[Submitted on 22 Oct 2026]
Title:Permission to Retry: A Study of Authorization and Recovery in AI Agent Workflows
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Abstract:A durable agent can resume a consequential action while its authority changes and the action's external outcome remains uncertain. We study execution-time reliability of tool-using AI agents, isolating recovery decisions from language-model behavior. Checkpoint state, permission to dispatch, evidence of an external effect and permission to reconcile are distinct obligations. We present a finite conformance study that checks these obligations through histories scored against a provider effect ledger hidden from the recovering application. A prospectively frozen campaign executes 336 histories on native LangGraph and Temporal hosts, crossing 14 schedules, three provider contracts and four matched application policies. All 168 paired host cells have identical checked outcomes. Per host, retained-ledger reconciliation passes all 42 histories: 21 checked completions, 15 denials and six unknown outcomes with reservations retained. Cached retry, revalidation before retry and stable-key retry pass 30, 36 and 31 histories respectively. A separate 16-case component study of InfinityOS, an enterprise AI platform developed by this http URL, exercises exported authority, worker, recovery and settlement code over repository fakes. The results isolate the roles of current authority, provider contracts and retained observations in recovery, with safety, completion and unresolved obligations reported separately for the declared application profile.
Comments: 7 pages, 2 figures, 3 tables
Subjects:
Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
Cite as: arXiv:2610.31916 [cs.AI]
(or arXiv:2610.31916v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.31916
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Deekshith Marla [view email] [v1] Thu, 22 Oct 2026 21:09:22 UTC (324 KB)
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View a PDF of the paper titled Permission to Retry: A Study of Authorization and Recovery in AI Agent Workflows, by Deekshith Marla and 1 other authors
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Ancillary files (details):
data/README.txt
data/component-outcomes.csv
data/history-outcomes.csv
data/policy-outcomes.csv
data/replacement-worker-times.csv
software-versions.json
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