Research Documentation AI Agent Navigation
Research Documentation - AI Agent Navigation
Audience: Contributors, AI Agents, Research Team Last Updated: December 22, 2025 Purpose: Quick navigation to CODITECT research findings
🚀 Quick Start
For AI Agents
Looking for specific research? Start here:
| Need | Document | Tokens | Use Case |
|---|---|---|---|
| All research topics | RESEARCH-INDEX.md | ~5000 | Overview of all research |
| Anthropic patterns | ANTHROPIC-RESEARCH-SUMMARY.md | ~8000 | Claude implementation patterns |
| Infrastructure | TECHNICAL-RESEARCH-SUMMARY.md | ~6000 | Performance, deployment, architecture |
| Market analysis | MARKET-RESEARCH-SUMMARY.md | ~4000 | Competition, pricing, positioning |
| Business case | business/README.md | ~2000 | ROI, metrics, financials |
Total Token Budget: ~25,000 tokens to load all summaries (vs 150,000+ for all raw research)
📂 Directory Structure
internal/research/
├── RESEARCH-INDEX.md ⭐ START HERE - Master catalog
├── ANTHROPIC-RESEARCH-SUMMARY.md ⭐ Consolidated Anthropic findings
├── TECHNICAL-RESEARCH-SUMMARY.md ⭐ Infrastructure & performance
├── MARKET-RESEARCH-SUMMARY.md ⭐ Competitive landscape
│
├── anthropic-research/ # Anthropic Claude research (48 docs)
│ ├── ANTHROPIC-REFERENCE-INDEX.md # 35 curated sources
│ ├── ANTHROPIC-AGENT-PATTERNS.md
│ ├── ANTHROPIC-TOOL-USE-PATTERNS.md
│ └── anthropic-updates/ # Latest research (40+ docs)
│
├── business/ # Business cases & metrics
│ ├── README.md # Quick reference
│ ├── ANTI-FORGETTING-EXECUTIVE-SUMMARY.md # 1-page summary
│ └── ANTI-FORGETTING-BUSINESS-CASE.md # Complete analysis
│
├── market-research/ # Competitive intelligence
│ ├── GENAI-CONTEXT-MEMORY-MARKET-RESEARCH.md
│ └── SAAS-FRAMEWORK-COMPARISON-2025.md
│
├── claude-code-automation/ # Automation research (10 docs)
├── llm-council-pattern/ # Multi-agent patterns (9 docs)
├── generative-ui/ # Generative UI research
├── performance/ # Performance optimization
├── submodule-management/ # Git submodule patterns
└── [Other specialized research]
🎯 Research by Use Case
When Implementing Claude Features
Read:
- ANTHROPIC-RESEARCH-SUMMARY.md - Consolidated patterns
- anthropic-research/ANTHROPIC-REFERENCE-INDEX.md - 35 sources
- anthropic-research/ANTHROPIC-AGENT-PATTERNS.md - Specific patterns
Key Topics:
- Multi-session continuity
- Agent skills architecture
- Tool use patterns
- Memory systems
When Optimizing Performance
Read:
- TECHNICAL-RESEARCH-SUMMARY.md - All optimizations
- PARALLEL-TASK-EXECUTION-ENHANCEMENT.md - Parallel strategies
- performance/PERFORMANCE-OPTIMIZATIONS-SUMMARY.md
Key Findings:
- Session deduplication: 93% size reduction
- Parallel execution: 60% faster
- JSONL streaming: 75% throughput increase
When Planning Infrastructure
Read:
- TECHNICAL-RESEARCH-SUMMARY.md - Infrastructure overview
- OPENTOFU-INFRASTRUCTURE-OPERATIONAL-ANALYSIS.md - GCP analysis
- MULTI-TENANT-CONTEXT-architecture.md - Multi-tenancy
Key Metrics:
- 50+ GCP resources managed
- 18 months zero-incident operation
- $2,400/year infrastructure costs
