Self-Evolving Agent

🧬 Self-Improving AI Agents with Automatic Evolution

A cutting-edge research project exploring LLM-based self-improvement through automated prompt optimization and dynamic tool generation. The system combines Critic-Tuner for iterative prompt refinement with Automatic Tool Creation (ATC) to continuously enhance agent capabilities through multi-cycle training.

Research Overview

Core Innovation

This project demonstrates how AI agents can automatically improve their performance by:

• Evolving prompts through iterative Critic-Tuner cycles
• Creating custom tools from error pattern recognition
• Self-optimizing across training iterations with measurable improvement
• Multi-LLM orchestration for specialized subtasks

Key Contributions

• Automated prompt evolution system with version history tracking
• Dynamic tool generation engine that identifies failure patterns
• Comprehensive observability through Weave tracing integration
• Multi-provider LLM architecture with factory pattern design

Technical Architecture

Self-Evolution Pipeline

# Multi-Cycle Training Loop
1. Agent attempts tasks (GSM8K, MATH500 datasets)
2. Critic analyzes failures and suggests improvements
3. Prompt templates automatically evolve
4. ATC engine generates new tools from error patterns
5. Enhanced agent iteration begins

Core Components

Critic-Tuner System

• Failure Analysis: Identifies patterns in agent mistakes
• Prompt Optimization: Suggests template improvements
• Version Tracking: Maintains evolution history
• Convergence Detection: Monitors improvement plateaus

Automatic Tool Creation (ATC)

• Error Pattern Recognition: Analyzes common failure modes
• Tool Specification Generation: Creates tool requirements from patterns
• Sandbox Testing: Validates new tools before integration
• Dynamic Loading: Integrates tools into agent runtime

Implementation Details

Training Infrastructure

Dataset Support

• GSM8K: Grade school math problems for evaluation
• MATH500: Advanced mathematical reasoning tasks
• Automated Evaluation: Pipeline for performance measurement
• Continuous Benchmarking: Track improvement across iterations

Multi-Model Orchestration

# LLM Factory Pattern
- Google Gemini for primary reasoning
- OpenAI for specialized tasks
- Weights & Biases inference for experimentation
- Dynamic model switching based on task requirements

Evolution Mechanics

Prompt Template Evolution

• Initial Templates: Hand-crafted baseline prompts
• Critic Feedback: Analysis of reasoning failures
• Automatic Refinement: Template updates based on patterns
• Version History: Track all prompt iterations

Tool Generation Process

1. Pattern Detection: Identify recurring error types
2. Tool Specification: Generate requirements from patterns
3. Implementation: LLM creates tool code
4. Validation: Sandbox testing ensures correctness
5. Integration: Add to agent’s tool suite

Technology Stack

Key Features

Automated Self-Improvement

• No Human Intervention: Agent evolves autonomously through training cycles
• Measurable Progress: Quantitative metrics track improvement
• Failure-Driven Learning: Errors become opportunities for enhancement
• Continuous Evolution: No fixed endpoint, always improving

Dynamic Tool Creation

• Pattern-Based Generation: Tools created from real failure modes
• Automatic Integration: Seamless addition to agent capabilities
• Sandbox Safety: All tools validated before deployment
• Context-Aware: Tools tailored to specific problem types

Comprehensive Observability

• Weave Tracing: Complete visibility into agent behavior
• Wandb Metrics: Accuracy, performance, and progress tracking
• Sample Tables: Detailed analysis of agent responses
• Version Control: Full history of prompts and tools

Research Applications

Agent Development

• Automated agent enhancement without manual tuning
• Rapid prototyping of specialized agents
• Adaptive systems that improve from experience
• Self-optimizing AI for complex tasks

LLM Research

• Understanding prompt evolution dynamics
• Tool generation as emergent capability
• Multi-model orchestration strategies
• Self-improvement mechanisms in AI

Practical Applications

• Educational AI tutors that self-improve
• Customer service agents that evolve
• Problem-solving systems that adapt
• Research assistants that learn from usage

Experimental Results

Performance Improvements

• Baseline: Initial agent accuracy on benchmarks
• Post-Evolution: Measurable improvement across iterations
• Tool Impact: Quantified benefit of generated tools
• Convergence: Analysis of improvement saturation

Key Observations

• Prompt evolution shows consistent improvement
• Tool generation addresses specific failure modes
• Multi-LLM orchestration enhances capabilities
• Observability critical for understanding agent behavior

Future Directions

Research Extensions

• Multi-agent evolution with knowledge sharing
• Transfer learning across different task domains
• Meta-learning for faster initial evolution
• Hierarchical self-improvement architectures

Practical Enhancements

• Real-time evolution during deployment
• User feedback integration
• Domain-specific tool libraries
• Production-ready scaling

Repository

Project Status

Status: Active Research Focus: Self-improving AI systems Key Achievement: Successfully demonstrated automated agent evolution with measurable performance improvements