# Product Brief: TraceRTM

**Date:** 2025-11-20
**Author:** BMad
**Context:** Personal/Internal Tool - Enterprise-grade

---

## Executive Summary

TraceRTM is a multi-view requirements traceability and project management system designed to manage explosive complexity in rapidly evolving software projects. Born from the creator's personal frustration managing multiple complex projects that scale from 10 features to 500+, from 100 files to 10,000+, TraceRTM provides instant project state comprehension through 32 interconnected views spanning product strategy, UX design, technical architecture, implementation, quality, and operations.

Unlike traditional project management tools that force linear thinking or single-perspective views, TraceRTM enables seamless switching between perspectives (Feature → Code → Wireframe → Test → API) while maintaining bidirectional traceability and automatic progress tracking. The system is built for one primary user: the creator managing their own rapidly evolving personal projects, with potential future expansion to teams and enterprises.

---

## Core Vision

### Problem Statement

**The Personal Project Complexity Crisis**

As a developer managing multiple complex personal projects simultaneously, the fundamental challenge is **cognitive overload at scale**:

**Current Reality:**
- Projects start simple: 10 features, 100 files, 5 screens, 20 APIs
- Projects explode: 500 features, 10,000 files, 150 screens, 200 APIs
- Existing tools fail catastrophically at this transition

**Specific Pain Points:**

1. **Lost Context Switching**: Moving between feature planning, code implementation, UI design, and testing requires mental reconstruction each time. "What was I building? Why? What's the current state?"

2. **Invisible Relationships**: Can't see connections between a feature request, the code that implements it, the wireframe that designs it, and the tests that validate it. Everything lives in silos.

3. **Progress Blindness**: No way to grasp overall project health instantly. Is Feature X 30% done or 90% done? Which components are blocking others?

4. **Scope Chaos**: Rapid evolution means constant additions, cuts, merges. Existing tools assume stable scope - they break when you add 50 features in a day or cut 100 the next.

5. **Multi-Project Juggling**: Managing 5-10 active projects simultaneously. Each context switch costs 15-30 minutes of mental reconstruction.

### Problem Impact

**Time Cost:**
- 2-4 hours daily lost to context reconstruction
- 30-60 minutes per project switch
- Countless hours searching for "where did I define that feature/API/screen?"

**Cognitive Cost:**
- Mental exhaustion from holding entire project state in working memory
- Decision paralysis: "What should I work on next?"
- Fear of breaking things: "What will this change impact?"

**Opportunity Cost:**
- Projects stall at ~200-300 features due to management overhead
- Can't scale personal projects to production-grade complexity
- Ideas abandoned because "too complex to track"

**Emotional Cost:**
- Frustration with tools that don't match thinking patterns
- Anxiety about losing track of critical details
- Reduced joy in building when management becomes the bottleneck

### Why Existing Solutions Fall Short

**Traditional Project Management (Jira, Linear, Asana):**
- ❌ Feature-centric only - no code/wireframe/test views
- ❌ Assumes stable scope - breaks with rapid changes
- ❌ Heavy, slow, designed for teams not individuals
- ❌ No technical artifact tracking (code, APIs, database schemas)

**Requirements Management (Jama, Polarion, Doors):**
- ❌ Enterprise bloat - weeks to set up
- ❌ Compliance-focused, not development-focused
- ❌ Expensive ($1000s/year)
- ❌ Terrible UX for rapid iteration

**Developer Tools (GitHub Projects, GitLab Issues):**
- ❌ Code-centric only - no product/UX views
- ❌ Flat structure - poor hierarchy support
- ❌ No cross-artifact linking (feature ↔ wireframe ↔ test)

**Note-Taking/PKM (Notion, Obsidian):**
- ❌ Manual linking - no automatic relationship tracking
- ❌ No progress calculation
- ❌ Not designed for software artifacts
- ❌ Becomes a mess at scale

**The Gap:** No tool exists that:
1. Provides multiple interconnected views of the same project
2. Handles explosive scope changes gracefully
3. Tracks technical artifacts (code, APIs, schemas) alongside product artifacts
4. Optimized for solo developers managing complex projects
5. Fast, lightweight, CLI-first for developer workflow

### Proposed Solution

**TraceRTM: Multi-View Project State Management**

TraceRTM treats every project as a **multi-dimensional state space** that can be viewed, navigated, and manipulated from any perspective:

