evolving-ai-agents
Evolving AI Agents with A-Evolve
Overview
A-Evolve is universal infrastructure for evolving any AI agent across any domain using any evolution algorithm with zero manual engineering. It represents all evolvable agent state as files (prompts, skills, memory, tools), runs iterative solve-observe-evolve cycles against benchmarks, and uses LLM-driven mutation to improve agent performance automatically.
Benchmark results (Claude Opus 4.6):
- MCP-Atlas: 79.4% (#1)
- SWE-bench Verified: 76.8% (~#5)
- Terminal-Bench 2.0: 76.5% (~#7)
- SkillsBench: 34.9% (#2)
When to Use A-Evolve
Use A-Evolve when:
- Optimizing agent prompts, skills, or memory against a measurable benchmark
- Building self-improving agents with automated gating and rollback
- Evolving domain-specific tool usage and procedures through LLM-driven mutation
- Running iterative solve-observe-evolve loops to maximize agent performance
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