YAML Frontmatter (Required for CODITECT Standards v1.0)

CODITECT Core - Comprehensive Project Plan

Product: AZ1.AI CODITECT - Distributed Intelligence Framework Repository: CODITECT-core (Primary Product / CODITECT Brain) Owner: AZ1.AI INC. Author: Hal Casteel, Founder/CEO/CTO Version: 1.0.0 Status: Operational Foundation → Evolution to Full Autonomy Last Updated: December 4, 2025

Executive Summary

CODITECT Core is the foundational distributed intelligence framework that serves as the "brain" for the entire AZ1.AI CODITECT platform. This repository contains the complete agent orchestration system, command framework, skills library, and training materials that enable autonomous AI-powered project development from concept to production.

Product Classification

This is AZ1.AI's PRIMARY PRODUCT - the CODITECT brain that powers:

Local-first installation (no cloud dependency)
Optional cloud sync and collaboration
Distributed intelligence architecture via .coditect symlink chain
Complete project lifecycle automation
Zero catastrophic forgetting via MEMORY-CONTEXT system
Comprehensive operator training (4-6 hour certification)

Current Status

Operational Foundation (v1.0+): ✅ EXPANDED Dec 4, 2025

✅ all specialized AI agents across 8 domains (⭐ +2 Nov 22: project-discovery-specialist, project-structure-optimizer)
✅ all slash commands for autonomous workflows (⭐ +4 Nov 22: /new-project, /analyze-hooks, /web-search-hooks, /generate-project-plan-hooks)
✅ all production skills with transferable capabilities (⭐ +2 Nov 21: submodule management skills)
✅ MEMORY-CONTEXT system (7,507+ messages preserved, +148 unique Nov 22)
✅ Comprehensive training system (55,000+ words)
✅ Distributed intelligence architecture (symlink chain)
✅ Local-first installer (GUI + CLI + Bash)
✅ Multi-LLM compatibility (Claude, GPT, Gemini, Cursor, Cody)
✅ Claude Code Hooks Framework (4000+ line analysis + 3 implementation commands)
✅ Project Creation Workflow (/new-project - complete project initialization automation)
✅ Production Documentation Standards (⭐ +8 files upgraded Dec 4: 86% Grade A quality, +2,043 lines)

Framework Maturity

Core Framework: 78% complete (7/9 orchestration modules operational)
Agent System: Production-ready with 52 specialized agents (⭐ +2 new Nov 22)
Command System: all commands operational and documented (⭐ +4 new Nov 22)
Skills Library: all skills with 254+ reusable assets catalogued (⭐ +2 new Nov 21)
Training System: Complete 4-6 hour certification path (55,000+ words)
Installation: Cross-platform automated installers working
Hooks Framework: 4000+ lines of analysis and 3 production-ready commands (NEW Nov 22)
Documentation Quality: 86% Grade A standards compliance (⭐ +8 files upgraded Dec 4)
Total Components: 177 (all agents + all commands + all skills + all scripts)

Critical Gap for Full Autonomy

❌ Inter-agent communication (agents cannot send tasks to each other)
❌ Message Bus infrastructure (RabbitMQ)
❌ Agent Discovery Service (Redis-based registry)
❌ Task Queue Manager (persistent queue with dependencies)
❌ Circuit Breaker and resilience patterns
❌ Comprehensive test coverage (currently <10%)
❌ Production monitoring and observability

Strategic Positioning

Commercial Product Strategy

Local-First Open Core - Free local installation, commercial cloud features
Training & Certification - Revenue from operator training programs
Enterprise Features - Team collaboration, advanced analytics, priority support
Platform Services - Cloud sync, marketplace, analytics dashboard

Market Differentiation

Only distributed intelligence framework for project development
Zero vendor lock-in - works locally without cloud dependency
MEMORY-CONTEXT system eliminates catastrophic forgetting
Complete training system - 4-6 hour certification vs. competitors' weeks
Multi-LLM compatibility - not locked to single AI provider

Architecture Overview
Current Operational Status
Implementation Roadmap
Hooks Implementation Framework
CODITECT Next-Generation (Autonomous Platform)
Multi-Agent Orchestration Strategy
Quality Gates & Testing
Success Metrics
Budget & Resource Requirements
Risks & Mitigation
Dependencies & Integration
Long-Term Evolution

Architecture Overview

Distributed Intelligence Architecture

Core Concept: .coditect symlink chain enables intelligence at every project node

Master Repository (coditect-rollout-master)
├── .coditect/ (git submodule → coditect-core)
│   ├── agents/          # All specialized AI agents
│   ├── commands/        # All slash commands
│   ├── skills/          # all production skills
│   ├── scripts/         # 21 Python automation scripts
│   ├── user-training/   # 55K+ words training materials
│   ├── MEMORY-CONTEXT/  # Session preservation system
│   └── docs/            # 25 architectural documents
│
├── .claude -> .coditect (symlink for Claude Code compatibility)
│
└── submodules/
    ├── coditect-cloud-backend/
    │   ├── .coditect -> ../../.coditect
    │   └── .claude -> .coditect
    ├── coditect-cloud-frontend/
    │   ├── .coditect -> ../../.coditect
    │   └── .claude -> .coditect
    └── (42 total submodules with distributed intelligence)

Why This Matters

Every submodule has access to full CODITECT intelligence
Run Claude Code from any directory → same all agents, all commands, 24 skills
Single source of truth (git submodule updates propagate automatically)
Resilience: damage to one node doesn't disable system
Scalability: add nodes without redesigning architecture

System Components

1. Agent Framework (all agents)

Business Intelligence (6 agents)

venture-capital-business-analyst
competitive-market-analyst
business-intelligence-analyst
software-design-architect
software-design-document-specialist
ai-curriculum-specialist

Technical Development (see config/component-counts.json)

rust-expert-developer
actix-web-specialist
foundationdb-expert
frontend-react-TypeScript-expert
database-architect
multi-tenant-architect
websocket-protocol-designer
wasm-optimization-expert
terminal-integration-specialist
testing-specialist
security-specialist
monitoring-specialist
devops-engineer
cloud-architect
k8s-statefulset-specialist
codi-* specialists (QA, DevOps, Test, Documentation)

Research & Analysis (8 agents)

codebase-analyzer
codebase-locator
codebase-pattern-finder
thoughts-analyzer
thoughts-locator
web-search-researcher
research-agent
prompt-analyzer-specialist

Quality Assurance (6 agents)

rust-qa-specialist
codi-qa-specialist
codi-test-engineer
qa-reviewer
adr-compliance-specialist
CODITECT-adr-specialist

Project Management (5 agents)

orchestrator (multi-agent coordination)
project-organizer
skill-quality-enhancer
novelty-detection-specialist
script-utility-analyzer

Content & Documentation (4 agents)

educational-content-generator
assessment-creation-agent
codi-documentation-writer
senior-architect

Code Review (3 agents)

orchestrator-code-review
cloud-architect-code-reviewer
ai-specialist

2. Command System (all commands)

Research & Discovery (see config/component-counts.json)

/research - Web research and analysis
/research-codebase - Codebase analysis
/research-codebase_generic - Generic codebase research
/research-codebase_nt - Non-terminal codebase research
/smart-research - Intelligent research workflows
/multi-agent-research - Coordinated research
/ralph-research - RALPH research workflows
/analyze - Deep analysis
/complexity-gauge - Complexity assessment
/deliberation - Decision analysis
/agent-dispatcher - Agent selection
/COMMAND-GUIDE - Command decision trees

Planning & Strategy (8 commands)

/create-plan - Project plan generation
/validate-plan - Plan validation
/implement-plan - Plan execution
/ralph-plan - RALPH planning
/founder-mode - Strategic planning
/strategy - Strategic analysis
/oneshot - Single-command execution
/generate-project-plan - Automated project planning

Development & Implementation (see config/component-counts.json)

/implement - Feature implementation
/feature-development - Feature workflows
/prototype - Rapid prototyping
/rust-scaffold - Rust project scaffolding
/component-scaffold - Component generation
/typescript-scaffold - TypeScript scaffolding
/code-explain - Code documentation
/document - Documentation generation
/db-migrations - Database migrations
/create-worktree - Git worktree management
/tdd-cycle - TDD workflows
/debug - Debugging assistance
/smart-debug - Intelligent debugging
/error-analysis - Error analysis
/error-trace - Distributed tracing

Quality Assurance (9 commands)

/ai-review - AI code review
/local-review - Local review
/full-review - Comprehensive review
/security-sast - SAST scanning
/security-deps - Dependency scanning
/security-hardening - Security hardening
/test-generate - Test generation
/optimize - Performance optimization
/tech-debt - Technical debt analysis

Deployment & Operations (see config/component-counts.json)

/config-validate - Configuration validation
/monitor-setup - Monitoring setup
/slo-implement - SLO implementation
/incident-response - Incident management
/commit - CI commit workflows
/commit - Git commit automation
/describe-pr - CI PR descriptions
/describe-pr - PR documentation
/pr-enhance - PR enhancement
/export-dedup - Session export with deduplication

Context Management (4 commands)

/context-save - Context preservation
/context-restore - Context restoration
/create-handoff - Handoff documentation
/resume-handoff - Handoff resumption

Educational (3 commands)

/generate-curriculum-content - Curriculum automation
/optimize-work-reuse - Work reuse optimization
/notebooklm-optimize - NotebookLM formatting

Utility (see config/component-counts.json)

/refactor-clean - Code refactoring
/suggest-agent - Agent suggestions
/skill-enhance - Skill improvement
/evaluation-framework - Evaluation setup
/google-cloud-build - GCP Cloud Build
/gcp-cleanup - GCP resource cleanup
/foundationdb-queries - FDB query optimization
/communication-protocols - Protocol design
/internal-comms - Internal communication
/cross-file-update - Cross-file updates
/deployment-archeology - Deployment analysis
/build-deploy - Build and deploy automation
/coditect-router - AI command router (NEW!)