When Analyzing Competition
Read:
- MARKET-RESEARCH-SUMMARY.md - Complete landscape
- market-research/GENAI-CONTEXT-MEMORY-MARKET-RESEARCH.md
- market-research/SAAS-FRAMEWORK-COMPARISON-2025.md
Key Insights:
- $2.7B TAM
- 18-24 month technical lead
- Zero direct competitors in anti-forgetting
When Building Business Cases
Read:
- business/ANTI-FORGETTING-EXECUTIVE-SUMMARY.md - 1-page
- business/ANTI-FORGETTING-BUSINESS-CASE.md - Complete
- MARKET-RESEARCH-SUMMARY.md - Market context
Key Metrics:
- Year 1 Revenue: $1.79M
- LTV:CAC: 6.2x
- EBITDA: 25% → 35%
- ROI: 34% (Year 1), 1,067% (Year 3)
📚 Research Categories
1. Anthropic Research (48 documents)
Master Document: ANTHROPIC-RESEARCH-SUMMARY.md
Subcategories:
- Agent patterns (ANTHROPIC-AGENT-PATTERNS.md)
- Multi-session continuity (ANTHROPIC-MULTI-SESSION-PATTERN-RESEARCH.md)
- Tool use patterns (ANTHROPIC-TOOL-USE-PATTERNS.md)
- Prompt engineering (ANTHROPIC-PROMPT-ENGINEERING.md)
- Claude.md best practices (CLAUDE-MD-BEST-PRACTICES-RESEARCH.md)
Research Papers:
- A-MEM: Agentic Memory for LLM Agents
- Memp: Exploring Agent Procedural Memory
- Nested Learning: A New ML Paradigm for Continual Learning
External References: 35 sources indexed in ANTHROPIC-REFERENCE-INDEX.md
2. Technical Research (15 documents)
Master Document: TECHNICAL-RESEARCH-SUMMARY.md
Subcategories:
- Infrastructure (OPENTOFU-INFRASTRUCTURE-OPERATIONAL-ANALYSIS.md)
- Performance (PARALLEL-TASK-EXECUTION-ENHANCEMENT.md)
- Memory systems (CATASTROPHIC-FORGETTING-RESEARCH.md)
- Multi-tenancy (MULTI-TENANT-CONTEXT-architecture.md)
- Docker (internal/deployment/DOCKER-DEVELOPMENT-GUIDE.md)
Key Metrics:
- 50+ GCP resources managed
- 93% deduplication efficiency
- 60% faster parallel execution
- 10-minute Docker setup
3. Market Research (5 documents)
Master Document: MARKET-RESEARCH-SUMMARY.md
Subcategories:
- Market sizing (GENAI-CONTEXT-MEMORY-MARKET-RESEARCH.md)
- SaaS frameworks (SAAS-FRAMEWORK-COMPARISON-2025.md)
- Industry analysis (az1.ai-coditect-A16Z-response/)
Key Insights:
- TAM: $2.7B
- SAM: $759M
- SOM: $21.6M (Year 3)
- Category: Anti-forgetting memory systems
4. Business Cases (4 documents)
Directory: business/
Documents:
- Executive summary (1-page)
- Complete business case (30-minute read)
- Metrics dashboard
Key Metrics:
- Year 1: $1.79M revenue, 11,250 users
- Year 3: $33.21M revenue, 180,000 users
- LTV:CAC: 6.2x
- Payback: 10.6 months
5. Specialized Research
Agent Skills:
- AGENT-SKILLS-RESEARCH-INDEX.md
- AGENT-SKILLS-IMPLEMENTATION-PATTERNS.md
- MOE-STRATEGIC-ANALYSIS-AGENT-SKILLS-STANDARDS.md
Multi-Agent Patterns:
- llm-council-pattern/ - LLM council architecture (9 docs)
- llm-programmatic-control-research.md
Generative UI:
- generative-ui/ - Opus 4.5 research (15+ artifacts)
Academic:
- ACADEMIC-RESEARCH-REFERENCES-UQ-MOE-2024-2025.md
- UNCERTAINTY-QUANTIFICATION-MOE-FRAMEWORK.md
- GDPVal/ - GDP validation methodology (8 docs)
🔍 Research Workflow
For AI Agents
Step 1: Identify Research Need
Need: "How to implement multi-session continuity?"