**Core Concept:**
- Everything is an **item** (feature, file, screen, API, test, table)
- Every item exists in multiple **views** simultaneously
- Items are **linked** across views automatically and manually
- Progress **propagates** through hierarchies automatically
- State is **queryable** from any angle instantly

**The Experience:**

```
You're working on "User Authentication"

Feature View:     Epic: Auth System → Story: Login → Task: OAuth
Code View:        Module: auth → File: oauth.py → Class: OAuthHandler
Wireframe View:   Screen: Login → Component: OAuth Button
API View:         Service: Auth → Endpoint: POST /oauth/token
Test View:        Suite: Auth → Case: test_oauth_flow
Database View:    Schema: auth → Table: oauth_tokens

All connected. All visible. All manageable.
```

**Key Capabilities:**

1. **Instant Context**: Open any view, see entire project state in that dimension
2. **Seamless Switching**: Jump from feature to code to wireframe in one command
3. **Automatic Linking**: Code commits auto-link to stories, tests auto-link to features
4. **Progress Tracking**: Feature shows 60% done because 3/5 child tasks complete
5. **Chaos Handling**: Add 100 features? System adapts. Cut 50? Clean up automatically.
6. **CLI-First**: Fast keyboard-driven workflow for developers
7. **Agent-Native**: Designed for 1-1000 AI agents working simultaneously across projects

### Key Differentiators

**1. Agent-First Architecture**
- **Not just human-friendly, but agent-optimized**: While traditional tools assume human users, TraceRTM is designed for a **human + massive AI agent workforce** (1-1000 agents simultaneously)
- **Structured state for AI consumption**: Every view is queryable, every relationship is explicit, every change is traceable - perfect for AI agents that need clear context
- **Concurrent multi-agent operations**: Handles 100+ agents working on different parts of the same project without conflicts
- **Shared context across agent swarms**: Agents working on different projects can share the same local filesystem/worktree and maintain coherent state

**2. Multi-View Intelligence**
- **32 interconnected views** vs. single-perspective tools (Jira: features only, GitHub: code only)
- **Automatic cross-view linking**: Feature → Code → Wireframe → Test → API → Database
- **Bidirectional traceability**: Navigate up (code → feature) or down (feature → code) instantly

**3. Chaos Engineering for Scope**
- **Built for explosive change**: Add/cut/merge 100s of items without breaking
- **Zombie detection**: Automatically find orphaned/dead items
- **Temporal snapshots**: Rewind to any point in project history
- **Impact visualization**: See what breaks when you change anything

**4. Developer-Native UX**
- **CLI-first**: Keyboard-driven, scriptable, automatable
- **Fast**: Sub-second queries on 10,000+ item projects
- **Lightweight**: No enterprise bloat, no setup overhead
- **Local-first**: Works offline, no cloud dependency

**5. Three-Mode Flexibility**
- **Multi-View Mode (60%)**: Standard software development
- **Chaos Mode (30%)**: Fast-moving projects with explosive scope changes
- **Compliance Mode (10%)**: Regulated industries (aerospace, automotive, medical)

---

## Target Users

### Primary User: AI-Augmented Solo Developer (You!)

**Profile:**
- **Role**: Solo developer managing 5-10 complex personal projects simultaneously
- **Scale**: Projects ranging from 100-500 features, 1,000-10,000 files each
- **Workflow**: Orchestrates 1-1000 AI agents across multiple projects
- **Agent Distribution**:
  - Some agents work on single projects
  - Some agents work across multiple projects
  - Agents may share local filesystem/worktree or work independently
  - Need coherent state management across all agent activities

**Current Behavior:**
- Rapidly iterates on multiple projects (add 50 features today, cut 100 tomorrow)
- Context switches between projects 10-20 times daily
- Uses AI agents for implementation, testing, documentation, refactoring
- Needs to understand project state instantly to direct agent work
- Struggles with existing tools that assume human-only, stable-scope workflows

**Specific Frustrations:**
1. **Agent Coordination Chaos**: "I have 50 agents working on this project - which ones are blocked? Which features are they implementing? What's the current state?"
2. **Multi-Project Context Loss**: "I'm switching from Project A (authentication) to Project B (payment system) - what was the state? What's next?"
3. **Scope Explosion Management**: "I just had 10 new ideas and need to add them NOW, but my tool makes this painful"
4. **Cross-View Blindness**: "I know the feature exists, but where's the code? Where's the wireframe? Where's the test?"
5. **Progress Opacity**: "Is this project 30% done or 90% done? I can't tell!"