3. Skills Library (all production skills)

Development Skills

code-editor (7 variations)
code-analysis-planning-editor (5 variations)
framework-patterns (5 variations)
rust-backend-patterns (3 variations)
production-patterns (5 variations)

Project Management Skills

ai-curriculum-development (5 variations)
multi-agent-workflow (4 variations)
evaluation-framework (5 variations)

Infrastructure Skills

build-deploy-workflow (4 variations)
google-cloud-build (3 variations)
foundationdb-queries (3 variations)
gcp-resource-cleanup (4 variations)
deployment-archeology (3 variations)

Communication Skills

communication-protocols (5 variations)
internal-comms (5 variations)
document-skills (6 variations)
cross-file-documentation-update (4 variations)

Optimization Skills

notebooklm-content-optimization (3 variations)
search-strategies (5 variations)
token-cost-tracking (4 variations)

Quality Skills

git-workflow-automation (4 variations)

Total Skill Variations: 24 skill families with 254+ catalogued reusable assets

4. MEMORY-CONTEXT System

Purpose: Eliminate catastrophic forgetting across sessions

Architecture

MEMORY-CONTEXT/
├── checkpoints/          # Automated checkpoint creation
├── sessions/             # Session exports with deduplication
├── dedup_state/          # Deduplication tracking
│   ├── checkpoint_index.json
│   ├── global_hashes.json
│   └── unique_messages.jsonl
└── exports-archive/      # Historical session exports

Features

Automated session export on command completion
Content deduplication (80%+ reduction via hashing)
Checkpoint creation with git integration
Session continuity across multiple sessions
Privacy-controlled sharing model
Token optimization through reuse detection

Performance

6,400+ messages preserved
80%+ deduplication rate
<100ms export time
Automated cascade push to master repository

Integration

Export-dedup command: /export-dedup
Checkpoint creation: python scripts/create-checkpoint.py
Privacy controls: scripts/privacy_manager.py
Session analysis: NESTED LEARNING processor

5. Training System

Comprehensive Operator Training (55,000+ words)

Quick Start Path (30 minutes)

1-2-3-ONBOARDING-HOWTO-QUICK-GUIDE.md
Environment setup
First agent invocation
Basic command usage

Comprehensive Certification (4-6 hours)

Module 1: Foundations (1 hour)
- CODITECT-OPERATOR-TRAINING-SYSTEM.md
- Architecture understanding
- Environment setup
- Tool mastery
Module 2: Business Discovery (1 hour)
- Market research
- Value proposition
- Product-market fit
- GTM strategy
Module 3: Technical Specification (1.5 hours)
- C4 architecture diagrams
- Database design
- API specifications
- ADRs (Architecture Decision Records)
Module 4: Project Management (1 hour)
- PROJECT-PLAN creation
- TASKLIST management
- Checkpoint system
- Progress tracking
Module 5: Advanced Operations (1.5 hours)
- MEMORY-CONTEXT system
- Multi-session continuity
- Agent orchestration
- Command workflows

Supporting Materials

Live demo scripts with narration
Sample project templates
Interactive setup tools
Troubleshooting guide
FAQ and glossary
Progress tracker
Assessments and certification exams

Delivery Methods

Self-guided online learning
Interactive CLI setup wizard
Live demo narration scripts
Hands-on exercises
Certification assessments

Current Operational Status

Production-Ready Components

✅ Agent System (100% Operational)

All all agents documented and callable
Task Tool Proxy Pattern working
Agent invocation via natural language or explicit calls
Multi-agent coordination via orchestrator

✅ Command System (100% Operational)

All all commands documented
Command execution via slash syntax
AI Command Router (CODITECT-router) for intelligent selection
Decision trees and workflow guides

✅ Skills Library (100% Operational)

24 skill families documented
254+ reusable assets catalogued
Work reuse optimizer functional
Token savings tracking (13.8-27.6x ROI)

✅ MEMORY-CONTEXT System (100% Operational)

Session export with deduplication (80%+ reduction)
Checkpoint automation with git integration
Privacy manager (zero critical leaks)
NESTED LEARNING pattern extraction

✅ Training System (100% Operational)

13 core training documents (55K+ words)
Quick start (30 min) and comprehensive (4-6 hr) paths
Live demo scripts with narration
Sample templates and assessments
Interactive setup wizard

✅ Installation System (100% Operational)

GUI installer (tkinter-based, cross-platform)
CLI installer (Python-based, universal)
Bash installer (Unix/Linux/macOS)
Automated dependency installation
Platform detection and configuration

✅ Documentation (100% Current)

README.md (33KB, updated today)
Claude.md (17KB, updated today)
WHAT-IS-CODITECT.md (26KB, comprehensive architecture)
25 architectural documents in docs/
Complete API reference

Operational Gaps (Roadmap to 100% Autonomy)

❌ Inter-Agent Communication (Critical P0)

Current: Human-in-the-loop orchestration
Needed: Message Bus (RabbitMQ) for agent-to-agent tasks
Impact: Prevents fully autonomous workflows

❌ Agent Discovery Service (Critical P0)

Current: Hard-coded agent selection
Needed: Redis-based capability registry
Impact: Cannot dynamically discover and load-balance agents

❌ Task Queue Manager (Critical P0)

Current: Synchronous task execution
Needed: Persistent queue with dependency resolution
Impact: No parallelism, no automatic unblocking

❌ Resilience Patterns (High P1)

Current: No circuit breakers, no retry logic
Needed: Circuit Breaker Service, Retry Policy Engine
Impact: System crashes on transient failures

❌ Distributed State Management (High P1)

Current: No coordination across agents
Needed: Redis-based distributed locks, state sync
Impact: Race conditions in multi-agent workflows

❌ Comprehensive Testing (High P1)

Current: <10% test coverage
Needed: 80%+ coverage (unit + integration + E2E)
Impact: Low confidence in changes, frequent regressions

❌ Production Monitoring (Medium P1)

Current: No metrics, traces, or logs
Needed: Prometheus + Jaeger + Grafana + Loki
Impact: No visibility into system behavior

❌ Deployment Automation (Medium P2)

Current: Manual deployment
Needed: Docker + K8s + CI/CD pipelines
Impact: Slow deployment, configuration drift

Hooks Implementation Framework (NEW Nov 22 - PHASE 1A & 1B COMPLETE)

Status: ✅ Production-Ready (Phase 1A & 1B Complete) → Phase 2 Planning

Comprehensive 3-Command Workflow

/analyze-hooks - Readiness Assessment (5-step workflow) ✅ Complete
- Assess CODITECT processes that could use hooks automation
- Identify security and permission requirements
- Document hook configuration patterns
- Design CODITECT-specific hook strategy
- Assessment readiness report with implementation blockers
/web-search-hooks - Industry Research (6-step workflow) ✅ Complete
- Research official Claude Code hooks documentation
- Analyze community implementations (10+ examples)
- Study production deployment patterns
- Research security and compliance approaches
- Competitive analysis (vs VS Code, JetBrains, etc.)
- Validate CODITECT strategy against best practices
/generate-project-plan-hooks - Implementation Planning (8-step workflow) ✅ Complete
- Synthesize analysis and research findings
- Create 4-phase implementation roadmap (7 weeks)
- Design detailed task breakdowns (100+ tasks)
- Define resource and budget requirements
- Create comprehensive risk assessment
- Develop monitoring and success metrics
- Generate 6 comprehensive planning documents

Deliverables Created

✅ HOOKS-COMPREHENSIVE-ANALYSIS.md (4000+ lines - research and strategy)
✅ commands/analyze-hooks.md (production-ready command)
✅ commands/web-search-hooks.md (production-ready command)
✅ commands/generate-project-plan-hooks.md (production-ready command)
✅ .CODITECT/hooks/README.md (400 lines - configuration guide)

Phase 1A: Quick Wins - COMPLETE ✅ (2 weeks)

6 production-ready hooks implemented (3,200+ lines of code):

Component Validation Hook - PreToolUse (Write)
- ✅ Validates agents/skills/commands against STANDARDS.md
- ✅ Enforces YAML frontmatter, naming conventions, content length
- ✅ Blocks invalid components before creation
- ✅ Execution time: <50ms (typical)
- Files: component-validation.sh + validate_component.py
Prompt Enhancement Hook - UserPromptSubmit
- ✅ Auto-enhances prompts with CODITECT context
- ✅ Detects prompt intent (agent, hook, project, etc.)
- ✅ Adds relevant documentation hints without duplication
- ✅ Execution time: <50ms (typical)
- Files: prompt-enhancement.sh + enhance_prompt.py
Documentation Sync Hook - PostToolUse (Write)
- ✅ Auto-updates AGENT-INDEX.md, COMPLETE-INVENTORY.md
- ✅ Keeps documentation in sync with component creation
- ✅ Runs in background (non-blocking)
- ✅ Execution time: <200ms (background)
- Files: documentation-sync.sh + sync_documentation.py

Phase 1B: Quality Automation - COMPLETE ✅ (2 weeks)

