Self-Improve

Optimization SICA-inspired

Implements a self-improvement loop for benchmarking and iteratively enhancing agent capabilities through A/B testing and performance analysis.

Overview

The Self-Improve Agent implements a continuous improvement cycle inspired by SICA (Self-Improving Coding Agent) research. It enables agents to:

Benchmark Performance: Measure success rates on standardized tasks
Identify Weaknesses: Analyze failure patterns and edge cases
Generate Improvements: Propose prompt or configuration changes
A/B Test Changes: Compare variants on benchmark suite
Promote Winners: Deploy improvements that pass validation

Improvement Cycle

┌─────────────┐
│  BENCHMARK  │◀────────────────────────────────┐
│   (Measure) │                                 │
└──────┬──────┘                                 │
       │                                        │
       ▼                                        │
┌─────────────┐                                 │
│   ANALYZE   │                                 │
│  (Find gaps)│                                 │
└──────┬──────┘                                 │
       │                                        │
       ▼                                        │
┌─────────────┐     ┌─────────────┐    ┌───────┴───────┐
│  GENERATE   │────▶│   A/B TEST  │───▶│    PROMOTE    │
│ (Variants)  │     │  (Compare)  │    │   (Deploy)    │
└─────────────┘     └─────────────┘    └───────────────┘

Benchmark Suite

Category	Tests	Metrics
Code Generation	50 tasks	Pass rate, correctness, style
Bug Fixing	30 tasks	Fix rate, regression rate
Refactoring	20 tasks	Quality improvement, correctness
Documentation	25 tasks	Completeness, accuracy

Commands

/self-improve benchmark

/self-improve benchmark auto-code

Running benchmark suite for auto-code...

Results:
Category        | Pass Rate | Baseline | Delta
----------------|-----------|----------|-------
Code Generation | 82%       | 78%      | +4%
Bug Fixing      | 76%       | 75%      | +1%
Refactoring     | 71%       | 68%      | +3%
Documentation   | 88%       | 85%      | +3%

Overall: 79.25% (Baseline: 76.5%, +2.75%)

/self-improve analyze

/self-improve analyze auto-code

Analyzing failure patterns...

Top Failure Modes:
1. Complex async patterns (12 failures)
   - Recommendation: Add async handling examples to prompt
   
2. Edge case handling (8 failures)
   - Recommendation: Emphasize boundary conditions
   
3. Test coverage gaps (6 failures)
   - Recommendation: Include test generation checklist

/self-improve test-variant

/self-improve test-variant auto-code variant_async_v2

Testing variant against current...

Current:  79.25% (125 tasks)
Variant:  83.20% (125 tasks)

Improvement: +3.95% (statistically significant, p<0.05)
Recommendation: PROMOTE variant_async_v2

Integration Points

System	Integration
Episode	Records benchmark results as episodes
Learning	Stores improvement insights
Benchmark	Uses memory benchmark system
All Agents	Can optimize any agent's configuration

Safety Guardrails

Regression Prevention: Variants must pass all existing tests
Statistical Significance: Improvements must exceed noise threshold
Human Review: Major changes require approval before deployment
Rollback Capability: Previous versions preserved for quick revert

← All Agents Checkpoint Agent →