**What They Value Most:**
- **Speed**: Sub-second responses, keyboard-driven, no clicking
- **Clarity**: Instant project state comprehension from any angle
- **Flexibility**: Handles chaos, rapid changes, explosive growth
- **Agent-Friendly**: Structured data AI agents can consume and update
- **Multi-Project**: Seamless switching between 5-10 active projects

**Technical Comfort Level:**
- Expert developer
- Comfortable with CLI, Python, SQL, APIs
- Prefers code/config over GUI
- Values automation and scriptability

### Secondary User: AI Agent Swarms

**Profile:**
- **Count**: 1-1000 agents per user
- **Types**: Implementation agents, testing agents, documentation agents, refactoring agents, analysis agents
- **Deployment**: Local execution, shared filesystem access, concurrent operations
- **Intelligence**: LLM-powered (GPT-4, Claude, etc.)

**Agent Needs:**
1. **Structured Context**: "What feature am I implementing? What's the acceptance criteria? What code already exists?"
2. **Clear State**: "What's the current status? What's blocked? What's next?"
3. **Explicit Relationships**: "Which tests validate this feature? Which APIs does this screen call?"
4. **Concurrent Safety**: "Can I update this item while 50 other agents are working?"
5. **Queryable State**: "Show me all incomplete features assigned to me"

**Agent Workflows:**
- Query project state via CLI/API
- Update item status (feature → in_progress → complete)
- Create new items (code files, tests, documentation)
- Link items across views (code → feature, test → code)
- Report progress and blockers

**Why This Matters:**
- Traditional tools assume human users with GUIs
- AI agents need **structured, queryable, programmatic access**
- TraceRTM provides **agent-native interfaces** (CLI, Python API, JSON/YAML export)
- Enables **human-agent collaboration** at massive scale

### User Journey: Typical Day with TraceRTM

**Morning: Project Planning (Human-Driven)**
1. Open TraceRTM CLI
2. Switch to Project A: `rtm project switch auth-system`
3. View feature roadmap: `rtm view roadmap`
4. Add 20 new feature ideas from overnight brainstorm: `rtm batch-add features.yaml`
5. Prioritize top 5 for today: `rtm prioritize epic-auth --top 5`
6. Assign features to agent teams: `rtm assign epic-auth --agents team-alpha`

**Mid-Morning: Agent Orchestration**
7. Launch 50 agents to implement top 5 features
8. Agents query their assignments: `rtm query --assigned-to agent-12 --status todo`
9. Agents create code files, link to features: `rtm link code/oauth.py feature-auth-oauth`
10. Monitor agent progress: `rtm view progress --live`

**Afternoon: Context Switch to Project B**
11. Switch projects: `rtm project switch payment-system`
12. Check what changed since yesterday: `rtm diff --since yesterday`
13. Review agent-created code: `rtm view code --filter created-by:agents`
14. Spot issue, reassign to different agent team: `rtm reassign feature-payment-stripe --agents team-beta`

**Evening: Multi-Project Review**
15. View all projects dashboard: `rtm dashboard --all-projects`
16. See Project A: 60% complete (30/50 features done)
17. See Project B: 40% complete (blockers identified)
18. Export status for tomorrow: `rtm export --format markdown > status.md`

**Key Moments:**
- **Instant Context**: Every project switch takes <1 second
- **Agent Coordination**: Clear visibility into 50-1000 agent activities
- **Chaos Handling**: Added 20 features in minutes, system adapted
- **Multi-View Navigation**: Jumped between features, code, progress views seamlessly
- **Progress Clarity**: Always know exactly where each project stands


---

## Success Metrics

### Personal Success Indicators

**Primary Goal: Reduce Cognitive Load**
- **Time to Context Switch**: <10 seconds (currently 15-30 minutes)
- **Daily Context Reconstruction Time**: <30 minutes (currently 2-4 hours)
- **Projects Manageable Simultaneously**: 10+ (currently 3-5)

**Secondary Goal: Scale Project Complexity**
- **Max Features Per Project**: 1000+ (currently stalls at 200-300)
- **Max Files Tracked**: 50,000+ (currently loses track at 5,000)
- **Query Response Time**: <1 second for 10,000+ items