Category: Anthropic Research
Document: ANTHROPIC-RESEARCH-SUMMARY.md
Section: "Multi-Session Pattern Research"
Step 2: Load Relevant Summary
- Load ANTHROPIC-RESEARCH-SUMMARY.md (~8000 tokens)
- Find relevant section
- Check if deeper detail needed
Step 3: Drill Down (If Needed)
- Load specific document (e.g., ANTHROPIC-MULTI-SESSION-PATTERN-RESEARCH.md)
- Extract patterns/code examples
- Apply to current task
Token Budget:
- Summary: ~8000 tokens
- Specific doc: ~5000-15000 tokens
- Total: ~10,000-25,000 tokens (vs 150,000+ for all research)
For Contributors
Adding New Research:
- Create document with frontmatter:
---
title: "Research Title"
audience: contributor
type: research
tokens: ~X000
summary: "One-line AI agent summary"
when_to_read: "When to reference this"
keywords: [keyword1, keyword2]
research_status: [active|completed|archived]
research_date: YYYY-MM-DD
---
-
Add to RESEARCH-INDEX.md
- Categorize appropriately
- Add to relevant summary document
- Update table of contents
-
Update summary document (if major finding)
- Add key finding to relevant summary
- Update metrics/statistics
- Add cross-references
-
Tag for discoverability
- Add keywords in frontmatter
- Reference in related docs
- Update when_to_read field
📊 Research Statistics
Current State (Dec 22, 2025):
Total Documents: 144 markdown files
Active Research: ~80 documents
Archive Target: 87% reduction (144 → 20)
Total Size: 25MB (with PDFs/images)
Categories:
- Anthropic: 48 docs
- Technical: 15 docs
- Market: 5 docs
- Business: 4 docs
- Academic: 8 docs
- Specialized: 64 docs
Master Summaries: 4 (25,000 tokens total)
- RESEARCH-INDEX.md: 5,000 tokens
- ANTHROPIC-RESEARCH-SUMMARY.md: 8,000 tokens
- TECHNICAL-RESEARCH-SUMMARY.md: 6,000 tokens
- MARKET-RESEARCH-SUMMARY.md: 4,000 tokens
Token Efficiency:
- Loading all raw research: ~150,000 tokens
- Loading summaries only: ~25,000 tokens
- Reduction: 83% fewer tokens
🔗 Related Documentation
Internal (Contributor)
- internal/architecture/ - ADRs, system design
- internal/deployment/ - Deployment guides
- internal/project/ - Plans, status, roadmaps
Customer Documentation
- docs/reference/ARCHITECTURE-OVERVIEW.md
- docs/guides/ - User guides
📝 Citation Format
When citing research in documentation:
**Research Source:** [Document Name](#)
**Category:** [Anthropic|Technical|Market|Business]
**Date:** YYYY-MM-DD
**Key Finding:** [One-sentence summary]
**Relevance:** [Why this matters for current work]
Example:
**Research Source:** [CATASTROPHIC-FORGETTING-RESEARCH.md](#)
**Category:** Technical
**Date:** 2025-12-11
**Key Finding:** Multi-tier memory architecture prevents 95%+ context loss
**Relevance:** Validates CODITECT's SQLite + Git approach vs external vector DBs
🔄 Maintenance
Update Frequency:
- RESEARCH-INDEX.md: Monthly or after major additions
- Summary documents: Quarterly
- Individual research: As findings emerge
Next Review: March 2026
Maintainer: CODITECT Research Team
Version: 1.0.0 Last Updated: December 22, 2025 Status: Active Compliance: CODITECT CLAUDE.md Standard v1.0.0