agent-introspection-debugging

AI agent failure loops are debugged using a structured four-phase workflow: capture the failure state, perform root-cause diagnosis, apply contained recovery, and generate an introspection report.

Context drift occurs when an AI agent loses track of its operational state. Diagnose it through structured introspection that captures the failure state and performs root-cause analysis to identify the drift trigger.

Recover from repeated failed tool calls using contained recovery actions that apply non-destructive retries and evidence-based fixes, ensuring the agent safely escalates without causing system damage.

Use structured self-debugging when an AI agent hits maximum tool calls, experiences repeated retries, or suffers from context drift, requiring failure capture and root-cause analysis.

AI agent debugging requires generating an introspection report to document the failure capture, root-cause analysis, and contained recovery actions, providing a structured diagnostic record for human review.