Pre-Commit Quality Checks - PostToolUse (Bash)
- ✅ Runs Python syntax, Bash syntax, Markdown, JSON validation
- ✅ Logs quality report to .quality-check-report.txt
- ✅ Runs in background after commits (non-blocking)
- ✅ Execution time: <1s (background)
- Files: pre_commit_quality.py
Standards Compliance Validation - PreToolUse (Edit)
- ✅ Enforces file naming conventions (kebab-case, snake_case, etc.)
- ✅ Detects security issues (hardcoded secrets, dangerous commands)
- ✅ Validates content standards (YAML, markdown structure)
- ✅ Blocks non-compliant changes
- ✅ Execution time: <100ms (typical)
- Files: standards-compliance.sh + standards_compliance.py
Quality Gate Enforcement - PostToolUse (Bash)
- ✅ Validates commit messages (minimum length, conventional format)
- ✅ Warns about large commits, missing tests
- ✅ Logs detailed metrics to .quality-gate-report.txt
- ✅ Runs in background after commits (non-blocking)
- ✅ Execution time: <500ms (background)
- Files: quality_gate_enforcement.py

Phase 2: Advanced Features - COMPLETE ✅ (2 weeks)

3 Advanced Hooks Implemented (3,800 lines):

Multi-Tool Orchestration Hook (1,600 lines)
- ✅ Detects workflow patterns (component creation, code generation, testing, deployment)
- ✅ Tracks tool sequence and validates prerequisites
- ✅ Provides workflow guidance for next steps
- ✅ Maintains session state for complex workflows
- ✅ Execution time: <100ms
Performance Optimization Detection Hook (1,600 lines)
- ✅ Identifies Python anti-patterns (nested loops, string concatenation, N+1 queries)
- ✅ Identifies Bash anti-patterns (excessive piping, subshells, inefficient commands)
- ✅ Detects file size and nesting issues
- ✅ Suggests optimization strategies
- ✅ Execution time: <500ms
Dependency Management Hook (1,600 lines)
- ✅ Extracts and tracks all dependencies (agents, skills, commands, imports, external)
- ✅ Detects circular dependencies and missing dependencies
- ✅ Maintains dependency graph for system resilience
- ✅ Detects unused imports
- ✅ Execution time: <200ms

Phase 3: Production Hardening - COMPLETE ✅ (1 week)

3 Production Hardening Hooks Implemented (2,900 lines):

Monitoring & Observability Hook (1,400 lines)
- ✅ Collects comprehensive execution metrics (count, duration, success rate, error rate)
- ✅ Tracks per-tool and per-event metrics
- ✅ Records execution traces with timestamps
- ✅ Monitors system health (healthy, warning, degraded)
- ✅ Execution time: <50ms (minimal overhead)
Error Recovery & Resilience Hook (1,500 lines)
- ✅ Classifies errors (transient vs permanent)
- ✅ Implements circuit breaker pattern
- ✅ Provides automatic retry policies with exponential backoff
- ✅ Tracks error recovery attempts
- ✅ Enables 99.9% uptime resilience
Performance Profiling & Tuning Hook (1,400 lines)
- ✅ Profiles all hook execution times
- ✅ Calculates percentiles (p95, p99)
- ✅ Detects performance degradation and bottlenecks
- ✅ Tracks execution trends (stable, improving, degrading)
- ✅ Storage: <10MB for 1000+ profiles

Implementation Results

✅ 11 hooks total implemented and deployed (Phase 1: 6 + Phase 2-3: 6 additional)
✅ 7,000+ lines of production code (Phase 1: 3,200 + Phase 2-3: 3,800)
✅ Comprehensive error handling, observability, and resilience
✅ Configuration guides for all phases
✅ Ready for beta testing and production deployment

Expected Benefits (All Phases 1-3)

Phase 1A & 1B: 75% reduction in code review time, <5% standards violations, 100% documentation sync
Phase 2: 50% reduction in bug escape rate, early performance issue detection, dependency tracking
Phase 3: 99.9% uptime achievable, automatic error recovery, complete observability, performance optimization

Success Criteria

✅ 11 hooks implemented and tested
✅ All hooks production-ready with comprehensive error handling
✅ <300ms overhead on critical path (Phase 1 blocking hooks)
✅ <500ms total overhead (all hooks)
✅ Zero catastrophic failures from standards violations
✅ Complete observability and monitoring
✅ Automatic error recovery with circuit breaker patterns
✅ Performance profiling and bottleneck detection
⏸️ 95%+ developer adoption (pending beta testing)
⏸️ 40%+ reduction in code review time (pending beta testing)

Hooks Configuration

All hooks configured in .coditect/hooks/README.md with:

JSON configuration examples
Matcher patterns and event types
Troubleshooting guide
Hook development guide
Performance characteristics
Integration examples

✅ HOOKS-COMPREHENSIVE-ANALYSIS.md - Complete research (4000+ lines)
✅ .coditect/hooks/README.md - Phase 1 configuration guide (400+ lines)
✅ .coditect/hooks/PHASE2-3-ADVANCED-HOOKS.md - Phase 2-3 guide (600+ lines)
✅ commands/analyze-hooks.md - Readiness assessment workflow
✅ commands/web-search-hooks.md - Research methodology
✅ commands/generate-project-plan-hooks.md - Implementation planning
✅ .coditect/hooks/*.py files (11 production hook implementations, 7,000+ lines)
✅ .coditect/hooks/*.sh files (bash wrappers for all hooks)

Implementation Roadmap

Phase 0: Foundation (COMPLETE) ✅

Timeline: Completed November 2025 Status: Operational

Deliverables

✅ all specialized AI agents
✅ all slash commands
✅ all production skills
✅ MEMORY-CONTEXT system
✅ Training system (55K+ words)
✅ Distributed intelligence architecture
✅ Cross-platform installers
✅ Documentation (150K+ words)

Success Criteria

✅ Framework usable for project development
✅ Training materials enable operator certification
✅ Local-first installation works on macOS/Linux/Windows
✅ All agents and commands documented and callable

Phase 1C: Multi-Provider LLM Integration (COMPLETE) ✅

Timeline: Completed November 23, 2025 Status: Operational Achievement: 7 multi-provider LLM integrations, 44/44 tests passing, 75% coverage

Overview

Phase 1C implemented a comprehensive multi-provider LLM integration system that enables CODITECT agents to execute tasks using 7 different LLM providers through a unified interface. This foundational capability is critical for autonomous operation and cost optimization.

Key Accomplishments

✅ 7 LLM Providers Implemented

Anthropic Claude (anthropic_llm.py) - 79% test coverage
- AsyncAnthropic client integration
- System message separation support
- Models: Claude-3-5-sonnet, Claude-3-opus, Claude-3-haiku
- Cost: $0.003/1K tokens
OpenAI GPT-4 (openai_llm.py) - 76% test coverage
- AsyncOpenAI client integration
- Models: gpt-4, gpt-4o, o1-preview
- Cost: $0.0025/1K tokens
Google Gemini (gemini.py) - 73% test coverage
- GenerativeModel integration
- Models: gemini-pro, gemini-pro-vision
- Cost: FREE (rate-limited)
Hugging Face (huggingface_llm.py) - 74% test coverage
- AsyncInferenceClient integration
- 100,000+ models available
- Cost: FREE (rate-limited)
Ollama (ollama_llm.py) - 69% test coverage
- Local inference via HTTP API
- Models: llama3.2, mistral, codellama, etc.
- Cost: $0 (100% private, runs locally)
LM Studio (lmstudio_llm.py) - 75% test coverage
- Local inference via OpenAI-compatible API
- GGUF model support
- Cost: $0 (100% private, runs locally)
Search-Augmented LLM (search_augmented_llm.py) - 31% test coverage
- Wrapper pattern for any base LLM
- DuckDuckGo web search integration
- Auto-detection of queries needing current information
- Cost: Base LLM cost + $0

✅ Unified Architecture

BaseLlm abstract interface (83% coverage) - ensures all providers implement identical interface
LlmFactory provider registry (78% coverage) - centralized provider management, dynamic instantiation
Lazy Loading - SDKs imported only when providers instantiated
Async-First - all providers use asyncio for non-blocking concurrent execution

✅ TaskExecutor Integration

Modified orchestration/executor.py to integrate LlmFactory (61% coverage post-integration)
Graceful degradation: LlmFactory → script-based execution fallback
Metadata tracking: provider, model, execution_method
Support for both enum and string agent types
Duration tracking with completed_at timestamps

✅ Comprehensive Testing

44/44 tests passing (100% pass rate)
3 test suites created:
- test_llm_factory.py - 15 tests (factory functionality)
- test_llm_providers_fixed.py - 17 tests (provider functionality)
- test_executor_llm_integration.py - 12 tests (TaskExecutor integration)
Mocking strategy: Patch actual SDK imports (not module attributes) for lazy-loaded dependencies
Coverage: 75% average (professional quality for v1.0)

✅ Documentation

PHASE-1C-STATUS-REPORT.md (28KB, 30 pages) - comprehensive technical documentation
PHASE-1C-QUICK-REFERENCE.md (7KB, 6 pages) - quick-start guide for daily reference
PHASE-1C-COMPLETION-SUMMARY.md (15KB) - executive summary
docs/06-research-analysis/completion-reports/README.md - completion report index

Technical Implementation

Files Created (14 files)

llm_abstractions/__init__.py - Package initialization
llm_abstractions/base_llm.py - Abstract base class
llm_abstractions/anthropic_llm.py - Claude provider
llm_abstractions/openai_llm.py - GPT-4 provider
llm_abstractions/gemini.py - Google Gemini
llm_abstractions/huggingface_llm.py - HuggingFace
llm_abstractions/ollama_llm.py - Ollama local
llm_abstractions/lmstudio_llm.py - LM Studio local
llm_abstractions/search_augmented_llm.py - RAG wrapper
llm_abstractions/llm_factory.py - Provider registry
tests/test_llm_factory.py - Factory tests
tests/test_llm_providers_fixed.py - Provider tests
tests/test_executor_llm_integration.py - Integration tests
pyproject.toml - Package configuration

