Diagnosis Before Recovery: Turning Agent Failures into Selective Self-Correction

TL;DR - DARC is a diagnosis-guided recovery framework that identifies task-specific agent failure modes before deployment, then selectively applies compatible correction strategies. It improves task performance while reducing environment steps or retrieval costs compared with base agents and broad recovery playbooks.

• Profiles development-set failures to determine which recovery interventions are admissible.
• Prunes mismatched strategies and freezes a verifier-selected success-cost policy for test-time use.
• Produces task-specific recovery mechanisms for action validity, procedural failures, and strict formatting.
• Demonstrated improvements across ALFWorld, AppWorld, and XBRL Finance.