Inference Deployment Embodiments

Mechanism

Across all four embodiments, the gate consumes a structured candidate descriptor - the proposed inference step, the current semantic state digest, the active policy reference, the agent's capability tier, and the trust-slope trajectory - and produces one of three outcomes: admit, reject, or decompose. Admit causes the candidate to be committed to semantic state and to lineage as a constructive transition. Reject causes the candidate to be discarded and recorded as a rejection event that does not contaminate semantic state. Decompose causes the candidate to be rewritten into a sequence of smaller candidates, each of which is then re-presented to the gate.

In the sidecar embodiment, the gate runs as an out-of-process service co-scheduled with the inference engine. The engine emits each candidate over a local transport, blocks until the gate responds, and proceeds only on admit. Isolation is strong - the gate cannot be subverted by a compromised inference process - but the per-step latency cost is the round-trip across the transport.

In the inline-filter embodiment, the gate is linked into the inference engine itself and invoked as a function call at each transition point. Latency overhead is minimal, but isolation depends on the integrity of the engine binary; a compromised engine could in principle bypass the gate. Inline deployment is appropriate where the engine is trusted by construction (signed runtime, attested hardware) and where step latency is the binding constraint.

In the pre-flight gate embodiment, the gate runs once per request, before the inference engine is invoked at all. It evaluates the prompt, the requested model, the declared intent, and the capability tier, and either admits the entire request or rejects it. Pre-flight deployment is the cheapest form and is appropriate where the unit of governance is the request rather than the step, for example where downstream models are themselves trusted black boxes whose internal trajectories cannot be observed.

In the post-flight audit embodiment, the gate runs after inference is complete, against the full lineage of committed transitions. It cannot prevent generation, but it produces an authoritative governance verdict that may revoke, quarantine, or annotate the output before it is released. Post-flight is appropriate as a compliance backstop, as a learning signal for policy refinement, and as the only available form when the inference engine is operated by a third party who will not host an inline or sidecar gate.

The structural distinction across the four forms is the trust boundary between the gate and the engine and the temporal relationship between gate evaluation and effect commitment. Sidecar interposes a process boundary; inline collapses it; pre-flight evaluates before any effect; post-flight evaluates after every effect. Once a deployment selects a position on these two axes, every other property of the embodiment - latency, isolation, failure mode, attestation requirement - follows from the selection. The disclosure treats the four canonical forms as named points on a continuous design space rather than as a closed enumeration.

Operating Parameters

Each embodiment exposes a common parameter surface and an embodiment-specific parameter surface. The common surface includes the policy reference (the declarative document the gate evaluates against), the capability-tier mapping (which tiers may invoke which models for which intents), the rejection-event format (what is recorded when a candidate is refused), and the decompose ceiling (the maximum recursion depth before a decomposed candidate must be admitted or rejected outright).

The sidecar surface adds the transport configuration, the per-step timeout beyond which the engine treats the gate as unavailable and falls back to a configured default (typically reject-all), and the gate-restart policy. The inline surface adds the binary-integrity attestation requirements and the in-process panic policy. The pre-flight surface adds the prompt-classification model and the request-level rate limits. The post-flight surface adds the audit cadence (per-output, sampled, or batch), the output-quarantine policy, and the lineage retention horizon.

Crucially, a single deployment may run more than one embodiment simultaneously - a pre-flight gate to refuse obviously inadmissible requests cheaply, an inline filter to govern step-by-step trajectory, and a post-flight audit to provide an independent compliance record. The embodiments are not mutually exclusive; they are points on a defence-in-depth spectrum.

Failure-mode parameters are similarly common across embodiments but tuned per form. Each gate selects a default outcome for the case in which evaluation is structurally unable to complete - timeout, dependency unavailability, policy compilation failure - and the choice of default is itself a governance decision recorded in policy. High-assurance deployments configure fail-closed defaults, treating evaluation failure as rejection; throughput-bound deployments may configure fail-open defaults with mandatory rejection-event recording so that the inability to evaluate is itself made auditable.

Alternative Embodiments

Beyond the four canonical forms, the disclosure contemplates hardware-assisted embodiments in which the gate is implemented in a trusted execution environment or on a dedicated accelerator co-located with the inference hardware. This form combines the latency profile of inline deployment with the isolation profile of sidecar deployment, at the cost of hardware specificity.