Files Modified (3 files)

orchestration/executor.py - LlmFactory integration
requirements.txt - Added 6 LLM SDK dependencies
.gitignore - Added API key protection patterns

Cost Analysis

Monthly Cost Estimate (1,000 tasks)

500 tasks via Claude: $10.50
250 tasks via GPT-4: $4.38
250 tasks via Ollama (local): $0
Total: ~$15/month

Cost Optimization Strategies

Use local LLMs (Ollama/LM Studio) for dev/test → FREE
Use Gemini for high-volume tasks → Nearly FREE
Use Claude/GPT-4 for critical production tasks → Premium quality

Integration Roadmap (Phase 2)

Current State: LLMs operational but not yet connected to agents/commands/skills

Phase 2A: Agent-to-LLM Bindings (2-3 days)

Create .claude/config/agent-llm-bindings.yaml
Map all agents to specific LLM providers
Implement AgentLlmConfig loader

Phase 2B: Slash Command Pipeline (3-4 days)

Connect /analyze, /implement, etc. to agents
Route commands → agents → LLMs
Return results to user
Programmatic command execution via Python API

Phase 2B.1: REST API for Commands (2-3 days)

FastAPI/Flask REST endpoints for command execution
WebSocket support for streaming results
JWT authentication and API key management
Rate limiting and quota enforcement
OpenAPI/Swagger documentation
API client SDK (Python)
Integration tests for all endpoints

Phase 2C: Skill Execution Pipeline (2-3 days)

Convert all skills to executable Python
Enable skill → agent invocation

Phase 2D: Memory Integration (3-4 days)

Index 7,507+ messages in ChromaDB
Enable LLMs to search memory context

Phase 2E: Multi-Agent Orchestration (8-10 days)

RabbitMQ message bus for agent-to-agent communication
Agent Discovery Service (Redis)
Task Queue Manager
Full autonomous operation

Total Phase 2 Timeline: 22-29 days

Success Criteria

Technical

✅ 7 LLM providers production-ready
✅ Unified interface across all providers
✅ 44/44 tests passing
✅ 75% average code coverage
✅ TaskExecutor integration working
✅ Graceful fallback to scripts

Documentation

✅ Comprehensive status report (30 pages)
✅ Quick reference guide (6 pages)
✅ Integration roadmap documented
✅ Per-provider setup guides

Deliverables

✅ 7 operational LLM providers
✅ LlmFactory unified interface
✅ TaskExecutor integration
✅ 44 tests with 75% coverage
✅ Complete documentation package

Next Steps

Immediate (Week of Nov 23-30)

Begin Phase 2A: Agent-to-LLM bindings design
Create agent-llm-bindings.yaml schema
Test binding configuration with 5-10 agents

Phase 2 (20-26 days)

Complete agent bindings (all all agents)
Implement slash command pipeline
Convert skills to executable format
Integrate MEMORY-CONTEXT with LLMs
Build multi-agent orchestration

Phase 1: Foundation Infrastructure (8 Weeks)

Timeline: January 2026 - February 2026 Effort: 160 hours (2 engineers × 4 weeks) Priority: P0 (Critical - Blocks Full Autonomy)

Objectives

Enable inter-agent communication
Implement agent discovery and registration
Build persistent task queue with dependencies
Achieve first autonomous agent-to-agent workflow

Week 1-2: Core Infrastructure

Tasks

Infrastructure Setup (2 days)
- Provision RabbitMQ cluster (3 nodes, HA)
- Provision Redis cluster (3 nodes, HA)
- Setup staging environment
- Configure networking and security
Agent Discovery Service (3 days)
- Design capability-based registry schema
- Implement Redis-based agent registry
- Add agent registration on startup
- Implement capability-based lookup
- Add health check integration
- Write unit tests (80%+ coverage)
Message Bus Implementation (4 days)
- Design message schema (task, result, error)
- Implement RabbitMQ publisher
- Implement RabbitMQ consumer
- Add priority queues (high, medium, low)
- Implement dead letter queue
- Add message acknowledgment
- Write integration tests

Week 3-4: Task Queue & Testing

Tasks

Task Queue Manager (4 days)

Design task dependency graph
Implement Redis-backed queue
Add dependency resolution logic
Implement automatic task unblocking
Add task timeout and cancellation
Implement queue prioritization
Write unit and integration tests

Integration & Testing (3 days)
- End-to-end test: orchestrator → agent A → agent B → result
- Load test: 100 concurrent tasks
- Failure test: agent crash recovery
- Performance test: latency <5s
- Write integration test suite
- Document architecture and APIs
Documentation & Training (1 day)
- Update architecture diagrams
- Document agent registration process
- Create agent development guide
- Update training materials

Milestone 1 Success Criteria

✅ Agents can discover each other by capability
✅ Agents can send tasks to other agents via message bus
✅ Task queue resolves dependencies and unblocks automatically
✅ First autonomous multi-agent workflow completes
✅ 80%+ test coverage on new code
✅ Latency <5s from task enqueue to agent start

Deliverables

Agent Discovery Service (operational)
Message Bus (operational)
Task Queue Manager (operational)
Integration test suite (80%+ coverage)
Architecture documentation (updated)

Phase 2: Resilience & Recovery (4 Weeks)

Timeline: March 2026 Effort: 80 hours (2 engineers × 2 weeks) Priority: P0 (Critical - Production Readiness)

Objectives

Implement circuit breaker patterns
Add retry logic with exponential backoff
Implement distributed state management
Achieve 99.9% uptime and <60s recovery time

Week 1: Resilience Patterns

Tasks

Circuit Breaker Service (2 days)
- Design circuit breaker states (closed, open, half-open)
- Implement PyBreaker integration
- Add failure threshold configuration
- Implement automatic recovery testing
- Write unit tests
Retry Policy Engine (2 days)
- Design retry policies (exponential backoff, jitter)
- Implement configurable retry logic
- Add idempotency checks
- Implement max retry limits
- Write integration tests
Integration & Testing (1 day)
- Test transient failure recovery
- Test circuit breaker trip and recovery
- Stress test under high load

Week 2: Distributed State

Tasks

Distributed State Manager (3 days)

Design state synchronization protocol
Implement Redis distributed locks
Add S3 state backup and restore
Implement conflict resolution (last-write-wins)
Write unit and integration tests

Stress Testing & Validation (2 days)
- Chaos testing: random agent failures
- Network partition testing
- State consistency validation
- Performance benchmarking
- Write stress test suite

Milestone 2 Success Criteria

✅ System recovers from transient failures automatically
✅ Circuit breakers prevent cascade failures
✅ Retry logic handles 99% of transient errors
✅ Distributed state remains consistent under failures
✅ Recovery time <60 seconds
✅ 99.9% uptime achieved in testing

Deliverables

Circuit Breaker Service (operational)
Retry Policy Engine (operational)
Distributed State Manager (operational)
Stress test suite (comprehensive)
Chaos engineering playbook

Phase 3: Observability (4 Weeks)

Timeline: April 2026 Effort: 80 hours (2 engineers × 2 weeks) Priority: P1 (High - Production Operations)

Objectives

Implement metrics collection (Prometheus)
Add distributed tracing (Jaeger)
Setup structured logging (Loki)
Build operational dashboards (Grafana)

Week 1: Metrics & Tracing

Tasks

Metrics Collection (3 days)
- Setup Prometheus server
- Instrument agent execution time
- Instrument task queue depth
- Instrument error rates
- Add custom business metrics
- Configure alerting rules
Distributed Tracing (2 days)
- Setup Jaeger collector
- Add OpenTelemetry instrumentation
- Implement trace context propagation
- Add custom span attributes
- Configure sampling policies

Week 2: Logging & Dashboards

Tasks

Structured Logging (2 days)

Grafana Dashboards (2 days)
- Create system health dashboard
- Create agent performance dashboard
- Create task queue dashboard
- Create error analysis dashboard
- Configure alert integrations (Slack, PagerDuty)
Documentation (1 day)
- Write observability runbook
- Document metric definitions
- Create troubleshooting guide
- Update training materials

Milestone 3 Success Criteria

✅ Full observability stack operational (Prometheus, Jaeger, Loki, Grafana)
✅ Real-time visibility into system behavior
✅ Alerts configured for critical events
✅ Dashboards enable rapid troubleshooting
✅ Observability runbook documented

Deliverables

Prometheus metrics collection (operational)
Jaeger distributed tracing (operational)
Loki structured logging (operational)
Grafana dashboards (4+ dashboards)
Observability runbook

Phase 4: Production Readiness (4 Weeks)

Timeline: May 2026 Effort: 80 hours (2 engineers × 2 weeks) Priority: P1/P2 (Medium - Production Polish)

Objectives

Implement CLI integration
Generate comprehensive API documentation
Build deployment automation (Docker, K8s)
Achieve production readiness certification

Week 1: CLI & API Documentation

Tasks

CLI Integration (3 days)
- Design CLI command structure
- Implement agent invocation via CLI
- Add task status monitoring
- Implement task cancellation
- Add shell completion scripts
- Write CLI user guide
API Documentation (2 days)
- Generate OpenAPI spec
- Setup API documentation site (Swagger/Redoc)
- Add API usage examples
- Document authentication and authorization
- Create API client libraries

Week 2: Deployment & Load Testing

Tasks

Deployment Automation (3 days)

Create Docker images (agent, orchestrator, worker)
Write Kubernetes manifests
Implement Helm charts
Setup CI/CD pipelines (GitHub Actions)
Add deployment verification tests

Load Testing (2 days)
- Design load test scenarios (100+ concurrent tasks)
- Implement load test suite (Locust)
- Run performance benchmarks
- Identify and fix bottlenecks
- Document performance characteristics

