Safe Recovery of AI Agents in Enterprise Systems: Preventing Duplicate Actions and Preserving Human Corrections

Reliable use of AI agents in enterprise systems depends on what happens after an action fails or its outcome becomes uncertain. Retrying can duplicate an order, while reversing a workflow can invalidate a person’s subsequent correction. The central challenge is deciding whether recovery is justified by the available evidence and still satisfies the business request. We investigate this challenge in a reproducible testbed connecting order, inventory and billing services with different API guarantees. Controlled failures and concurrent edits reveal two evaluation gaps: protecting individual records does not ensure consistency across related services, and preserving an edit does not ensure that the final workflow fulfils its instruction. A separate diagnostic identified nine apparent completions that left human instructions unsatisfied. A conventional recovery implementation with equivalent safeguards matched the evaluated controller in all 270 paired runs, locating the observed benefit in shared engineering protections rather than a distinct recovery algorithm. On the prospective test configurations, both completed 108 of 180 runs without scored violations and left 72 for manual resolution. These findings provide a practical basis for evaluating agent integrations: verify uncertain effects, check related records, and distinguish completed business goals from unresolved handoffs. The contribution is a reproducible evaluation of these recovery requirements within a synthetic enterprise workflow, with operational effectiveness remaining to be established.