agent-introspection-debugging

Debug an AI agent stuck in a repeated tool call loop by capturing failures, context, and tool sequences to enable systematic root-cause diagnosis. This approach guides resolution within a contained recovery loop to stop non-progressing retries and resolve the workflow.

Agent introspection for failure analysis is a structured workflow that diagnoses AI agent failures through four phases: failure capture, root-cause diagnosis, contained recovery, and an introspection report. It systematically identifies why an agent drifts from objectives or exhausts tokens.

Find the root cause of prompt drift in agent workflows by applying a phase-driven failure analysis. Capture the failing session context and tool sequences to diagnose the underlying issue and produce a structured debug report suitable for human review or automated escalation.

This debugging workflow does not require external dependencies or components. It operates as a standalone phase-driven process using structured templates and guidance for escalation to diagnose failing agent runs that exhaust tokens or drift from objectives.

The best way to recover an agent from a non-progressing retry state is using a contained recovery loop. This phase-driven approach captures the failure context and generates an introspection report to guide structured resolution without uncontrolled escalation.

Generate an introspection report for an agent failure when workflows exhibit repeated tool calls, exhaust tokens, or drift from objectives. The report provides structured debug output suitable for human review or automated escalation after root-cause diagnosis.