The gap

Platforms deploying autonomous AI agents generate candidate actions through probabilistic inference and then commit them. Governance is applied after generation — post-hoc guardrails, output filters, and safety wrappers that operate on completed outputs. By the time a wrapper inspects an output, the invalid semantic transition that produced it has already been committed.

Operating after the fact, those mechanisms cannot enforce policy compliance, lineage continuity, or semantic admissibility at the moment a transition is made. They cannot gate execution at the structural level where the decision actually occurs. Invalid transitions are detected retrospectively, if at all — never prevented at the point of commitment.

The invention

Inference-time execution control places a semantic admissibility gate inside the inference loop itself. Each candidate transition is evaluated against the agent's persistent semantic state — policy constraints, lineage continuity, entropy bounds, and trust-slope validation — before any commitment occurs. A transition that fails the gate is structurally prevented rather than allowed to commit and be cleaned up afterward.

Because the gate sits before commitment, every admitted transition carries deterministic lineage recording and auditable decision provenance as a byproduct of how it was admitted. The mechanism is model-agnostic: it governs the transition between inference steps, so it operates over heterogeneous models without depending on any single model internals.

The inventive step

The departure from prior art is when governance acts. Existing approaches evaluate completed outputs; this evaluates candidate transitions pre-commit, inside the loop, against persistent agent state. The distinction is structural rather than additive — admissibility is a precondition of commitment, not a filter layered over the result.

That pre-generation positioning is what makes the guarantees possible: invalid transitions cannot occur instead of being caught after they occur, lineage is continuous because each step is admitted against the last, and entropy and trust-slope bounds are enforced at the step where they are still enforceable. None of these follow from post-generation filtering; each follows directly from gating the transition itself.

Alone, and in composition

On its own, inference-time execution control is a governance primitive that any commercial AI platform deploying autonomous agents can adopt — an admissibility gate that closes structural exposure between generation and commitment, applicable across enterprise LLM governance, regulated-output generation, and edge inference.

In composition, it is the enforcement layer the wider architecture relies on. It gates transitions on the canonical agent object the schema defines, reads agent state to judge admissibility, and records the governed lineage downstream layers verify. Where other layers define what an agent is and what it may be trusted to do, inference control is where those determinations are enforced at runtime.