self-improve

Autonomous code improvement orchestrates multi-agent experiments where multiple candidate plans are generated, tested against benchmarks, and the superior change is selected for merging. This tournament-style evaluation ensures only validated modifications improve the codebase.

You automate end-to-end codebase optimization by configuring your target repository and goal, then starting a self-improvement loop. The loop handles goal clarification, benchmarking, planning, execution, evaluation, and recording automatically across multiple languages and repos.

Yes, multi-agent orchestration applies to software projects needing iterative optimization with strict guardrails. The process enforces safety via harness rules, sealed-files checks, and trust gates to prevent unsafe modifications during the automated improvement loop.

The best way to evaluate multi-agent code changes is tournament-based selection. Multiple candidate plans undergo benchmarking and validation, and only the highest-performing change is merged after passing strict trust gates and verification.

Benchmarking results, iteration histories, and progress visuals are stored for analysis during automated codebase optimization. This traceable history records each step from goal clarification to merge decision-making for ongoing evaluation.

You should avoid autonomous self-improvement loops when your codebase lacks clear benchmarking criteria or strict guardrails. The process requires defined goals, sealed files, and trust gates to safely prevent unsafe modifications during automated multi-agent execution.