As AI agent systems increasingly execute rather than merely advise, ensuring the correctness of their actions becomes paramount. Specifically, when an AI agent queries regulated data, invokes effectual tools, and mutates persistent state, the plausibility of terminal output alone cannot guarantee correctness. The operative questions are: was each step authorized under a contract? Is the recorded history tamper-evident? Can the trajectory be reconstructed deterministically? We formalize this process as Proof of Execution (PoE).
An execution is defined as a triple $x = (C, T, R)$: a contract $C$, an Execution Causal Event Stream (ECES) $T$, and a replay context $R$. We construct the PoE validity predicate using a well-formedness predicate and five validator-checkable invariants. These five semantic guarantees describe authorization, path compliance, null effect on denial, history integrity, and replayability. We prove soundness under explicit cryptographic and deployment assumptions: any PPT adversary producing a PoE-valid execution that violates a semantic guarantee results in signature forgery, hash collision, or a quantified deployment-failure event.
The Prime Execution Model (PEM) separates planning, enforcement, effect, and recordkeeping into distinct authority planes; a lemma reduces trace completeness to Effector-exclusive credentialing. An Execution Attestation Certificate (EAC) is issued only when PoE = 1. In a single-node TypeScript prototype, PoE adds approximately 2.7 ms on minimal flows and 4.4% overhead on concurrent batch workloads; a standard eight-event trace compresses to about 1.1 KB; injected Gateway-bypass and trace-mutation attacks are rejected. PoE does not replace consensus, TEEs, or zkVMs; it binds authorization, effect, history, and replay into a single runtime-checkable object, rendering governed execution attestable under contract.
Blogger's Review: The Proof of Execution framework introduces a robust mechanism for validating AI agent actions, ensuring accountability and transparency. By formalizing the execution process and providing clear semantic guarantees, it effectively mitigates the risks associated with autonomous decision-making in AI systems. This innovative approach could significantly enhance trust in AI applications across regulated environments.