Noēsis

A cognitive runtime for observable, replayable, governable agent runs.

Noēsis is a cognitive runtime that makes AI agent runs traceable, replayable, and governable.

The problem

Enterprises want to deploy AI agents. But agents today are black boxes.

When an agent makes a mistake—gives bad advice, takes a wrong action, violates a policy—teams can't explain what happened. There's no audit trail. No way to replay the failure. No proof of what the system believed when it made that decision.

For regulated industries—healthcare, finance, legal—this isn't acceptable. For any production system, it's a liability.

The barrier to enterprise agent adoption isn't capability. It's trust.

The solution

Noēsis wraps any agent and treats every run as a structured cognitive episode.

Each episode has a unique ID, follows a fixed phase sequence, and produces a bundle of artifacts: the full event stream, the final state, metrics, and cryptographic hashes for verification.

Three things make this different:

Observable. Every decision the agent makes is logged with timestamps, causal links, and typed payloads. When something goes wrong, you don't guess—you trace.

Replayable. Artifacts are self-contained. Same inputs, same outputs, verified by hash. You can reproduce any failure exactly as it happened.

Governable. Governance isn't a prompt—it's a phase. Policy checks run before actions execute. Violations are flagged, logged, and can veto outputs before they reach users.

Why now

Agents are moving from demos to production. The companies deploying them are hitting the same wall: they can't debug failures, they can't prove compliance, they can't trust the system to behave consistently.

The infrastructure for running agents reliably doesn't exist yet. Noēsis is that infrastructure.

Who it's for

Teams shipping agents into environments where "it usually works" isn't good enough:

• →Regulated industries — healthcare, finance, legal—where audit and policy enforcement are requirements, not features
• →Enterprise AI teams — where debugging failures and measuring improvement over time is the difference between a pilot and a production system
• →AI safety and alignment research — where you need to study agent behavior systematically, not anecdotally

Where it stands

Noēsis is live, open source, and in active development. Early adopters welcome.

I'm also applying Noēsis to governance-heavy domains. Research paper forthcoming.