self-evolver

Continuous self-improvement orchestration automates agent system evolution by periodically evaluating agent health, matching tasks to capable agents, decomposing tasks into wave-based plans, and tracking decisions. This creates ongoing learning loops and deterministic workflow planning.

Automate task decomposition and routing by running modular scripts that generate wave-based execution plans with dependencies, evaluate agent capabilities, and assign pilot tasks to best-fit agents. Follow up by recording rationales with decision tracking scripts.

Yes, self-monitoring scripts periodically evaluate agent health, learning streaks, and coordination metrics across your AI agent system. They provide deterministic behavior through standard interfaces to maintain ongoing learning loops.

The best way to track decisions for post-mortem reviews is using dedicated decision tracking scripts that record rationales and outcomes. This ensures accountability and provides auditable logs for workflow planning and task routing evaluations.

Yes, agent matching automatically assigns tasks to the best-fit agent by evaluating agent capabilities and workload. It uses standard interfaces to ensure deterministic behavior when routing tasks across an AI agent system.

Automated task decomposition may not suit workflows requiring non-deterministic, ad-hoc execution rather than wave-based plans with dependencies. It is designed for structured ongoing learning loops and developer agent coordination rather than simple, single-step tasks.