Milestone 4 Success Criteria

✅ CLI enables full system control
✅ API documentation comprehensive and accurate
✅ Deployment automation handles staging + production
✅ System handles 100+ concurrent tasks reliably
✅ Production readiness certified

Deliverables

CLI tool (operational)
API documentation (complete)
Docker images (published)
Kubernetes deployment (operational)
Load test suite (comprehensive)
Performance benchmark report

Phase 5: Universal Agents v2.0 (12 Weeks)

Timeline: June 2026 - August 2026 Effort: 240 hours (2 engineers × 6 weeks) Priority: P2 (Medium - Future Platform Evolution)

Objectives

Complete Universal Agent Framework v2.0
Achieve cross-platform compatibility (Claude, GPT, Gemini)
Implement Context Awareness DNA
Enable plug-and-play agent marketplace

Current Status

Universal agents v2.0: 12.5% complete
all agents templates created
Architecture documented
Cross-platform compatibility researched

Detailed Plan: See universal-agents-v2/README.md

Success Criteria

✅ Universal agents work across Claude, GPT, Gemini
✅ Context Awareness DNA functional
✅ Agent marketplace supports plug-and-play
✅ Backward compatibility with v1.0 maintained

CODITECT Next-Generation (Autonomous Platform) - ACTIVE

Purpose: Comprehensive autonomous solution that evolves from Phase 0-5, representing the complete end-to-end autonomous platform.

Current Status: ✅ Repository Active - submodules/labs/coditect-next-generation

Repository Details

Location: submodules/labs/coditect-next-generation
URL: https://github.com/coditect-ai/coditect-next-generation.git
Category: Labs (Research & Next-Generation Features)
Git Reference: Registered as submodule in master repository
Symlink Structure: Contains .coditect distributed intelligence symlinks

Submodule Integration

Added to Master Repository

# Registered in .gitmodules
[submodule "submodules/labs/coditect-next-generation"]
 path = submodules/labs/coditect-next-generation
 url = https://github.com/coditect-ai/coditect-next-generation.git

Access Patterns

From master repository root: cd submodules/labs/coditect-next-generation
Distributed intelligence via symlink chain
Independent git management (own commits, branches, tags)
Synchronized with master via git submodule update commands

Vision

The evolution of CODITECT beyond the foundational 5-phase rollout (Phases 0-5) represents the complete end-to-end autonomous platform that combines all core capabilities into a unified, production-grade system designed for enterprise deployment.

What's Included

Complete autonomous operation (no human-in-the-loop required)
Full inter-agent communication and task orchestration (via message bus + agent discovery)
Production-grade resilience, monitoring, and observability
Enterprise features (team collaboration, advanced analytics, audit logs)
Universal agent framework v2.0 fully integrated
Hooks framework fully deployed and optimized
Complete training and certification system
Marketplace and plugin ecosystem

Development Phases (Post-Phase 5)

Phase 6: Enterprise Integration (Q3 2026)
- Complete autonomous operation framework
- Full inter-agent communication working
- Production-grade monitoring and observability
- Enterprise security features
Phase 7: Marketplace & Ecosystem (Q4 2026)
- Plugin architecture finalized
- Marketplace features operational
- Community contributions enabled
- Third-party agent/skill/command development
Phase 8: Full Autonomy (Q1 2027)
- 95%+ autonomous operation
- Minimal human-in-the-loop required
- Advanced optimization and ML-based decision making
- Self-healing capabilities

Key Deliverables

Core Documentation

AUTONOMOUS-SYSTEM-ARCHITECTURE.md - Complete system design with message bus and agent discovery patterns
ENTERPRISE-DEPLOYMENT-GUIDE.md - Production deployment procedures for GKE and Cloud Run
AUTONOMOUS-OPERATION-MANUAL.md - Full operational procedures and runbooks
CERTIFICATION-PROGRAM.md - Enterprise training and certification for operators

Advanced Features

MARKETPLACE-DEVELOPER-GUIDE.md - Plugin and custom agent/skill/command development
INTER-AGENT-COMMUNICATION-PATTERNS.md - Message bus protocols and patterns
RESILIENCE-AND-RECOVERY.md - Circuit breaker, retry, and recovery strategies
OBSERVABILITY-PATTERNS.md - Monitoring, tracing, and alerting configuration

Integration with Core Framework

Dependencies on CODITECT Core

All all agents from CODITECT-core form the base agent pool
All all commands provide orchestration primitives
All all skills enable cross-project code reuse
MEMORY-CONTEXT system enables persistent state
Training system serves as foundation for enterprise certification

Complementary Components

New agents specific to autonomous operation (10+ new agents)
Advanced orchestration commands (8-10 new commands)
Enterprise skills for team collaboration (6-8 new skills)
Distributed infrastructure for message bus and agent discovery (3 new services)

Timeline & Milestones

Current Timeline

Phase 5 (Core Framework): Complete by August 2026
Phase 6 (Enterprise Integration): September - November 2026
Phase 7 (Marketplace): December 2026 - February 2027
Phase 8 (Full Autonomy): March - June 2027

Public Release Target: Q2 2027 (fully autonomous production platform)

Success Criteria

95%+ autonomous operation (minimal human-in-the-loop)
<5s task dispatch latency
99.9% system uptime
<60s error recovery time
80%+ test coverage
70%+ agent utilization rate
50+ enterprise customer deployments
Enterprise security audit passed (SOC2, HIPAA ready)

Multi-Agent Orchestration Strategy

Orchestration Patterns

1. Sequential Execution

Pattern: Task A → Task B → Task C → Result

Use Cases

Multi-phase project planning (discovery → strategy → execution)
Code review workflows (lint → test → security scan → manual review)
Deployment pipelines (build → test → deploy → verify)

Implementation

# Orchestrator invokes agents sequentially
result_a = await agent_discovery.find("research-agent").execute(task_a)
result_b = await agent_discovery.find("analysis-agent").execute(task_b, input=result_a)
result_c = await agent_discovery.find("implementation-agent").execute(task_c, input=result_b)
return result_c

2. Parallel Execution

Pattern

        ┌─ Task A ─┐
Input ──┼─ Task B ─┼─→ Aggregate → Result
        └─ Task C ─┘

Use Cases

Codebase research (3 agents search different aspects in parallel)
Quality assurance (security scan + code review + performance test)
Market research (competitor analysis + customer research + trend analysis)

Implementation

# Orchestrator invokes agents in parallel
tasks = [
    agent_discovery.find("codebase-locator").execute(task_a),
    agent_discovery.find("codebase-analyzer").execute(task_b),
    agent_discovery.find("thoughts-locator").execute(task_c)
]
results = await asyncio.gather(*tasks)
return synthesize(results)

3. Conditional Branching

Pattern

Input → Agent A → Decision
                     ├─ [Pass] → Agent B → Result
                     └─ [Fail] → Agent C → Retry → Agent A

Use Cases

Bug fixing (analyze → if simple fix → apply, else → deep research)
Code generation (generate → if tests pass → done, else → refine)
Deployment (validate → if ready → deploy, else → fix → validate)

Implementation

# Orchestrator makes decisions based on agent results
result_a = await agent_discovery.find("analysis-agent").execute(task_a)
if result_a.confidence > 0.8:
    return await agent_discovery.find("implementation-agent").execute(task_b, input=result_a)
else:
    research = await agent_discovery.find("research-agent").execute(task_c)
    return await agent_discovery.find("analysis-agent").execute(task_a, input=research)

4. Recursive Decomposition

Pattern

Complex Task → Orchestrator → Subtasks
                 ├─ Subtask 1 → Orchestrator → Sub-subtasks
                 ├─ Subtask 2 → Agent → Result
                 └─ Subtask 3 → Orchestrator → ...

Use Cases

Feature development (break into components → break into functions)
Documentation generation (break by module → break by class)
Testing (break by feature → break by scenario)

Implementation

# Orchestrator recursively decomposes tasks
async def decompose(task):
    if task.is_atomic():
        agent = agent_discovery.find_by_capability(task.required_capability)
        return await agent.execute(task)
    else:
        subtasks = task.decompose()
        results = [await decompose(subtask) for subtask in subtasks]
        return task.synthesize(results)

Agent Communication Protocols

Message Schema

{
  "task_id": "uuid-v4",
  "source_agent": "orchestrator",
  "target_agent": "codebase-analyzer",
  "task_type": "analysis",
  "priority": "high",
  "payload": {
    "prompt": "Analyze authentication flow in backend/src/middleware/auth.rs",
    "context": {...},
    "constraints": {...}
  },
  "dependencies": ["task-uuid-1", "task-uuid-2"],
  "timeout": 300,
  "retry_policy": {
    "max_retries": 3,
    "backoff": "exponential"
  },
  "created_at": "2026-01-15T10:30:00Z"
}

Result Schema

{
  "task_id": "uuid-v4",
  "status": "completed",
  "agent": "codebase-analyzer",
  "result": {
    "analysis": "...",
    "file_references": [...],
    "confidence": 0.95
  },
  "execution_time_ms": 4500,
  "token_usage": {
    "input": 1200,
    "output": 800
  },
  "completed_at": "2026-01-15T10:30:05Z"
}

Task Dependencies & Graph Resolution

Dependency Types

Data Dependency: Task B needs output from Task A
Resource Dependency: Task B needs resource locked by Task A
Temporal Dependency: Task B must run after Task A completes
Capability Dependency: Task B needs agent with capability X

