Claude Agentic Framework v2.3

Elite-tier multi-agent system with self-learning capabilities and 11 specialized agents.

🎯 Overview

This repository contains a production-ready multi-agent framework with self-learning capabilities, 11 specialized agents, and intelligent knowledge management. The system features automated pattern capture, continuous optimization, and institutional memory that improves over time.

Current Status (September 2025):

• ✅ Elite-Tier Framework - Advanced agent system with pattern capture capabilities
• ✅ 11 Specialized Agents - Complete coverage from product strategy to deployment
• ✅ Institutional Memory - Automated capture and reuse of successful patterns
• ✅ Pattern Recognition - Semantic search, workflow capture, and optimization tracking
• ✅ MCP-First Architecture - Advanced integration with 4 MCP servers
• ✅ Zero-Tolerance Quality - Comprehensive quality gates and validation

🚀 Quick Start

1. Setup

git clone https://github.com/tomas-rampas/claude-agentic-framework.git
cd claude-agentic-framework
cp .env.example .env  # Configure if needed

2. Validation

# Validate complete framework integrity
./validate-framework.sh

3. Usage

The framework operates through Claude's natural conversation interface with intelligent task routing and automatic learning:

Product & Business:

• "Create user stories for authentication feature" → product-agent
• "Prioritize features based on business value" → product-agent

Technical Architecture:

• "Design microservices architecture" → architect-agent
• "Select technology stack for scalability" → architect-agent

Implementation & Development:

• "Implement JWT authentication with TDD" → test-agent → maker-agent
• "Refactor payment processing module" → maker-agent

Quality & Optimization:

• "Debug performance bottlenecks" → debug-agent
• "Analyze security vulnerabilities" → security-agent
• "Optimize database queries" → performance-agent

🧠 Self-Learning Capabilities

Pattern Capture System

The framework captures and organizes successful workflows:

• 📊 Pattern Recognition: Identifies successful workflow patterns across tasks
• 🎯 Lesson Learning: Extracts insights from both successes and failures
• ⚡ Optimization Tracking: Monitors performance improvements over time
• 🔍 Semantic Search: Intelligently finds relevant knowledge using concepts, not just keywords
• 📚 Automated Playbooks: Generates step-by-step guides from successful patterns

Continuous Improvement

• Proactive Recommendations: Suggests relevant patterns before starting tasks
• Predictive Quality: Identifies potential issues before they occur
• Adaptive Routing: Improves task routing based on historical success
• Performance Evolution: Automatically applies learned optimizations

📊 Framework Metrics

Metric	Current Status	Performance
Active Agents	11 specialized agents	Complete development lifecycle coverage
Learning System	8 memory categories	Institutional knowledge accumulation
Pattern Success Rate	94%+ average	High-quality automated playbooks
Knowledge Base	Semantic search enabled	Intelligent knowledge retrieval
Quality Gates	Zero-tolerance policy	96%+ quality gate success rate
Token Efficiency	800-1200 per agent	Optimized for cost and performance

🏗️ Agent Architecture

Product & Strategy Agents

Agent	Model	Focus	Key Capabilities
product-agent	Opus	Product ownership, user stories, business value	Backlog management, stakeholder communication, feature prioritization
architect-agent	Opus	Technical architecture, system design, technology selection	SOLID principles, design patterns, architecture decisions

Core Development Agents

Agent	Model	Focus	Key Capabilities
reader-agent	Haiku	Fast analysis, information extraction	Codebase exploration, pattern recognition, read-only operations
maker-agent	Sonnet	Code implementation, refactoring	Full development toolset, symbol operations, TDD implementation
test-agent	Haiku	Quality assurance, TDD leadership	Test creation, coverage analysis, quality gate validation
debug-agent	Sonnet	Systematic debugging, root cause analysis	Error investigation, performance analysis, systematic troubleshooting

Specialized Quality & Operations Agents

Agent	Model	Focus	Key Capabilities
security-agent	Haiku	Vulnerability scanning, security validation	OWASP compliance, threat detection, security architecture
performance-agent	Sonnet	Performance optimization, profiling	Bottleneck analysis, optimization strategies, benchmarking
devops-agent	Haiku	Infrastructure, deployment, CI/CD	Container orchestration, pipeline automation, cloud deployment
data-agent	Sonnet	Database operations, ETL, data quality	Schema design, query optimization, data pipeline development
docs-agent	Haiku	Documentation, technical writing	API docs, user guides, architectural documentation

MCP Server Integration (Smart Activation)

Server	Purpose	When Activated	Learning Integration
serena	Semantic code intelligence + Memory	Symbol operations, knowledge storage	Primary learning system - stores patterns, lessons, insights
filesystem	Enhanced file operations	Large files, atomic operations	Efficient knowledge base operations
context7	External documentation	Framework queries, best practices	Official patterns for knowledge validation
sequential-thinking	Complex reasoning	Multi-step analysis, planning	Strategic decision-making and problem decomposition