A federated embodiment distributes the gate across multiple administrative domains: a local gate enforces tenant policy, an upstream gate enforces platform policy, and a regulator-operated gate enforces jurisdictional policy. Each gate emits its own lineage record, and admission requires all three to admit. A speculative embodiment runs the gate against candidate inferences that have not yet been generated, producing a pre-decision that the engine may rely on if the actual candidate matches the speculation, and falling back to synchronous evaluation otherwise.

A batched embodiment groups candidates within a short temporal window and evaluates them collectively, exploiting the fact that many candidates emitted by a single agent share most of their evaluation context. The gate amortises policy evaluation across the batch, producing per-candidate decisions at fractional cost while preserving the per-candidate decision granularity in lineage. A staged embodiment chains the canonical forms in series: a pre-flight gate produces a coarse admit-or-reject for the request, an inline filter governs the per-step trajectory, and a post-flight audit closes the loop with an independent verdict. Staged deployment is the structural form most often appropriate for regulated domains in which no single embodiment provides sufficient defence-in-depth.

Composition with the Inference-Control Stack

Whichever embodiment is selected, the gate composes with the lineage subsystem (which persists every admit, reject, and decompose event), with the semantic-state manager (which the gate consults and which it updates only on admit), with the policy compiler (which converts declarative governance documents into the gate's internal evaluation form), and with the capability-tier registry (which authenticates the agent making the request). The embodiment choice changes the transport and the trust boundary; it does not change the composition.

This compositional invariance is the structural value of treating deployment form as an embodiment dimension. A deployment that begins life as a pre-flight gate can be upgraded to an inline filter without changing the policy documents, the capability-tier matrix, or the lineage schema. The governance contract is preserved across the migration because the contract is defined at the level of the logical primitive, not the deployment form.

Prior-Art Distinctions

Content-moderation pipelines in conventional language-model deployments are typically post-flight only: the model generates freely, and a downstream filter suppresses outputs that match disallowed categories. This corresponds to a single embodiment of the present mechanism, and it is the weakest one - it cannot prevent the consumption of resources by problematic generations and cannot prevent semantic-state contamination in agentic settings where the generation itself updates a persistent state. Prompt-injection guards in retrieval-augmented systems correspond to a partial pre-flight embodiment but do not produce a structured admit-reject-decompose decision and do not record rejection events as first-class lineage.

Policy-enforcement points in service-mesh architectures share the sidecar topology but operate on network requests, not on inference transitions, and have no notion of semantic state, capability tier, or decomposition. Trusted-execution-environment based ML inference systems share the hardware-assisted topology but typically protect model weights and inputs, not the per-step admissibility of the inference itself. The combination of a single logical gate, four canonical deployment forms, and a uniform admit-reject-decompose contract across them is, to the best of the inventor's knowledge, not present in the prior art.

Disclosure Scope

The cognition patent discloses inference-control deployment embodiments as a structural family rather than a fixed implementation. The disclosure expressly contemplates that the same logical gate may be implemented in any combination of the canonical forms, in hardware-assisted form, in federated form, and in speculative form, and that a single deployment may evolve between these forms over its lifecycle without altering the governance contract.

The scope extends to embodiments in which the gate is itself the subject of governance - meta-gates that evaluate the gate's decisions against a higher-level policy - and to embodiments in which the gate's policy is updated dynamically based on rejection-event statistics produced by previous deployments. Across all such embodiments the invariants are the same: structured input, deterministic three-valued decision, lineage-persisted record, and capability-tier-aware authentication of the requesting agent.

The disclosure further contemplates application across modalities. The same gate structure governs text-token transitions, image-region generations, audio-frame emissions, and tool-invocation candidates within an agentic loop. The candidate descriptor's content varies by modality but its shape - structured, signed, capability-tier-stamped - does not. The disclosure also contemplates application across deployment scales, from single-tenant on-device inference, where the gate may be a small in-process function, to multi-tenant cloud inference, where the gate may be a clustered service handling millions of candidates per second, to regulated inference, where the gate may be a verified implementation whose binary attestation is itself part of the lineage record. The structural primitive is invariant under these scaling choices; only the engineering of the embodiment changes.