**Tertiary Goal: Agent Productivity**
- **Agents Coordinated Simultaneously**: 1000+ without conflicts
- **Agent Context Query Time**: <100ms
- **Agent Task Completion Rate**: 80%+ (clear context = better results)

### Usage Metrics (If Shared Publicly)

**Adoption:**
- 100 developers using TraceRTM for personal projects (Year 1)
- 10 teams adopting for collaborative work (Year 2)

**Engagement:**
- Daily active usage (tool becomes indispensable)
- Average 50+ CLI commands per day per user
- 5+ projects tracked per user

**Retention:**
- 90%+ retention after 30 days (tool becomes essential)
- Users report 50%+ time savings on project management

### Business Objectives

**Phase 1: Personal Tool (Current)**
- Build for personal use
- Validate core multi-view concept
- Prove agent coordination at scale
- No revenue goal - pure utility

**Phase 2: Open Source (Future)**
- Release as open-source tool
- Build community of AI-augmented developers
- Establish TraceRTM as standard for agent-driven development

**Phase 3: Enterprise (Optional)**
- Offer enterprise version with compliance features
- Target regulated industries (aerospace, automotive, medical)
- Revenue potential: $50-200/user/month for compliance mode

---

## MVP Scope

### Core Features (Must-Have for Launch)

**1. Multi-View System (8 Essential Views)**
- **FEATURE**: Epics → Features → Stories → Tasks
- **CODE**: Modules → Files → Classes → Functions
- **WIREFRAME**: Screens → Components → Elements
- **API**: Services → Endpoints → Parameters
- **TEST**: Suites → Cases → Assertions
- **DATABASE**: Schemas → Tables → Columns
- **ROADMAP**: Timeline-based feature planning
- **PROGRESS**: Real-time completion tracking

**2. Item Management**
- Create, read, update, delete items in any view
- Hierarchical decomposition (epic → feature → story → task)
- Status tracking (todo, in_progress, blocked, complete, cancelled)
- Automatic progress calculation (parent = avg of children)
- Bulk operations (batch add/update/delete)

**3. Cross-View Linking**
- Manual linking: `rtm link feature-1 code-auth.py implements`
- Automatic linking: Code commits → Stories (via commit messages)
- Bidirectional navigation: Feature → Code, Code → Feature
- Link types: implements, tests, designs, depends_on, blocks

**4. CLI Interface**
- Fast keyboard-driven commands
- Project switching: `rtm project switch <name>`
- View navigation: `rtm view <view-name>`
- Item CRUD: `rtm create/show/update/delete`
- Querying: `rtm query --filter <criteria>`
- Export: `rtm export --format json/yaml/markdown`

**5. Agent-Native API**
- Python API for programmatic access
- JSON/YAML import/export for agent consumption
- Structured query language for agent queries
- Concurrent operation safety (locking, conflict detection)
- Agent activity logging

**6. Multi-Project Support**
- Switch between projects instantly
- Shared agent pool across projects
- Cross-project queries: `rtm query --all-projects --filter status:blocked`
- Project dashboard: `rtm dashboard --all-projects`

**7. Basic Versioning**
- Track item changes over time
- View history: `rtm history <item-id>`
- Temporal queries: `rtm show <item-id> --at 2025-11-15`
- Rollback capability

**8. Search & Filter**
- Full-text search across all items
- Filter by status, type, owner, date
- Saved queries for common patterns
- Fuzzy matching for fast lookup

### Out of Scope for MVP (Phase 2+)

**Deferred to Phase 2:**
- ❌ TUI (Textual) interface - CLI only for MVP
- ❌ Web UI - Command-line first
- ❌ Real-time collaboration - Single user for MVP
- ❌ Advanced graph algorithms (PageRank, community detection)
- ❌ Neo4j integration - PostgreSQL only for MVP
- ❌ TimescaleDB for time-series - Basic PostgreSQL
- ❌ Advanced chaos mode features (zombie detection, conflict resolution)
- ❌ Compliance mode features (electronic signatures, audit trails)
- ❌ Smart contracts for immutable traceability
- ❌ MCP (Model Context Protocol) integration
- ❌ Multi-language support - Python only
- ❌ Cloud sync - Local-first only
- ❌ Mobile apps
- ❌ Integrations (Jira, GitHub, Slack)

