self-improvement-protocol

To prevent agent performance degradation and state drift, implement a continuous self-correcting loop that tracks success rates and maintains system state across session lifecycles. This ensures agents remain production-grade and aligned with strategic goals.

A reflexion loop for autonomous agents is a structured framework for analyzing failures and logging patterns to ensure mistakes are never repeated. It provides outcome-based learning by systematically reviewing session-end mistakes.

Automatically detect and repair broken file references using a self-healing protocol that checks system integrity. It repairs stale pulse data and git drift to maintain operational continuity without manual intervention.

Yes, you need Supabase integration for trace logging to track agent performance metrics effectively. The protocol also requires local file system access for memory and pulse management to operate correctly.

Manage the promotion of probationary skills by systematically logging capability gaps and evaluating real-world outcomes. The protocol tracks skill activation metrics to validate performance and identify areas for capability development.