self-improving-agent

To identify performance bottlenecks in automation workflows, you can analyze skill performance, usage patterns, and failure signals to surface actionable bottlenecks. This process diagnoses inefficiencies in tool calls and generates prioritized improvement proposals.

Yes, you can automatically apply skill optimization proposals with rollback safeguards. The system orchestrates end-to-end improvement workflows and validates changes via build checks before applying them, ensuring safe automated application.

The best way to analyze failure patterns in skill libraries is to run an end-to-end analysis across the agent ecosystem. This detects inefficiencies in skill usage and tool calls, diagnosing bottlenecks to drive targeted improvements.

Yes, you need to run build checks before applying workflow optimization changes. The system validates all generated proposals via build checks to ensure reliability and performance improvements before any changes are applied.

Skill performance analysis may not generate actionable improvements if failure signals and usage patterns are not properly surfaced. Providing a target skill for deep analysis ensures the generation of prioritized, actionable optimization proposals.