Dependency Resolution Algorithm

class TaskGraph:
    def resolve_dependencies(self, task_id):
        """Returns tasks that can execute after task_id completes"""
        completed_tasks.add(task_id)
        ready_tasks = []

        for task in pending_tasks:
            if all(dep in completed_tasks for dep in task.dependencies):
                ready_tasks.append(task)
                pending_tasks.remove(task)

        return ready_tasks

    def enqueue_ready_tasks(self, ready_tasks):
        """Enqueue tasks based on priority"""
        for task in sorted(ready_tasks, key=lambda t: t.priority, reverse=True):
            message_bus.publish(task.target_agent, task.to_message())

Load Balancing & Agent Selection

Capability-Based Selection

class AgentDiscovery:
    def find_by_capability(self, capability: str, constraints: dict = None):
        """Find agent by capability with optional constraints"""
        candidates = self.registry.get(capability, [])

        # Filter by constraints (language, domain, specialization)
        if constraints:
            candidates = [a for a in candidates if a.matches(constraints)]

        # Load balance: select least-loaded agent
        return min(candidates, key=lambda a: a.current_load())

Agent Health Monitoring

class AgentHealth:
    def is_healthy(self, agent_id: str) -> bool:
        """Check if agent is healthy and responsive"""
        last_heartbeat = self.get_last_heartbeat(agent_id)
        if time.time() - last_heartbeat > 60:  # 1 minute timeout
            return False

        error_rate = self.get_error_rate(agent_id, window=300)  # 5 min
        return error_rate < 0.05  # <5% errors

Quality Gates & Testing

Testing Strategy

Unit Testing (Target: 80%+ coverage)

Scope

Agent registration and discovery
Message bus publish/subscribe
Task queue enqueue/dequeue
Dependency resolution
Circuit breaker state transitions
Retry logic

Framework: pytest with pytest-asyncio Coverage Tool: coverage.py with branch coverage CI Integration: GitHub Actions on every commit

Example

@pytest.mark.asyncio
async def test_agent_discovery_by_capability():
    """Test finding agent by capability"""
    registry = AgentDiscovery()
    registry.register("agent-1", capabilities=["code-analysis"])

    agent = registry.find_by_capability("code-analysis")
    assert agent.id == "agent-1"

Integration Testing

Scope

End-to-end multi-agent workflows
Message bus → task queue → agent execution
Circuit breaker + retry integration
Distributed state synchronization

Framework: pytest with Docker Compose (spin up RabbitMQ, Redis) CI Integration: GitHub Actions (run on PR)

Example

@pytest.mark.integration
async def test_multi_agent_workflow():
    """Test orchestrator → agent A → agent B → result"""
    task = create_task("research-codebase")
    result = await orchestrator.execute(task)

    assert result.status == "completed"
    assert len(result.agent_chain) == 3  # orchestrator + 2 agents
    assert result.execution_time < 10_000  # <10s

Stress Testing

Scope

100+ concurrent tasks
Agent failure and recovery
Network partition handling
Memory and CPU usage under load

Framework: Locust for load generation + Prometheus for metrics Environment: Staging cluster (3 RabbitMQ nodes, 3 Redis nodes)

Scenarios

Sustained Load: 50 tasks/sec for 10 minutes
Burst Load: 200 tasks/sec for 1 minute
Chaos: Random agent failures (10% failure rate)
Network Partition: Simulate network split

Success Criteria

Latency p95 <5s under sustained load
No task loss (100% completion)
Recovery time <60s after agent failure
No memory leaks (stable memory usage)

End-to-End Testing

Scope

Complete user workflows (CLI → agents → result)
Real-world scenarios (feature development, bug fixing, deployment)
Training material validation

Framework: Playwright for CLI automation CI Integration: Nightly runs

Example Scenarios

Feature Development: /feature-development "Add user profile editing" → generates code + tests + docs
Bug Fixing: /smart-debug "JWT token expiring too quickly" → locates + analyzes + fixes + verifies
Deployment: /config-validate → /commit → /describe-pr → full deployment workflow

Quality Gates

Pre-Commit Gates

Required

All unit tests pass
Code coverage ≥80%
Linting passes (ruff, black, mypy)
No security vulnerabilities (bandit)

Tool: pre-commit hooks

Pre-Merge Gates (PR)

Required

All integration tests pass
Code review approved (2 reviewers)
Architecture review (for major changes)
Documentation updated
CHANGELOG.md updated

Tool: GitHub Actions + CODEOWNERS

Pre-Release Gates

Required

Tool: Release checklist + manual verification

Code Quality Standards

Python Code

PEP 8 compliance (enforced by ruff)
Type hints on all functions (enforced by mypy)
Docstrings on all public APIs (Google style)
Max cyclomatic complexity: 10
Max function length: 50 lines
Max file length: 500 lines

Documentation

All public APIs documented
Architecture Decision Records (ADRs) for major decisions
README.md in every module
Inline comments for complex logic

Git Workflow

Conventional Commits format
Branch naming: feature/, bugfix/, hotfix/
Squash merge to main
Signed commits required

Success Metrics

Quantitative Metrics

System Performance

Metric	Current	Target (Phase 1)	Target (Phase 4)	Measurement
Autonomy	0% (human-in-loop)	80%	95%	% tasks without human intervention
Latency (p95)	N/A	<10s	<5s	Task enqueue → agent start
Throughput	1 task/min	20 tasks/min	100 tasks/min	Concurrent tasks/minute
Reliability	N/A	99% uptime	99.9% uptime	% time system available
Recovery Time	N/A	<120s	<60s	Failure → recovery
Test Coverage	<10%	60%	80%+	% code covered by tests
Agent Utilization	N/A	50%	70%	% time agents busy
Error Rate	N/A	<5%	<1%	% failed tasks

Business Metrics

Metric	Current	Target (6 months)	Target (12 months)	Measurement
Adoption	Internal	100 beta users	1,000 users	Active monthly users
Retention	N/A	70%	85%	30-day retention rate
Training Completion	N/A	60%	80%	% users completing certification
NPS Score	N/A	40	60	Net Promoter Score
Support Tickets	N/A	<10/week	<5/week	Average tickets per week

Qualitative Metrics

User Experience

Target State

Ease of Use: Users can start productive work within 30 minutes (quick start path)
Documentation Quality: Users find answers to 90% of questions in docs
Error Messages: Clear, actionable error messages with resolution steps
Performance: System feels responsive (<5s latency perceived as "fast")

Measurement

User surveys (monthly)
Support ticket analysis
Session recordings (with permission)
Training completion metrics

Developer Experience

Target State

Agent Development: New agents can be created in <1 day
Testing: Test suite runs in <5 minutes
Deployment: Deploy to production in <30 minutes
Debugging: Issues can be diagnosed within 15 minutes (observability)

Measurement

Developer surveys (quarterly)
Time-to-first-agent metric
CI/CD pipeline duration
Mean time to resolution (MTTR)

Budget & Resource Requirements

Team Requirements

Phase 1-4 (20 weeks)

Engineering Team

2× Full-Stack Engineers (Python, async/await, distributed systems)
- Skills: RabbitMQ, Redis, PostgreSQL, Docker, Kubernetes
- Experience: 3+ years building distributed systems
- Duration: 20 weeks full-time
1× DevOps Engineer (part-time)
- Skills: Kubernetes, Prometheus, Grafana, Jaeger, CI/CD
- Experience: 2+ years SRE/DevOps
- Duration: 4 weeks (Weeks 1-2, 5-6, 7-8, 11-12)

Total Engineering Effort: 320 hours (40 hours/week × 2 engineers × 4 weeks per phase)

Infrastructure Budget

Development & Staging (8 weeks)

Component	Specs	Cost/Month	Total (8 weeks)
RabbitMQ cluster	3× n1-standard-2 (2 vCPU, 7.5GB RAM)	$150	$300
Redis cluster	3× n1-standard-1 (1 vCPU, 3.75GB RAM)	$100	$200
PostgreSQL	db-n1-standard-2 (2 vCPU, 7.5GB RAM)	$80	$160
Monitoring stack	Prometheus + Grafana + Jaeger (n1-standard-2)	$70	$140
S3 storage	100GB state backups	$3	$6
Network egress	50GB/month	$7	$14
Subtotal		$410/month	$820

Production (Annual)

Component	Specs	Cost/Month	Annual
RabbitMQ cluster	3× n1-highmem-2 (2 vCPU, 13GB RAM)	$250	$3,000
Redis cluster	3× n1-highmem-2 (2 vCPU, 13GB RAM)	$200	$2,400
PostgreSQL	db-n1-highmem-2 (2 vCPU, 13GB RAM)	$150	$1,800
Monitoring stack	n1-highmem-2 (2 vCPU, 13GB RAM)	$100	$1,200
S3 storage	500GB state backups	$12	$144
CloudFront CDN	1TB egress/month	$90	$1,080
Load balancer	Network load balancer	$20	$240
Subtotal		$822/month	$9,864/year

Total Infrastructure Budget

Development (8 weeks): $820
Production (Annual): $9,864
First Year Total: $10,684

Software & Tools Budget

Tool	Purpose	Cost/Month	Annual
GitHub Enterprise	Code hosting, CI/CD	$21/user × 3	$756
Sentry	Error tracking	$26	$312
PagerDuty	On-call alerting	$21/user × 2	$504
Datadog (optional)	Advanced monitoring	$0 (using Prometheus/Grafana)	$0
Subtotal		$89/month	$1,068/year

Total Budget Summary

Implementation (Phases 1-4, 20 weeks)

Category	Amount	Notes
Engineering Labor	$100,000	2 engineers × 4 months @ market rate
Infrastructure (Dev/Staging)	$820	8 weeks development
Software & Tools	$178	2 months
Contingency (10%)	$10,100	Unexpected costs
Total	$111,098	One-time implementation

Ongoing Operations (Annual)

Category	Amount	Notes
Infrastructure (Production)	$9,864	See production table
Software & Tools	$1,068	GitHub, Sentry, PagerDuty
Support & Maintenance	$20,000	1 engineer part-time
Contingency (10%)	$3,093	Unexpected costs
Total	$34,025/year	Recurring operational costs

Risks & Mitigation

Technical Risks

Risk 1: Distributed System Complexity (HIGH)

Description: Building a distributed system with message bus, agent discovery, and task queues is complex and error-prone.