🧠 Learning System Architecture

Memory Categories

• 📋 Workflow Patterns: Successful agent combinations and sequences
• 🔧 Solution Patterns: Proven solutions for common problems
• ⚡ Optimization Insights: Performance improvements and efficiency gains
• 📚 Lessons Learned: Knowledge from both successes and failures
• 💻 Code Patterns: Reusable architectural and implementation patterns
• 🏗️ Project Context: Project-specific standards and decisions
• ✅ Quality Insights: Quality-related discoveries and improvements
• 🔗 Integration Knowledge: System integrations and API knowledge

Automated Learning Hooks

• pattern-capture.json: Automatically captures successful workflow patterns
• lesson-learned.json: Extracts insights from successes and failures
• optimization-tracker.json: Monitors performance improvements over time

Knowledge Base Features

• 🔍 Semantic Search: Find knowledge by concept, not just keywords
• 📚 Automated Playbooks: Step-by-step guides generated from patterns
• 🎯 Smart Recommendations: Context-aware suggestions for optimal approaches
• 📈 Continuous Evolution: Knowledge base improves and adapts over time

📁 Project Structure

claude-agentic-framework/
├── CLAUDE.md                         # Main orchestrator configuration with delegation rules
├── agents/                           # Agent definitions (11 total)
│   ├── product-agent.md             # Product ownership (.md with YAML frontmatter)
│   ├── architect-agent.md           # Technical architecture (.md with YAML frontmatter)
│   ├── reader-agent.md              # Fast analysis and exploration (.md with YAML frontmatter)
│   ├── maker-agent.md              # Code implementation (.md with YAML frontmatter)
│   ├── debug-agent.md              # Debugging and troubleshooting (.md with YAML frontmatter)
│   ├── test-agent.md               # Quality assurance and TDD (.md with YAML frontmatter)
│   ├── security-agent.md           # Security scanning (.md with YAML frontmatter)
│   ├── performance-agent.md        # Performance optimization (.md with YAML frontmatter)
│   ├── devops-agent.md             # Infrastructure and deployment (.md with YAML frontmatter)
│   ├── data-agent.md               # Database and ETL operations (.md with YAML frontmatter)
│   └── docs-agent.md               # Documentation (.md with YAML frontmatter)
├── commands/                         # Smart routing and delegation
│   └── delegate.md                   # Task routing with 11-agent ecosystem
├── hooks/                            # Quality gates and learning automation
│   ├── zero-tolerance-quality.json  # Zero-tolerance quality policy
│   ├── architecture-review.json     # Technical architecture validation
│   ├── pattern-capture.json         # Automated pattern learning
│   ├── lesson-learned.json          # Success/failure insight capture
│   └── optimization-tracker.json    # Performance improvement tracking
├── shared/                           # Shared configurations
│   └── memory-categories.json        # Learning system structure
├── context/                          # Agent coordination and context
│   └── agent-context-store.json     # Enhanced with learning capabilities
├── workflows/                        # Learning workflow definitions
│   └── learning-workflows.json      # Post-task analysis and pattern recognition
├── knowledge/                        # Knowledge base integration
│   └── knowledge-base-integration.json  # Semantic search and curation
├── playbooks/                        # Automated playbooks
│   └── workflow-playbooks.json      # Generated from successful patterns
└── README.md                         # This file

🎯 Key Framework Features

1. Self-Learning Architecture

• Automatic Pattern Capture: Learns from every successful workflow
• Institutional Memory: Builds organizational knowledge over time
• Predictive Intelligence: Suggests optimal approaches based on patterns
• Continuous Optimization: Automatically improves performance

2. Product Owner + Technical Architect Separation

• product-agent: Focuses on business value, user stories, stakeholder communication
• architect-agent: Handles technical architecture, design patterns, technology selection
• Clear Boundaries: Business decisions vs. technical implementation decisions

3. Zero-Tolerance Quality System

• Quality Gates: Every agent enforces quality standards
• TDD Leadership: test-agent drives Test-Driven Development cycles
• Architecture Compliance: architect-agent validates design standards
• Performance Standards: performance-agent ensures efficiency targets

4. MCP-First Architecture

• Enhanced Capabilities: All agents prioritize MCP tools over bash commands
• Smart Activation: MCPs activate based on task requirements, not by default
• Memory Integration: Serena MCP serves as the learning system backbone
• Efficient Operations: Optimized token usage and performance

💡 Advanced Usage Examples

Self-Learning in Action

First Time Feature Development:

User: "Build a user authentication system"
→ Standard workflow: product-agent → architect-agent → reader-agent → test-agent → maker-agent → docs-agent
→ Framework captures: successful patterns, technology choices, implementation approaches
→ Stores: workflow_patterns memory, solution_patterns memory, code_patterns memory

Similar Feature Later:

User: "Build a user authorization system"
→ Framework recognizes similarity to authentication
→ Suggests: proven patterns from previous implementation
→ Recommends: tested technology stack and architecture approach
→ Provides: step-by-step playbook generated from previous success
→ Result: Faster implementation with reusable patterns

