self-improve

Automate continuous code improvement by orchestrating a configurable pool of agents that research, plan, benchmark, and implement changes inside your target repository. The autonomous loop safely validates plans and merges improvements within established guardrails.

Autonomous code optimization via an agent tournament is a process where multiple agents propose and validate codebase changes. Agents compete by researching, planning, and benchmarking improvements within isolated git branches to determine the best modifications.

Agent-based benchmarking ensures safety and traceability by isolating work in git branches and applying built-in stop conditions. It validates plans and results before merging, sealing critical artifacts to maintain determinism throughout the optimization lifecycle.

You can use automated agents to optimize code reliability and maintainability by iteratively proposing and validating changes within a real codebase. The orchestration handles end-to-end execution, benchmarking, and merging of these improvements.

Limitations of autonomous repository optimization include relying on configurable guardrails and stop conditions to prevent runaway processes. Work must be isolated in git branches and validated, meaning improperly configured benchmarks could yield ineffective improvements.

Track historical optimization results generated by autonomous agents using built-in state tracking and visualization features. These tools guide decisions by mapping the validated changes and benchmarking outcomes across iterative improvement cycles.