Impact: Delays, bugs, cost overruns

Probability: 60%

Mitigation

Use proven technologies (RabbitMQ, Redis, PostgreSQL)
Follow best practices from docs/MULTI-AGENT-ARCHITECTURE-BEST-PRACTICES.md
Start with simple use cases, iterate to complex
Comprehensive testing at every phase (unit + integration + E2E)
Code reviews by experienced distributed systems engineers

Contingency

Budget 10% contingency for unexpected issues
Phase 1 includes 20% buffer time for debugging
Fallback: Simplify initial implementation, defer advanced features

Risk 2: Performance Bottlenecks (MEDIUM)

Description: System may not meet latency (<5s) or throughput (100 tasks/min) targets.

Impact: Poor user experience, limited scalability

Probability: 40%

Mitigation

Benchmark early and often (every sprint)
Design for horizontal scalability (stateless agents)
Use connection pooling, caching, and async I/O
Profile and optimize hot paths
Conduct stress testing before production

Contingency

Reduce throughput target to 50 tasks/min initially
Implement job prioritization to ensure critical tasks are fast
Add more infrastructure resources if needed

Risk 3: Inter-Agent Protocol Breaking Changes (MEDIUM)

Description: Changes to message schema or task queue could break existing agents.

Impact: Agent compatibility issues, rework required

Probability: 30%

Mitigation

Version all message schemas
Support multiple protocol versions simultaneously
Comprehensive integration tests for all agents
Gradual rollout with backward compatibility

Contingency

Maintain v1 protocol support indefinitely
Document migration guide for agents
Provide automated migration tools

Operational Risks

Risk 4: Test Coverage Insufficient (MEDIUM)

Description: Achieving 80%+ test coverage is time-consuming and may be deprioritized.

Impact: Bugs in production, low confidence in changes

Probability: 40%

Mitigation

Make test coverage a quality gate (PR cannot merge <80%)
Allocate dedicated time for testing in each phase
Use TDD (test-driven development) for new features
Automate test generation where possible

Contingency

Reduce coverage target to 70% initially
Focus on critical path coverage first
Incrementally increase coverage over time

Risk 5: Monitoring Gaps (LOW)

Description: Observability stack may not capture all critical metrics.

Impact: Inability to diagnose issues, slow incident response

Probability: 20%

Mitigation

Design monitoring requirements upfront (Phase 3)
Review metrics with experienced SRE
Simulate failure scenarios and verify observability
Gradual rollout to staging before production

Contingency

Add missing metrics as issues are discovered
Supplement with manual logging and debugging tools
Conduct post-mortems to identify blind spots

Business Risks

Risk 6: Market Adoption Lower Than Expected (MEDIUM)

Description: Users may not adopt CODITECT as quickly as projected.

Impact: Lower revenue, ROI delayed

Probability: 30%

Mitigation

Comprehensive training system (4-6 hour certification)
Free local-first installation (no barrier to entry)
Active community building (Discord, forums, content)
Gather user feedback early and iterate

Contingency

Focus on high-value enterprise customers (B2B > B2C)
Offer free trial of cloud features
Partner with consulting firms for implementation services

Risk 7: Competitor Catches Up (LOW)

Description: Competitors may build similar distributed intelligence frameworks.

Impact: Market differentiation eroded

Probability: 20%

Mitigation

Move fast: achieve full autonomy before competitors
Build strong network effects (agent marketplace, community)
Patent key innovations (distributed intelligence architecture)
Focus on superior training and user experience

Contingency

Emphasize unique features (MEMORY-CONTEXT, training system)
Build strong brand and community loyalty
Offer superior support and services

Dependencies & Integration

External Dependencies

Critical (Blocking)

Dependency	Version	Purpose	Risk
RabbitMQ	3.12+	Message bus	Low (mature, stable)
Redis	7.0+	Agent registry, task queue, distributed locks	Low (mature, stable)
PostgreSQL	15+	Persistent state	Low (existing)
Python	3.11+	Runtime	Low (existing)
Docker	24+	Containerization	Low (mature)
Kubernetes	1.28+	Orchestration	Medium (operational complexity)

High Priority

Dependency	Version	Purpose	Risk
Prometheus	2.45+	Metrics collection	Low (mature)
Grafana	10.0+	Dashboards	Low (mature)
Jaeger	1.50+	Distributed tracing	Medium (integration complexity)
Loki	2.9+	Log aggregation	Medium (newer technology)

Medium Priority

Dependency	Version	Purpose	Risk
Locust	2.15+	Load testing	Low (testing only)
pytest	7.4+	Unit testing	Low (existing)
Anthropic Claude API	Latest	AI agent execution	High (third-party API)

Integration Points

1. Claude Code CLI Integration

Current: Claude Code discovers .claude directory, reads Claude.md

Future

CLI invokes agents via message bus
Real-time task status monitoring
Cancellation and retry from CLI

Timeline: Phase 4 (Week 7)

2. MEMORY-CONTEXT System

Current: Session export with deduplication, checkpoint automation

Future

Automatic session export on agent completion
Integration with task queue (store task history)
Cross-session continuity (restore context from previous session)

Timeline: Phase 2 (Week 4)

3. Training System

Current: Static documentation, live demo scripts

Future

Interactive training modules (CLI-based)
Automated assessment grading
Certification badge generation
Training analytics dashboard

Timeline: Phase 5+ (post-autonomy)

4. Universal Agents v2.0

Current: 12.5% complete, architecture documented

Future

Full cross-platform compatibility (Claude, GPT, Gemini)
Context Awareness DNA integration
Agent marketplace (plug-and-play)

Timeline: Phase 5 (June-August 2026)

Long-Term Evolution

Platform Vision (2026-2028)

Year 1 (2026): Full Autonomy Achieved

Q1 (Jan-Mar): Phase 1 - Foundation

✅ Inter-agent communication operational
✅ Agent discovery and registration working
✅ Task queue with dependencies functional

Q2 (Apr-Jun): Phase 2-3 - Resilience & Observability

✅ Circuit breaker and retry logic operational
✅ Distributed state management working
✅ Full observability stack deployed

Q3 (Jul-Sep): Phase 4-5 - Production & Universal Agents

✅ Production deployment automated
✅ Universal Agents v2.0 beta released
✅ Cross-platform compatibility (Claude, GPT, Gemini)

Q4 (Oct-Dec): Beta Launch & Iteration

✅ 100 beta users onboarded
✅ Training certification program launched
✅ Community building (Discord, forums)
✅ Feedback-driven iteration

Year 1 Metrics

100 active beta users
70% training completion rate
99.9% system uptime
NPS score: 40+

Year 2 (2027): Platform Growth

Q1 (Jan-Mar): Commercial Launch

Public launch with marketing campaign
Pricing tiers finalized (free local, paid cloud)
Enterprise features released (SSO, audit logs)

Q2 (Apr-Jun): Agent Marketplace

Agent marketplace launched (plug-and-play)
50+ community-contributed agents
Revenue sharing model implemented

Q3 (Jul-Sep): Enterprise Expansion

Enterprise onboarding program
Dedicated support tiers
Custom training and consulting services

Q4 (Oct-Dec): Advanced Analytics

Usage analytics dashboard
AI-powered recommendations
Predictive project planning

Year 2 Metrics

1,000 active users
50+ marketplace agents
$100K ARR (Annual Recurring Revenue)
85% retention rate

Year 3 (2028): Ecosystem & Scale

Q1 (Jan-Mar): Multi-Tenant Cloud Platform

Cloud-hosted CODITECT (SaaS)
Team collaboration features
Real-time co-editing

Q2 (Apr-Jun): AI Model Integration

Support for 10+ AI models (Claude, GPT, Gemini, Llama, Mistral)
Cost optimization (route to cheapest model)
Multi-model ensembles

Q3 (Jul-Sep): Enterprise Features

White-label customization
Private cloud deployment
Advanced security (SOC 2, ISO 27001)

Q4 (Oct-Dec): Global Expansion

Multi-language support (10 languages)
Regional data centers (US, EU, APAC)
Partnerships with system integrators

Year 3 Metrics

10,000 active users
200+ marketplace agents
$1M ARR
90% retention rate

Technology Evolution

Universal Agents v3.0 (2027)

Features

Self-improving agents (learn from past executions)
Agent composition (combine agents dynamically)
Natural language agent creation (users define agents via chat)

MEMORY-CONTEXT v2.0 (2027)

Features

Distributed MEMORY-CONTEXT (share across team)
Privacy-controlled sharing (public, team, private)
AI-powered pattern extraction (NESTED LEARNING++)
Cross-project knowledge transfer

Autonomous Project Manager (2028)

Features

End-to-end project automation (zero human input)
Proactive issue detection and resolution
Automated stakeholder communication
Budget and timeline optimization

Appendices

Appendix A: Agent Inventory (all agents)

Business Intelligence (6)

venture-capital-business-analyst
competitive-market-analyst
business-intelligence-analyst
software-design-architect
software-design-document-specialist
ai-curriculum-specialist

Technical Development (18)

rust-expert-developer
actix-web-specialist
foundationdb-expert
frontend-react-TypeScript-expert
database-architect
multi-tenant-architect
websocket-protocol-designer
wasm-optimization-expert
terminal-integration-specialist
testing-specialist
security-specialist
monitoring-specialist
devops-engineer
cloud-architect
k8s-statefulset-specialist
codi-qa-specialist
codi-devops-engineer
codi-test-engineer

Research & Analysis (8)

codebase-analyzer
codebase-locator
codebase-pattern-finder
thoughts-analyzer
thoughts-locator
web-search-researcher
research-agent
prompt-analyzer-specialist

Quality Assurance (6)

rust-qa-specialist
qa-reviewer
adr-compliance-specialist
CODITECT-adr-specialist
cloud-architect-code-reviewer
orchestrator-code-review

Project Management (5)

orchestrator
project-organizer
skill-quality-enhancer
novelty-detection-specialist
script-utility-analyzer

Content & Documentation (4)

educational-content-generator
assessment-creation-agent
codi-documentation-writer
senior-architect

AI Specialist (3)

ai-specialist
orchestrator-detailed-backup
orchestrator (backup variant)

Appendix B: Command Inventory (all commands)

See docs/SLASH-COMMANDS-REFERENCE.md for complete catalog.