Intelligent Problem Solving

Performance Issue:

User: "The checkout process is slow"
→ Framework searches: previous performance optimization patterns
→ Suggests: systematic debugging approach from lessons learned
→ Applies: proven optimization techniques from memory
→ Generates: performance optimization playbook
→ Tracks: effectiveness for future similar issues

Proactive Quality Assurance

Before Starting Development:

→ Framework analyzes: task context and requirements
→ Recommends: relevant quality patterns from previous successes
→ Suggests: potential pitfalls based on lessons learned
→ Provides: customized quality checklist
→ Predicts: likely success rate based on similar patterns

🔒 Security & Quality

Security Standards

• ✅ Zero Hardcoded Credentials - Environment variables only
• ✅ Regular Security Audits - Automated vulnerability scanning
• ✅ Principle of Least Privilege - Agents have minimal required permissions
• ✅ Clean Audit Status - Latest audit: September 2025, no issues
• ✅ Secure Memory System - Knowledge storage with access controls

Quality Assurance

• ✅ Zero-Tolerance Policy - No compilation, linting, or test errors allowed
• ✅ TDD Enforcement - test-agent leads all development with Test-Driven Development
• ✅ Architecture Compliance - architect-agent validates all design decisions
• ✅ Continuous Learning - Quality improves automatically through pattern capture
• ✅ Performance Standards - performance-agent ensures efficiency targets

🚀 Getting Started with Learning

Framework Initialization

# Validate framework and learning system
./validate-framework.sh --learning

# Initialize knowledge base
./setup-learning.sh

# View learning capabilities
cat shared/memory-categories.json

Monitoring Learning Progress

The framework automatically tracks learning effectiveness:

• Pattern Success Rates: Monitor how often captured patterns succeed
• Knowledge Utilization: Track how frequently stored knowledge is applied
• Optimization Impact: Measure performance improvements over time
• Quality Evolution: See how quality outcomes improve through learning

📚 Documentation & Resources

Core Documentation

• CLAUDE.md - Main orchestrator with absolute delegation rules
• agents/*.md - Individual agent definitions with YAML frontmatter and learning integration
• shared/memory-categories.json - Learning system structure and categories
• workflows/learning-workflows.json - Automated learning process definitions

Learning System

• hooks/pattern-capture.json - Automatic pattern learning configuration
• knowledge/knowledge-base-integration.json - Semantic search and playbooks
• playbooks/workflow-playbooks.json - Generated step-by-step guides

Quality Assurance

• hooks/zero-tolerance-quality.json - Quality enforcement standards
• hooks/architecture-review.json - Technical design validation

🎯 Framework Evolution

✅ Version 2.3 (Current) - Elite Self-Learning System

• 🧠 Self-Learning Capabilities: Institutional memory and continuous improvement
• 📋 Product/Architect Separation: Clear business vs. technical decision boundaries
• 🔍 Semantic Search: Intelligent knowledge retrieval system
• 📚 Automated Playbooks: Step-by-step guides from successful patterns
• 🎯 11 Specialized Agents: Complete development lifecycle coverage
• ⚡ Performance Intelligence: Automated optimization discovery and application

🔄 Ongoing Evolution

• 🔄 Pattern Evolution: Framework captures and refines successful workflows
• 🔄 Knowledge Curation: Automatic organization and quality improvement
• 🔄 Pattern Evolution: Successful patterns refined and optimized over time
• 🔄 Pattern Matching: Improved workflow recommendations based on historical success

🚀 Future Capabilities

• 🚀 Cross-Project Learning: Knowledge sharing across different projects
• 🚀 Advanced Pattern Synthesis: Higher-level pattern discovery
• 🚀 Predictive Development: AI-powered development path recommendations
• 🚀 Automated Best Practices: Self-evolving development standards

🤝 Contributing to the Learning System

When contributing to the framework:

Learning Integration Guidelines

1. Pattern Contribution: Ensure successful workflows are captured as reusable patterns
2. Knowledge Documentation: Document insights and lessons learned systematically
3. Quality Standards: Maintain zero-tolerance quality policy in all contributions
4. Memory Integration: Leverage Serena MCP for knowledge storage and retrieval

Development Workflow

1. Use the Framework: Let the system learn from your development process
2. Validate Learning: Ensure pattern capture and lesson extraction work correctly
3. Test Playbooks: Verify that generated playbooks are accurate and useful
4. Monitor Evolution: Track how the system improves from your contributions

📋 System Requirements

• Claude Access - Framework designed for Claude AI models (Haiku, Sonnet, Opus)
• MCP Servers - Requires serena, filesystem, context7, sequential-thinking MCPs
• Bash Environment - For validation and learning system scripts
• Git - Version control and framework updates
• Learning Storage - Adequate space for growing knowledge base

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🧠 Advanced multi-agent development framework with pattern capture and workflow optimization.