`visions_generated_total`	Total number of visions successfully generated.
`vision_validation_failures_total`	Total number of validation failures.
`vision_retry_attempts_total`	How many retries were needed on average.
`human_intervention_triggers_total`	Number of visions requiring human manual fix.
`vision_generation_duration_seconds`	Histogram of how long vision generation takes.
`vision_confidence_score_distribution`	Distribution of confidence scores assigned.
`vision_events_emitted_total`	Count of successful `VisionDocumentCreated` events.

🏗️ Visual: Observability Integration Flow¶

flowchart TD
    SessionStart --> LogStartSession
    VisionGeneration --> LogVisionOutput
    Validation --> LogValidation
    RetryAttempts --> LogRetry
    EventEmission --> LogEvent
    HumanIntervention --> LogEscalation
    LogStartSession --> ObservabilityStack
    LogVisionOutput --> ObservabilityStack
    LogValidation --> ObservabilityStack
    LogRetry --> ObservabilityStack
    LogEvent --> ObservabilityStack
    LogEscalation --> ObservabilityStack

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🤖 Human Intervention Points¶

While Vision Architect Agent is designed for autonomous operation,
humans can (and should) intervene when:

Situation	Intervention Action
Validation Fails After Retry	Human is asked to manually complete missing sections.
Low Confidence Detected (e.g., < 0.80)	Human reviews the generated vision and either approves, edits, or regenerates.
Conflicting Interpretations	(e.g., two possible software types detected) — agent proposes alternatives, human selects best.
Sensitive Domain Detected (e.g., Healthcare, Finance)	Human validation enforced before proceeding due to regulatory risks.
Critical Information Missing	(e.g., no clear user personas extracted) triggers a human clarification session.

🧩 How Human Intervention Works¶

flowchart TD
    VisionGenerated --> ValidationCheck
    ValidationCheck -->|Fail or Low Confidence| HumanInterventionTrigger
    HumanInterventionTrigger --> HumanNotification
    HumanNotification --> HumanReviewPortal
    HumanReviewPortal -->|Approve| EmitEvent
    HumanReviewPortal -->|Edit and Save| EmitEvent
    HumanReviewPortal -->|Reject| RestartProcess

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Mechanisms:

✅ Notification to human reviewer (via dashboard, portal, or messaging system).
✅ View/edit generated Vision Document in ConnectSoft Admin Portal.
✅ Approve, modify, or reject with explanations.

📣 Best Practices¶

Tip

All human interventions should be logged with traceability — reviewer ID, action taken, reasons.

Warning

Human intervention should be configured per project or domain — default is "Autonomous unless critical".

Info

Metrics on human interventions help improve agent autonomy rates and refine training over time.

🏗️ Full Agent Blueprint Overview¶

The Vision Architect Agent is a Level 1 (Entry Point) Core Agent in the ConnectSoft AI Software Factory.
It transforms raw human intent into structured software vision artifacts, enabling downstream autonomous software generation.

📚 Complete Logical Responsibilities Map¶

flowchart TD
    A1[Human Input: Idea / Goal / Problem] --> A2[Vision Architect Agent]
    A2 --> B1[Problem Extraction Skill]
    A2 --> B2[Opportunity Framing Skill]
    A2 --> B3[Solution Conceptualization Skill]
    A2 --> B4[Software Type Classification Skill]
    A2 --> B5[Stakeholder Persona Identification Skill]
    A2 --> B6[Feature Decomposition Skill]
    A2 --> B7[Success Metric Formulation Skill]
    B1 & B2 & B3 & B4 & B5 & B6 & B7 --> C1[Vision Document Assembly Skill]
    C1 --> D1[Validation Skills]
    D1 -->|Pass| E1[Emit VisionDocumentCreated Event]
    D1 -->|Fail| F1[Retry/Correction Flow]
    F1 -->|2 Retries Max| E1
    F1 -->|Fail| G1[Human Review Intervention]
    E1 --> H1[Product Manager Agent]
    E1 --> H2[Enterprise Architect Agent]
    E1 --> H3[Solution Architect Agent]
    E1 --> H4[Knowledge Management Agent]

