evolving-ai-agents

You can improve AI agent performance by running repeated solve-observe-evolve cycles against benchmark feedback, mutating prompts, skills, and memory files based on deterministic scoring to systematically optimize the agent workspace.

Benchmark-driven agent evolution is a process that uses scored task batches to discover what actually improves agent performance over time by mutating prompts, skills, memory, and tools against benchmark feedback.

You can automate prompt optimization by applying an EvolutionEngine to read observations, update git-tracked workspace files like YAML manifests, and reload agent state safely to refine prompts based on benchmark feedback.

Yes, agent evolution works with git-tracked workspace files by mutating prompts, skills, memory, and tools as plain files so every change is reviewable and reversible within the git workspace.

To run an agent evolution loop, you need a BaseAgent solve loop, a BenchmarkAdapter with deterministic scoring, and an EvolutionEngine that can read observations, update workspace files, and reload state safely.

You should use benchmark feedback to evolve agents instead of manual tuning when you need a repeatable optimize-observe-refine workflow to reduce ad hoc debugging and systematically turn failed tasks into actionable evolution steps.