**Future Vision (Phase 3+):**
- ❌ AI-powered insights ("This feature is likely to cause delays")
- ❌ Predictive analytics (completion date estimation)
- ❌ Automated refactoring suggestions
- ❌ Visual graph editor for relationships
- ❌ Natural language queries ("Show me blocked features in auth system")
- ❌ Agent performance analytics
- ❌ Multi-tenant SaaS offering

### MVP Success Criteria

**Functional:**
- ✅ Can manage 3 projects with 500+ features each
- ✅ Can coordinate 100+ agents simultaneously
- ✅ Sub-second query response for 10,000+ items
- ✅ Zero data loss during concurrent operations
- ✅ Context switch between projects in <10 seconds

**User Experience:**
- ✅ Daily usage for all personal projects
- ✅ Replaces existing tools (Notion, GitHub Projects, spreadsheets)
- ✅ Reduces context reconstruction time by 80%+
- ✅ Enables managing 10+ projects simultaneously

**Technical:**
- ✅ PostgreSQL backend with proper indexing
- ✅ Python 3.12+ with Pydantic models
- ✅ Typer-based CLI with rich output
- ✅ Comprehensive test coverage (80%+)
- ✅ Documentation for all CLI commands

---

## Technical Preferences

### Technology Stack (Validated from Research)

**Backend:**
- **Language**: Python 3.12+ (chosen for rapid development, rich ecosystem, agent-friendly)
- **Database**: PostgreSQL 16+ (relational integrity, JSONB for flexibility, proven at scale)
- **ORM**: SQLAlchemy 2.0+ (type-safe, async support, migration management)
- **Validation**: Pydantic v2 (data validation, serialization, agent-friendly schemas)

**CLI:**
- **Framework**: Typer (modern, type-safe, auto-documentation)
- **Output**: Rich (beautiful terminal output, tables, progress bars)
- **Config**: YAML/TOML (human and agent readable)

**Future Additions (Phase 2+):**
- **Graph Database**: Neo4j (for advanced relationship queries)
- **Time-Series**: TimescaleDB (for metrics and history)
- **Cache**: Redis (for performance optimization)
- **Message Queue**: NATS (for agent coordination)

### Platform & Deployment

**MVP:**
- **Platform**: macOS/Linux (developer-focused)
- **Deployment**: Local installation via pip
- **Storage**: Local filesystem + PostgreSQL
- **Distribution**: Python package (PyPI)

**Future:**
- Windows support
- Docker containers
- Cloud-hosted option (optional)

### Performance Requirements

**Response Times:**
- Item CRUD: <50ms
- Simple queries: <100ms
- Complex queries (10,000+ items): <1 second
- View rendering: <200ms
- Project switch: <500ms

**Scalability:**
- 10,000+ items per project
- 100+ projects per user
- 1,000+ concurrent agent operations
- 100,000+ total items across all projects

**Reliability:**
- 99.9% uptime (local tool)
- Zero data loss
- Automatic backups
- Crash recovery

---

## Risks and Assumptions

### Key Assumptions

**User Assumptions:**
1. ✅ **Validated**: Personal need is real (creator experiences the pain daily)
2. ⚠️ **To Validate**: Other AI-augmented developers have similar needs
3. ⚠️ **To Validate**: Multi-view approach is superior to single-view tools
4. ⚠️ **To Validate**: CLI-first is acceptable (vs GUI)

**Technical Assumptions:**
1. ✅ **Validated**: PostgreSQL can handle 10,000+ items with sub-second queries
2. ✅ **Validated**: Python 3.12 performance is sufficient
3. ⚠️ **To Validate**: Concurrent agent operations won't cause conflicts
4. ⚠️ **To Validate**: Local-first architecture scales to 100+ projects

**Market Assumptions:**
1. ⚠️ **To Validate**: AI-augmented development is growing trend
2. ⚠️ **To Validate**: Developers will adopt CLI tools over web UIs
3. ⚠️ **To Validate**: Open-source model can build community

### Key Risks

**Technical Risks:**

1. **Concurrent Agent Conflicts** (HIGH)
   - **Risk**: 100+ agents updating same items causes data corruption
   - **Mitigation**: Implement optimistic locking, conflict detection, transaction isolation
   - **Fallback**: Agent queuing system if conflicts are frequent

2. **Performance Degradation at Scale** (MEDIUM)
   - **Risk**: Queries slow down with 50,000+ items
   - **Mitigation**: Proper indexing, query optimization, caching layer
   - **Fallback**: Add Redis cache, consider sharding