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📈 Technical Stack Blueprint¶

flowchart TD
    SemanticKernel -->|Skills Orchestration| OpenAIService
    SemanticKernel -->|Short-Term Memory| InMemoryContext
    SemanticKernel -->|Long-Term Memory| VectorDB
    SemanticKernel -->|Emit Event| EventBus
    SemanticKernel -->|Observability| OpenTelemetryStack
    OpenAIService -->|Natural Language Understanding| SemanticKernel
    VectorDB -->|Store Summarized Visions| KnowledgeBase
    EventBus --> ProductManagerAgent
    EventBus --> EnterpriseArchitectAgent
    EventBus --> SolutionArchitectAgent

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🧩 Component and Subsystem Diagram¶

flowchart LR
    subgraph Vision Architect Agent
        HumanInput --> InputPreprocessing
        InputPreprocessing -->|Detect Ambiguity?| ClarificationSkill
        ClarificationSkill -->|If Clarification Needed| HumanClarificationRequest
        ClarificationSkill -->|Else| SkillChain
        SkillChain --> VisionDraft
        VisionDraft --> ValidationModule
        ValidationModule -->|Pass| EventEmissionModule
        ValidationModule -->|Fail| RetryCorrectionModule
        RetryCorrectionModule -->|Success| EventEmissionModule
        RetryCorrectionModule -->|Escalation| HumanInterventionModule
        EventEmissionModule --> EventBusPublisher
    end
    EventBusPublisher --> ProductManagerAgent
    EventBusPublisher --> EnterpriseArchitectAgent
    EventBusPublisher --> SolutionArchitectAgent

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🛡️ Responsibility Layer Mapping¶

Layer	Responsibilities
Session Management	Accept input, manage session memory.
Skill Chain Execution	Invoke modular skills sequentially.
Validation & Correction	Validate outputs, attempt auto-fixes.
Artifact Assembly	Build Markdown and JSON vision documents.
Metadata & Traceability	Add traceability headers, timestamps, classifications.
Event Emission	Emit structured events to factory system.
Observability Hooks	Emit logs, traces, metrics for full visibility.
Human Intervention Points	Handle escalations cleanly if needed.

🧠 Key Highlights¶

✅ Skills are modular and can evolve independently over time.
✅ Vision is always traceable end-to-end (trace ID across all outputs, memory entries, events).
✅ Observability-first with full logs, traces, and metrics export.
✅ Agent collaboration designed in an event-driven decoupled way.
✅ Human-in-the-loop capability only triggers when truly needed (critical gaps, confidence issues).

🧩 Vision Architect Agent in the Entire Factory Flow (Macro View)¶

flowchart TD
    HumanInput --> VisionArchitectAgent
    VisionArchitectAgent --> VisionDocument
    VisionDocument --> ProductManagerAgent
    VisionDocument --> EnterpriseArchitectAgent
    VisionDocument --> SolutionArchitectAgent
    VisionDocument --> KnowledgeManagementAgent
    ProductManagerAgent --> FeatureSpecifications
    EnterpriseArchitectAgent --> HighLevelSystemDesign
    SolutionArchitectAgent --> CleanArchitectureBlueprint

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📚 Conclusion for Vision Architect Agent Spec¶

The Vision Architect Agent is the originating seed of the entire ConnectSoft AI Software Factory flow.
It defines what will be built, why it matters, and how the initial concept will be decomposed —
while ensuring traceability, modularity, and future extensibility for every software project launched inside the Factory.

🏗️ Without a clear, structured vision, no autonomous software generation process can succeed — the Vision Architect Agent guarantees that foundation is perfect.

🚀 Future Extensions¶

In upcoming versions, the Vision Architect Agent could evolve to:

Automatically generate Lean Canvas Models.
Produce Business Model Canvas structures.
Recommend strategic product differentiators.
Align vision documents directly to regulatory compliance models (e.g., HIPAA, GDPR blueprints).