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# Agent-Personas System v1.0.0 - Feature Overview ## Specialized AI Agent Team for Autonomous Software Development **Date:** October 16, 2025 **Status:** 📋 PLANNING PHASE **Next Phase:** Context Analysis & Implementation Planning --- ## Executive Summary Agent-Personas is an MCP server providing highly-trained specialist AI agents for autonomous software development. Each agent has domain expertise, follows standardized communication protocols, and delivers precise results through coordinated teamwork. **Key Goals:** - Improved task completion efficiency via expert specialization - Complex multi-step workflow automation - Parallel processing across different task types - Context-aware execution using project-specific standards --- ## Core Agent Roster ### Meta-Level Coordinators #### Project Leader (Coordinator) **Type:** Oversight & Orchestration **Responsibilities:** - Break down complex requests into subtasks - Delegate tasks to appropriate specialist agents - Monitor progress and coordinate between agents - Make architectural decisions and approvals - Validate deliverables against success criteria - Manage phase transitions and handoffs - Maintain documentation and changelog #### Project Assistant **Type:** User-Facing Support **Responsibilities:** - Answer questions about project (codebase, architecture, decisions) - Track tasks and progress (TODO management, status updates) - Run routine checks (tests, builds, linting) - Gather information and conduct research - Take notes and document decisions - Work interactively with user as dedicated assistant --- ## Specialist Agent Domains ### 1. Code-Focused Agents - **Debugging Specialist**: Root cause analysis, error resolution - **Refactoring Expert**: Code quality improvement, pattern optimization - **Testing Engineer**: Unit/integration/E2E test creation, coverage analysis ### 2. UI/UX Agents - **UI Component Expert**: Component design, accessibility, best practices - **UX Analyst**: User flow optimization, interaction patterns - **Design System Specialist**: Consistency enforcement, design tokens ### 3. Framework Specialists - **React/Vue/Angular Expert**: Framework-specific best practices - **Backend Framework Specialist**: FastAPI, Flask, Express, etc. - **Full-Stack Architect**: Cross-layer integration ### 4. Documentation & Planning - **Technical Writer**: API docs, user guides, README generation - **Architecture Planner**: System design, implementation planning - **Process Documentarian**: Workflows, runbooks, decision logs ### 5. Analysis & Review - **Code Reviewer**: Quality gates, standards enforcement - **Security Auditor**: Vulnerability scanning, compliance checks - **Performance Analyst**: Optimization, profiling, bottleneck detection ### 6. Integration & DevOps - **API Integration Specialist**: Third-party integrations, SDK usage - **CI/CD Engineer**: Pipeline configuration, deployment automation - **Infrastructure Expert**: Docker, Kubernetes, cloud deployment --- ## Agent Training Strategy ### Static Training (Pre-configured) - Domain-specific prompts and instructions - Best practices and coding standards - Anti-patterns and pitfalls to avoid - Quality checklists and validation rules ### Dynamic Context Loading - Project-specific codebase analysis - Extracted coding standards (from /establish-standards) - Component patterns and examples - Framework documentation and guides - Real-world reference implementations ### Knowledge Base Access - Framework documentation - Testing strategies - Security best practices - Performance optimization guides - Accessibility standards (WCAG, ARIA) --- ## Communication Protocol **Source:** `agentic-interaction.json` standard ### Message Types | Type | Purpose | Response Time | |------|---------|---------------| | **handoff** | Phase completion & transition | Same session | | **question** | Clarification request | Same session | | **blocker** | Critical issue | Immediate | | **update** | Progress notification | Async | | **validation** | Deliverable review | Same session | | **completion** | Phase finished | Immediate | ### Message Structure ```json { "id": "msg_001", "timestamp": "2025-10-16T12:00:00Z", "from": "ui_expert", "to": "project_leader", "phase": "implementation", "type": "validation", "priority": "high", "subject": "Component review complete", "message": "Detailed status...", "status": "posted", "action_required": "Approve merge" } ``` ### Priority Levels - **High**: Address within same session - **Medium**: Address before phase completion - **Low**: Informational, no immediate action ### Coordination Rules - ✅ Sequential execution (one phase at a time unless parallel authorized) - ✅ Dependency enforcement (Phase N waits for N-1) - ✅ Documentation mandatory (all decisions logged) - ✅ Commit before handoff (all changes committed) - ✅ Coordinator validation (approval before transition) --- ## Integration Methods ### 1. MCP Tools ```typescript mcp__agents__invoke_ui_expert mcp__agents__invoke_backend_architect mcp__agents__invoke_test_engineer mcp__agents__invoke_security_auditor ``` ### 