agent-introspection-debugging

To debug an agent stuck in a tool calling loop, you can apply a structured introspection workflow that captures the failure, diagnoses the root cause, applies contained recovery, and generates an audit report.

Structured introspection for failing agent runs is a deterministic debugging process divided into four phases: failure capture, root-cause diagnosis, contained recovery, and an introspection report to ensure safe and auditable recovery.

You can diagnose an agent drifting from its goals by initiating an introspection workflow that captures the failure details and applies root-cause diagnosis before attempting contained recovery actions.

Yes, agent introspection integrates with ECC to align recovery actions with verification loops and continuous-learning processes, improving long-term agent reliability while ensuring safe and auditable recovery.

Diagnosing agent failures with introspection produces a human-readable debug report that documents the original failure, root-cause diagnosis, recovery actions taken, and final results for audit and learning purposes.

You should use a structured self-debug workflow when your AI agents fail repeatedly, loop on tools, or drift from the task, enabling systematic failure capture and contained recovery before escalation.