Runtime Control for AI Agents: Closing the Evaluation--Execution Gap in Operational Workflows
Language-model agents increasingly participate in operational workflows where approvals, binds, and payments create commitments.
ProofPaper ↗
Key points
- An approval valid when issued may no longer authorize execution after relevant facts, evidence, policy, or authority change.
- We call this the evaluation--execution gap.
- We specify a state-bound runtime assurance layer that separates probabilistic measurement from deterministic adjudication, binds an execution allowance to the decision-relevant basis of an approval, invalidates that allowance after relevant logged changes, and rechecks admissibility at dispatch.
- Under explicit interception, ingestion, dependency, and serialization assumptions, the model establishes dispatch-time validity within the registered execution surface.
Sources (1)
- [1]Runtime Control for AI Agents: Closing the Evaluation--Execution Gap in Operational WorkflowsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 01:46 AM
Language-model agents increasingly participate in operational workflows where approvals, binds, and payments create commitments.
An approval valid when issued may no longer authorize execution after relevant facts, evidence, policy, or authority change.
Extractive summary: sentences quoted from the sources.
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