Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents
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Abstract (arXiv)
Software engineering agents are increasingly deployed in evaluable engineering environments, yet post-failure recovery remains costly, manual, and ad hoc. Existing systems expose traces or generate follow-up feedback, but they do not convert heterogeneous runtime evidence into grounded, bounded recovery guidance for a subsequent attempt. We present PROBE, a failure-anchored framework for structured recovery in software engineering agents. PROBE organizes failed-run telemetry into structured evidence, structured diagnosis, and bounded recovery guidance through a Telemetry Layer, a Diagnosis Layer, and a Guidance Gate. The Telemetry Layer preserves fine-grained runtime signals, the Diagnosis Layer fuses cross-signal evidence into grounded diagnoses, and the Guidance Gate produces diagnosis-derived guidance only when it is evidence-grounded, actionable, and within the scope of agent-side behavior. We evaluate PROBE across three settings: repository-level software repair, enterprise workflow recovery, and AIOps service mitigation. On 257 initially unresolved cases, PROBE achieves 65.37% Top-1 diagnosis accuracy and a 21.79% recovery rate, outperforming the strongest non-PROBE baseline by 43.58 and 12.45 percentage points. The results reveal a diagnosis-recovery gap: accurate diagnosis is necessary but insufficient unless translated into bounded guidance that a subsequent attempt can execute and verify. Beyond controlled evaluation, a Microsoft IcM prototype shows that PROBE can attach as a non-intrusive side channel to existing service-diagnosis workflows without changing the agent policy, toolset, or execution budget. These results suggest that telemetry-grounded, failure-anchored recovery can improve post-failure recoverability under realistic engineering constraints.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 23 of its 50 references that are in the map. First posted on arXiv 9 May 2026.
Builds on
23 of 50 references.
References · 23
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11 more
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Citing papers per year: 2026: 4.
4 papers.
- 2026AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operationcited by 4
- 2026Repair or Resample? Rethinking Failure Debugging in LLM Multi-Agent Systemscited by 3
- 2026When Agentic Executions Fail: Detecting and Localizing Runtime Faults from Telemetrycited by 1
- 2026Terminal Agents: A Survey of AI Agents in Command-Line Environmentscited by 1
- 2026Repair or Resample? Rethinking Failure Debugging in LLM Multi-Agent Systems26 Aug 2026
- 2026Terminal Agents: A Survey of AI Agents in Command-Line Environments20 Aug 2026
- 2026When Agentic Executions Fail: Detecting and Localizing Runtime Faults from Telemetry4 Aug 2026
- 2026AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation7 Jul 2026
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- 2026Diagnosis Before Recovery: Turning Agent Failures into Selective Self-Correction
- 2026SWE-Gate: Passing Functional Tests Is Not Enough for Software Engineering Agents
- 2026ContraFix: Skill-Enhanced Contrastive Runtime Analysis for Vulnerability Repair
- 2025InspectCoder: Dynamic Analysis-Enabled Self Repair through interactive LLM-Debugger Collaboration
- 2026Evaluating Agentic Code Repair Capabilities in Distributed Systems
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