01.1
Permissions are not verification
Access controls and approval rules decide authority over data. They never ask whether the model's inference is coherent, grounded, or free of overgeneralization.
FineSchema is a deterministic verification gate for agentic AI. When an agent proposes an action, FineSchema checks whether the reasoning is sound — before anything is written back to your systems of record.
Modern AI platforms govern who can act, on what data, with which permissions — and they log data lineage. But the reasoning itself — the inference from data to recommendation — stays probabilistic, non-reproducible, and unchecked at runtime.
Access controls and approval rules decide authority over data. They never ask whether the model's inference is coherent, grounded, or free of overgeneralization.
You can trace where a number came from. You cannot reproduce why the agent recommended an irreversible action — the rationale is a black-box, run-to-run variable.
Language models are built to be helpful. Under ambiguity they still produce a confident output — and an agent will act on it.
When an agent freezes an account, deletes a record, or administers a dose, "review it later" is not a control. The check has to happen before the write-back.
FineSchema does not generate answers. It verifies whether a judgment is formally legitimate, checking it against a deterministic schema of valid inference built on Kant's twelve categories. CPU-only. No model weights. Same input, same output, every time. When reasoning is incoherent, overgeneralized, or ungrounded, it fails closed.
The reasoning is sound and within policy. The action proceeds.
Borderline or unsupported. The action is staged for a mandatory human checkpoint.
A formal defect or policy violation. The write-back is refused and the reason is recorded.
FineSchema sits in the action path. The agent proposes; FineSchema returns a deterministic verdict and a replayable audit trace; only verified actions execute.
Non-deterministic models cannot be formally verified or certified. A deterministic gate can be specified, tested, and accredited — the same component, byte-for-byte, in the cloud or inside a classified enclave.
Wherever autonomous AI writes back to systems of record, FineSchema gives the reasoning a deterministic, accountable checkpoint.
Air-gapped, deterministic, accreditable. A reasoning gate you can specify and test — not a black box you have to trust.
Block a contraindicated order or an unstable-patient discharge before it executes; route the borderline to a clinician.
Stop an account freeze, a fund movement, or a refund built on flawed inference — and prove why, on demand.
Every automated decision carries a reproducible reasoning record an auditor or regulator can replay.
FineSchema runs between your agent — Palantir AIP, a custom orchestrator, or any agentic platform — and your systems of record. Compile your action policy from your ontology; the engine verifies every proposal before it commits.
A stateless HTTP service. One call — /verify-action — returns the verdict and an audit trace.
Packaged as a function inside your platform, invoked in the action submission path.
CPU-only, no network, no model weights — for classified and offline environments.
Tell us about your agent stack and the actions you need to govern. We'll set up a pilot on one action family — your policy, your environment.