AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation

• URL: http://arxiv.org/abs/2607.06273v1
• Date: Tue, 07 Jul 2026 13:40:31 GMT
• Title: AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation
• Abstract summary: AgentTether is a run-time repair framework that automates post-run diagnosis and guided recovery without modifying the underlying agent or environment.<n>It localizes failure-critical subtrajectories by combining an offline normal-behavior model with a run-local graph detector.<n>It can be deployed as an offline diagnostic-and-guidance tool or as an online repair layer.
• Score: 27.0859707906163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model (LLM) agents are increasingly used for multi-step, stateful tool-use tasks, yet production reliability remains limited. Unlike static software repair, agent repair must recover dynamic trajectories whose early decisions can propagate into later errors and external state changes. Existing automatic remedies address only part of this problem: blind retry adds no diagnosis, outcome feedback says whether a run failed but not where or why, and self-reflection often lacks grounded evidence to prevent the same failure from recurring. We present AgentTether, a run-time repair framework that automates post-run diagnosis and guided recovery without modifying the underlying agent or environment. AgentTether abstracts each run into Transition Units, links them through a dependency-aware Critical Transition Graph, and localizes failure-critical subtrajectories by combining an offline normal-behavior model with a run-local graph detector. It then converts the localized cause into behavior-scoped guidance backed by cross-iteration Repair Memory, and can optionally apply guarded run-time intervention to keep the correction active during re-execution. The same design can be deployed as an offline diagnostic-and-guidance tool or as an online repair layer. We evaluate AgentTether on 261 tau-bench tasks across three domains with Qwen3.7-max, and test cross-model transfer on Banking with GPT-5.4. On the hardest Banking domain, AgentTether repairs 59.04% (49/83) of initially failed Qwen3.7-max tasks and 65.12% (56/86) of initially failed GPT-5.4 tasks. Overall, AgentTether improves repair effectiveness while reducing agent turns and end-to-end approach tokens, suggesting a practical reliability layer that can wrap existing agent deployments, reduce wasted re-execution, and improve recovery without retraining the agent.

Related papers

• Diagnosis Before Recovery: Turning Agent Failures into Selective Self-Correction [22.654396704495934]
  Development-set failures can recover part of the missing diagnostic substrate by deciding which recovery interventions are admissible.<n>We propose DARC, a diagnosis-guided recovery harness that profiles task-family failure modes, prunes mismatched interventions from a shared recovery library, and freezes a verifier-selected success-cost policy for deployment.
  arXiv  Detail & Related papers  (2026-08-12T08:14:45Z)
• FailForge: Distilling Procedural Competence from Persistent Failures into Code Agents [49.519184792774304]
  FailForge is an agentic framework that converts failed rollouts into training signal.<n>FailForge recovers over 26% of previously failed instances at marginal additional cost.
  arXiv  Detail & Related papers  (2026-08-09T08:22:57Z)
• ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step [74.83535434786145]
  We introduce ScrambleToolBench, an interactive terminal benchmark designed to isolate behavioral reasoning.<n>The benchmark requires agents to uncover hidden tool behaviors entirely through trial-and-error interaction.<n>Our evaluation of state-of-the-art language models reveals that successful initial discovery does not translate into robust adaptation.
  arXiv  Detail & Related papers  (2026-08-03T15:07:46Z)
• When Agents Do Not Stop: Uncovering Infinite Agentic Loops in LLM Agents [7.986500985812644]
  We call this problem Infinite Agentic Loops (IALs)<n>IALs arise from the interaction between agent logic, framework semantics, runtime observations, and termination mechanisms.<n>We propose IAL-Scan, a static analysis tool for detecting IAL failures in real-world LLM agent projects.
  arXiv  Detail & Related papers  (2026-07-02T03:14:27Z)
• Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems [60.79651380135334]
  Long-lived AI agents are increasingly deployed as persistent operational systems.<n>How long does an agent remain reliable after deployment?<n>We introduce AgingBench, a longitudinal reliability benchmark for agent lifespan engineering.
  arXiv  Detail & Related papers  (2026-05-25T19:55:12Z)
• On-Policy Self-Evolution via Failure Trajectories for Agentic Safety Alignment [54.30690671490447]
  Existing safety-alignment signals are largely response-level or off-policy.<n>FATE transforms verifier-scored failures into repair supervision without expert demonstrations.<n>FATE reduces attack success rate by 33.5%, harmful compliance by 82.6%, and improves external trajectory-safety diagnosis by 6.5%.
  arXiv  Detail & Related papers  (2026-05-12T09:56:28Z)
• Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents [26.16025682022058]
  PROBE is a failure-anchored framework for structured recovery in software engineering agents.<n>We evaluate PROBE across three settings: repositorylevel software repair, enterprise workflow recovery, and AIOps service mitigation.
  arXiv  Detail & Related papers  (2026-05-09T06:02:08Z)
• AgentDevel: Reframing Self-Evolving LLM Agents as Release Engineering [8.201374511929538]
  AgentDevel is a release engineering pipeline that iteratively runs the current agent.<n>It produces implementation-blind, symptom-level quality signals from execution traces.<n>It aggregates dominant symptom patterns and produces auditable engineering specifications.
  arXiv  Detail & Related papers  (2026-01-08T05:49:01Z)
• DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems [48.971606069204825]
  DoVer is an intervention-driven debug framework for large language model (LLM)-based multi-agent systems.<n>It augments hypothesis generation with active verification through targeted interventions.<n>DoVer flips 18-28% of failed trials into successes, achieves up to 16% milestone progress, and validates or refutes 30-60% of failure hypotheses.
  arXiv  Detail & Related papers  (2025-12-07T09:23:48Z)
• Alita-G: Self-Evolving Generative Agent for Agent Generation [54.49365835457433]
  We present ALITA-G, a framework that transforms a general-purpose agent into a domain expert.<n>In this framework, a generalist agent executes a curated suite of target-domain tasks.<n>It attains strong gains while reducing computation costs.
  arXiv  Detail & Related papers  (2025-10-27T17:59:14Z)
• Where LLM Agents Fail and How They can Learn From Failures [62.196870049524364]
  Large Language Model (LLM) agents have shown promise in solving complex, multi-step tasks.<n>They amplify vulnerability to cascading failures, where a single root-cause error propagates through subsequent decisions.<n>Current systems lack a framework that can comprehensively understand agent error in a modular and systemic way.<n>We introduce the AgentErrorTaxonomy, a modular classification of failure modes spanning memory, reflection, planning, action, and system-level operations.
  arXiv  Detail & Related papers  (2025-09-29T18:20:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.