2. Slash Commands ```bash /ui-review # UI/UX expert review /framework-migrate # Framework migration specialist /security-audit # Security vulnerability scan /performance-check # Performance analysis /code-review # Code quality review ``` ### 3. Automatic Routing - System analyzes user request - Determines required expertise - Routes to appropriate specialist agent - Returns expert-level response ### 4. Custom Personas ```json // config/agents/custom-agent.json { "id": "custom_agent_1", "role": "GraphQL Specialist", "type": "specialist", "domains": ["graphql", "api", "schema"], "training": { "static": "prompts/graphql-expert.txt", "context": ["docs-mcp://standards/api"] } } ``` --- ## Persistence & Tracking ### Database Storage - **External Database**: Agent context separate from project files - **Schema**: agents, messages, work_orders, context_snapshots - **Retention**: Configurable TTL per context type ### Work Order System ```json { "work_order_id": "WO-2025-001", "task_id": "TASK-123", "agent": "ui_expert", "project": "my-app", "status": "completed", "summary": "Component accessibility audit", "artifacts": ["audit-report.md", "fixes.patch"], "completed_at": "2025-10-16T14:30:00Z" } ``` ### Context Persistence - Session memory across conversations - Agent collaboration logs - Decision history tracking - Archived project integration - Summarization on completion --- ## Phase Overview | Phase | Description | Duration | Status | |-------|-------------|----------|--------| | **Phase 0** | Context Gathering & Planning | 1 week | 📋 Current | | **Phase 1** | Core Agent Framework | 2-3 weeks | 📋 Planned | | **Phase 2** | Communication Protocol | 1-2 weeks | 📋 Planned | | **Phase 3** | Specialist Agents (Batch 1) | 3-4 weeks | 📋 Planned | | **Phase 4** | Persistence & Database | 1-2 weeks | 📋 Planned | | **Phase 5** | MCP Integration | 1-2 weeks | 📋 Planned | | **Phase 6** | Testing & Validation | 2 weeks | 📋 Planned | | **Total** | **Full Implementation** | **10-16 weeks** | **Planning** | --- ## Success Criteria ### Functional Requirements - ✅ Project Leader can decompose complex tasks - ✅ Project Assistant responds to user queries < 2s - ✅ Specialist agents deliver domain expertise - ✅ Communication protocol handles all message types - ✅ Context persists across sessions - ✅ Work orders track completed tasks ### Quality Requirements - ✅ Agent responses accurate to domain (> 95%) - ✅ Handoff protocol success rate > 98% - ✅ Zero message loss in communication - ✅ Context retrieval < 500ms - ✅ Custom agent definition < 5 min ### Performance Requirements - ✅ Agent routing decision < 100ms - ✅ Message delivery < 50ms - ✅ Database query < 200ms - ✅ Context summarization < 2s ### Integration Requirements - ✅ Works with existing docs-mcp tools - ✅ MCP protocol compliant - ✅ Backward compatible - ✅ Claude/Anthropic model support --- ## Out of Scope (Future Phases) ### Deferred Features - 🔮 Custom AI model training - 🔮 Multi-user real-time collaboration - 🔮 External PM tool integration (Jira, Asana) - 🔮 Autonomous code execution without approval - 🔮 Multi-project concurrent support - 🔮 Web UI/dashboard interface --- ## MCP Tools (Planned) | Tool | Purpose | Target Response | |------|---------|-----------------| | `invoke_agent` | Invoke specific agent | < 1s routing | | `agent_status` | Check agent availability | < 100ms | | `work_order_create` | Create task tracking | < 200ms | | `work_order_status` | Query work order | < 200ms | | `context_persist` | Save agent context | < 500ms | | `context_retrieve` | Load agent context | < 500ms | | `message_send` | Agent communication | < 50ms | | `message_history` | Retrieve messages | < 300ms | --- ## Implementation Timeline ### Phase 1: Foundation (2-3 weeks) - P1.1 (1w): Agent base classes and interfaces - P1.2 (1w): Project Leader & Assistant implementation - P1.3 (1w): Basic MCP integration ### Phase 2: Communication (1-2 weeks) - P2.1 (1w): Message protocol implementation - P2.2 (1w): Handoff workflow ### Phase 3: Specialists (3-4 weeks) - P3.1 (1w): Code-focused agents (3) - P3.2 (1w): UI/UX agents (3) - P3.3 (1w): Analysis agents (3) - P3.4 (1w): Integration agents (3) ### Phase 4: Persistence (1-2 weeks) - P4.1 (1w): Database schema & setup - P4.2 (1w): Work order system ### Phase 5: Integration (1-2 weeks) - P5.1 (1w): MCP tool exposure - P5.2 (1w): Slash commands ### Phase 6: Validation (2 weeks) - P6.1 (1w): Integration tests - P6.2 (1w): Documentation **Total: 10-16 weeks** --- ## Next Steps ### Immediate Actions 1. ✅ Context gathered (this document) 2. 📋 Execute `/analyze-for-planning` on docs-mcp project 3. 📋 Execute `/create-plan agent-personas` 4. 📋 Review and validate implementation plan 5. 📋 Begin Phase 1 implementation ### Required Decisions - Database technology (SQLite, PostgreSQL, MongoDB?) - Agent definition format (JSON, YAML, custom DSL?) - Context storage strategy (in-memory, persistent, hybrid?) - Security model (agent permissions, user authentication?) --- **Status: 📋 READY FOR PLANNING PHASE** Document Version: v1.0 Date: October 16, 2025 Review Status: Context Gathered - Ready for Analysis