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This research identifies "empty commitments" in AI agents, where promises of future actions exceed the capabilities of the agent's current runtime or toolset. The study establishes a formal taxonomy for these failures and proposes measurement protocols to evaluate how different persistence affordances impact an agent's ability to honor its word. This work is critical for advancing reliable autonomous agents by addressing fundamental gaps in execution planning rather than just model accuracy.
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Empty Commitments: When Agents Promise What Their Runtime Cannot Deliver

arXiv2 min read

arXiv:2610.01045v1 Announce Type: new Abstract: A chatbot that says "I will remind you tomorrow" will not run again until the user writes. We call such a promise an empty commitment: a promise of an action after the current turn that nothing in the agent's tools or runtime can carry out. Unlike a broken promise, its emptiness follows from the agent's configuration alone; no later trajectory is needed. We define empty commitments on top of commitment semantics, with three failure types, an anchoring condition for promises that a tool could make real, and a response-level outcome taxonomy. We then describe a measurement protocol: follow-up requests run in five setups that add one persistence affordance at a time, with the environment either left implicit or stated.

Abstract:A chatbot that says "I will remind you tomorrow" will not run again until the user writes. We call such a promise an empty commitment: a promise of an action after the current turn that nothing in the agent's tools or runtime can carry out. Unlike a broken promise, its emptiness follows from the agent's configuration alone; no later trajectory is needed. We define empty commitments on top of commitment semantics, with three failure types, an anchoring condition for promises that a tool could make real, and a response-level outcome taxonomy. We then describe a measurement protocol: follow-up requests run in five setups that add one persistence affordance at a time, with the environment either left implicit or stated.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2610.01045 [cs.AI]

(or arXiv:2610.01045v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.01045 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 217 words

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