Evidence-Guided Recovery for AI Agents with Ambiguous Tool Outcomes and Partial Execution

Authors: Genliang Zhu , Xiaobo Chen , Ziyuan Wang , Chu Wang

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Abstract: Tool timeouts can leave AI agents facing ambiguous tool outcomes and partial execution: an action may have failed, committed or remained in flight while other workflow steps have already completed. We formulate evidence-guided task recovery over approved obligations, provider capabilities and states compatible with public evidence. A shared admissibility check handles key expiry, authorization timing, dependencies and residual effects; a terminal certificate separates actual goal satisfaction from publicly supported safe completion. The known-model implementation combines standard observation-contingent planning with a separately implemented effect-history adjudicator. In a finite study of 37 assignments, planning and three active controls each certify 28 completions; four saved synthetic fee units incur additional computation. A preregistered assignment-held-out comparison then evaluates planning advice against evidence prompting with two local model bundles. Across 58 paired units in 19 previously developed task families, advice certifies 30 completions versus 9, a 36.2-percentage-point difference (exact family sign-flip p=0.0078), using 272 proposals versus 399. All 116 trajectories pass independent replay. Both models exhibit a positive completion difference. The comparison measures the evaluated advice interface under a common shield; search-versus-rule advice requires a separate matched comparison. Unknown and handoff outcomes remain explicit, with guarantees tied to the declared provider capabilities rather than arbitrary third-party exactly-once execution.

Comments:

24 pages, 1 figure, and 10 tables. Includes a finite recovery model, model-facing confirmation study with two local-model bundles, independent replay, and an explicit provider-capability scope

Subjects:

Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.41453 [cs.AI]

(or

arXiv:2610.41453v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.41453

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Chu Wang [ view email ]

[v1]

Fri, 30 Oct 2026 08:55:55 UTC (36 KB)

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