3. **Data Model Complexity** (MEDIUM)
   - **Risk**: 32 views create schema complexity and maintenance burden
   - **Mitigation**: Start with 8 core views, add incrementally
   - **Fallback**: Simplify to fewer views if complexity is unmanageable

**User Adoption Risks:**

4. **Learning Curve** (MEDIUM)
   - **Risk**: CLI complexity intimidates users
   - **Mitigation**: Excellent documentation, interactive tutorials, sensible defaults
   - **Fallback**: Add TUI (Textual) for visual learners

5. **Tool Switching Friction** (LOW)
   - **Risk**: Users won't migrate from existing tools
   - **Mitigation**: Import from Jira/GitHub/Notion, gradual adoption path
   - **Fallback**: Build integrations to work alongside existing tools

**Scope Risks:**

6. **Feature Creep** (HIGH)
   - **Risk**: 32 views + chaos mode + compliance mode is too ambitious for MVP
   - **Mitigation**: Ruthless prioritization - 8 views only for MVP
   - **Fallback**: Ship minimal viable version, iterate based on usage

### Open Questions

**Product Questions:**
1. Should MVP include TUI or stay CLI-only?
2. Which 8 views are truly essential vs nice-to-have?
3. Is agent coordination a core feature or Phase 2?
4. Should we support Windows from day 1?

**Technical Questions:**
1. How to handle schema migrations as views evolve?
2. What's the optimal locking strategy for concurrent agents?
3. Should we use event sourcing for full history?
4. How to handle cross-project queries efficiently?

**Go-to-Market Questions:**
1. Open source from day 1 or private beta first?
2. Target solo developers or teams?
3. Free forever or freemium model?
4. Build community before launch or after?

---

## Supporting Materials

### Research Documents Incorporated

This Product Brief synthesizes insights from extensive research documentation:

**Architecture & Design:**
- COMPREHENSIVE_RESEARCH_COMPLETE.md (250,000+ words)
- RTM_FINAL_ARCHITECTURE_SUMMARY.md (Three-mode architecture)
- COMPLETE_VIEW_TAXONOMY.md (32-view system design)
- ATOMIC_TRACE_ARCHITECTURE.md (Atomic decomposition model)
- INTERNAL_VIEW_ARCHITECTURES.md (Deep view internals)

**Technical Stack:**
- OPTIMAL_INFRASTRUCTURE_ARCHITECTURE.md (11-component justified stack)
- TECH_STACK_VALIDATION.md (Python 3.12 justification)
- NATS_TECHNICAL_ARCHITECTURE.md (Event sourcing design)

**Implementation:**
- REQUIREMENTS_TRACEABILITY_MVP.md (MVP scope definition)
- ATOMIC_IMPLEMENTATION_ROADMAP.md (20-week plan)
- ATOMIC_EXAMPLES.md (10 complete examples)

**Domain Research:**
- REQUIREMENTS_TRACEABILITY_RESEARCH.md (Industry analysis)
- RTM_DEEP_DIVE_GRAPH_DATABASES.md (Neo4j evaluation)
- RTM_DEEP_DIVE_REGULATORY_COMPLIANCE.md (Compliance requirements)
- RTM_CHAOS_ENGINEERING_SCOPE_MANAGEMENT.md (Chaos mode design)

### Key Insights from Research

1. **Multi-View is Essential**: Single-perspective tools fail at scale
2. **Agent Coordination is Critical**: AI-augmented development is the future
3. **Chaos Mode is Differentiator**: Handling explosive scope changes is unique value
4. **PostgreSQL is Sufficient**: No need for Neo4j in MVP
5. **CLI-First is Right**: Developer workflow demands keyboard-driven speed
6. **8 Views for MVP**: Feature, Code, Wireframe, API, Test, Database, Roadmap, Progress
7. **Local-First**: No cloud dependency for MVP

---

_This Product Brief captures the vision for TraceRTM: a multi-view requirements traceability system designed for AI-augmented solo developers managing multiple complex, rapidly evolving projects._

_It was created through analysis of 250,000+ words of research and reflects the authentic need of the creator managing 5-10 projects with 1-1000 AI agents simultaneously._

_Next: The PRD workflow will transform this brief into detailed product requirements with specific acceptance criteria, user stories, and technical specifications._