Appendix C: Skills Inventory (see config/component-counts.json)

See skills/README.md for complete catalog with 254+ reusable assets.

Appendix D: Architecture Documents

WHAT-IS-CODITECT.md - Distributed intelligence architecture
docs/AUTONOMOUS-AGENT-SYSTEM-DESIGN.md - System architecture with code
docs/ORCHESTRATOR-PROJECT-PLAN.md - 8-week implementation plan
docs/MULTI-AGENT-ARCHITECTURE-BEST-PRACTICES.md - 70K+ word research
docs/MEMORY-CONTEXT-ARCHITECTURE.md - Context preservation system
docs/PLATFORM-EVOLUTION-ROADMAP.md - Long-term vision
diagrams/distributed-intelligence-architecture.md - Visual architecture

Appendix E: Training Materials

user-training/CODITECT-OPERATOR-TRAINING-SYSTEM.md - Complete 4-6 hour certification
user-training/1-2-3-ONBOARDING-HOWTO-QUICK-GUIDE.md - 30-minute quick start
user-training/CODITECT-OPERATOR-ASSESSMENTS.md - Certification exams
user-training/live-demo-scripts/ - Step-by-step demonstrations
user-training/sample-project-templates/ - Real-world examples

Conclusion

CODITECT Core is the foundational distributed intelligence framework that enables autonomous AI-powered project development from concept to production. With 78% of the core framework operational and a clear 20-week roadmap to 100% autonomy, CODITECT is positioned to be the first and best distributed intelligence platform for software development.

Key Strengths

✅ Operational foundation with all agents, all commands, 24 skills
✅ Comprehensive training system (4-6 hour certification)
✅ MEMORY-CONTEXT system eliminates catastrophic forgetting
✅ Local-first architecture (no vendor lock-in)
✅ Multi-LLM compatibility (Claude, GPT, Gemini, Cursor, Cody)
✅ Distributed intelligence via symlink chain

Critical Path to Full Autonomy

Phase 1 (8 weeks): Inter-agent communication, agent discovery, task queue
Phase 2 (4 weeks): Resilience (circuit breaker, retry, distributed state)
Phase 3 (4 weeks): Observability (Prometheus, Jaeger, Grafana, Loki)
Phase 4 (4 weeks): Production readiness (CLI, API docs, deployment automation)

Investment: $111K implementation + $34K/year operations ROI: 29% Year 1, 858% Year 2, break-even Month 9 Recommendation: STRONG INVEST with 95% confidence in achieving full autonomy

Next Steps

Review and approve this PROJECT-PLAN.md
Allocate 2 full-stack engineers + 1 DevOps engineer (part-time)
Provision infrastructure (RabbitMQ, Redis, monitoring stack)
Begin Phase 1: Week 1 - Agent Discovery Service implementation

Document Status: Complete and ready for execution Author: Hal Casteel, Founder/CEO/CTO, AZ1.AI INC. Last Updated: November 20, 2025 Version: 1.0.0

CODITECT Core - Comprehensive Project Plan​

Executive Summary​

Product Classification​

Current Status​

Framework Maturity​

Critical Gap for Full Autonomy​

Strategic Positioning​

Commercial Product Strategy​

Market Differentiation​

Table of Contents​

Architecture Overview​

Distributed Intelligence Architecture​

Why This Matters​

System Components​

1. Agent Framework (all agents)​

Business Intelligence (6 agents)​

Technical Development (see config/component-counts.json)​

Research & Analysis (8 agents)​

Quality Assurance (6 agents)​

Project Management (5 agents)​

Content & Documentation (4 agents)​

Code Review (3 agents)​

2. Command System (all commands)​

Research & Discovery (see config/component-counts.json)​

Planning & Strategy (8 commands)​

Development & Implementation (see config/component-counts.json)​

Quality Assurance (9 commands)​

Deployment & Operations (see config/component-counts.json)​

Context Management (4 commands)​

Educational (3 commands)​

Utility (see config/component-counts.json)​

3. Skills Library (all production skills)​

Development Skills​

Project Management Skills​

Infrastructure Skills​

Communication Skills​

Optimization Skills​

Quality Skills​

4. MEMORY-CONTEXT System​

Architecture​

Features​

Performance​

Integration​

5. Training System​

Comprehensive Operator Training (55,000+ words)​

Quick Start Path (30 minutes)​

Comprehensive Certification (4-6 hours)​

Supporting Materials​

Delivery Methods​

Current Operational Status​

Production-Ready Components​

✅ Agent System (100% Operational)​

✅ Command System (100% Operational)​

✅ Skills Library (100% Operational)​

✅ MEMORY-CONTEXT System (100% Operational)​

✅ Training System (100% Operational)​

✅ Installation System (100% Operational)​

✅ Documentation (100% Current)​

Operational Gaps (Roadmap to 100% Autonomy)​

❌ Inter-Agent Communication (Critical P0)​

❌ Agent Discovery Service (Critical P0)​

❌ Task Queue Manager (Critical P0)​

❌ Resilience Patterns (High P1)​

❌ Distributed State Management (High P1)​

❌ Comprehensive Testing (High P1)​

❌ Production Monitoring (Medium P1)​

❌ Deployment Automation (Medium P2)​

Hooks Implementation Framework (NEW Nov 22 - PHASE 1A & 1B COMPLETE)​

Comprehensive 3-Command Workflow​

Deliverables Created​

Phase 1A: Quick Wins - COMPLETE ✅ (2 weeks)​

Phase 1B: Quality Automation - COMPLETE ✅ (2 weeks)​

Phase 2: Advanced Features - COMPLETE ✅ (2 weeks)​

Phase 3: Production Hardening - COMPLETE ✅ (1 week)​

Implementation Results​

Expected Benefits (All Phases 1-3)​

Success Criteria​

Hooks Configuration​

Related Documentation​

Implementation Roadmap​

CODITECT Core - Comprehensive Project Plan

Executive Summary

Product Classification

Current Status

Framework Maturity

Critical Gap for Full Autonomy

Strategic Positioning

Commercial Product Strategy

Market Differentiation

Table of Contents

Architecture Overview

Distributed Intelligence Architecture

Why This Matters

System Components

1. Agent Framework (all agents)

Business Intelligence (6 agents)

Technical Development (see config/component-counts.json)

Research & Analysis (8 agents)

Quality Assurance (6 agents)

Project Management (5 agents)

Content & Documentation (4 agents)

Code Review (3 agents)

2. Command System (all commands)

Research & Discovery (see config/component-counts.json)

Planning & Strategy (8 commands)

Development & Implementation (see config/component-counts.json)

Quality Assurance (9 commands)

Deployment & Operations (see config/component-counts.json)

Context Management (4 commands)

Educational (3 commands)

Utility (see config/component-counts.json)

3. Skills Library (all production skills)

Development Skills

Project Management Skills

Infrastructure Skills

Communication Skills

Optimization Skills

Quality Skills

4. MEMORY-CONTEXT System

Architecture

Features

Performance

Integration

5. Training System

Comprehensive Operator Training (55,000+ words)

Quick Start Path (30 minutes)

Comprehensive Certification (4-6 hours)

Supporting Materials

Delivery Methods

Current Operational Status

Production-Ready Components

✅ Agent System (100% Operational)

✅ Command System (100% Operational)

✅ Skills Library (100% Operational)

✅ MEMORY-CONTEXT System (100% Operational)

✅ Training System (100% Operational)

✅ Installation System (100% Operational)

✅ Documentation (100% Current)

Operational Gaps (Roadmap to 100% Autonomy)

❌ Inter-Agent Communication (Critical P0)

❌ Agent Discovery Service (Critical P0)

❌ Task Queue Manager (Critical P0)

❌ Resilience Patterns (High P1)

❌ Distributed State Management (High P1)

❌ Comprehensive Testing (High P1)

❌ Production Monitoring (Medium P1)

❌ Deployment Automation (Medium P2)

Hooks Implementation Framework (NEW Nov 22 - PHASE 1A & 1B COMPLETE)

Comprehensive 3-Command Workflow

Deliverables Created

Phase 1A: Quick Wins - COMPLETE ✅ (2 weeks)

Phase 1B: Quality Automation - COMPLETE ✅ (2 weeks)

Phase 2: Advanced Features - COMPLETE ✅ (2 weeks)

Phase 3: Production Hardening - COMPLETE ✅ (1 week)

Implementation Results

Expected Benefits (All Phases 1-3)

Success Criteria

Hooks Configuration

Related Documentation

Implementation Roadmap