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Semantic Agent Harness

Stop AI agents from doing things they shouldn't — before they do them.

When an agent acts on your behalf, the GRL semantic agent harness blocks any action that would breach your regulations, policies, or governance rules — in under a second, with the exact policy citation attached.

Book a 30-Minute Demo Talk About a Design Partnership
The New Problem

When the counterparty is an agent, the old controls stop working.

Traditional compliance was designed around humans. Audit trails, sign-offs, sample reviews, four-eyes checks. None of that works when an autonomous AI agent is the one executing a transaction, drafting a report, or moving capital on your behalf.

The questions you can no longer answer with the systems you have:

  • Did the agent have the authority to take that action?
  • Did the agent's reasoning comply with the rule it was trying to apply?
  • If something goes wrong, who is liable - and what evidence can you show the regulator?

The standard answers - guardrails baked into the LLM, after-the-fact review, an LLM judge watching another LLM - give you the comfort of supervision without the certainty of compliance. They fail the same way the underlying model does: probabilistically.

The Semantic Agent Harness solves the problem structurally instead.

What Happens at Runtime

Every agent action is checked against the same regulation graph the rule lives in.

  1. The agent proposes an action.

    Book a transaction, send a notification, change a record, file a report.

  2. The harness checks the proposal against the regulation graph.

    Operational resilience controls, AML rules, internal policies, segregation of duties — whatever you've encoded.

  3. If the proposal would breach a rule, the harness blocks it before it executes - in under a second.

    No override path, no review queue.

  4. The agent gets back the exact regulatory paragraph it would have breached.

    Not a probability score. Not a "low confidence" warning. The actual citation.

  5. Every action the agent does take is recorded as a provenance-tracked entity.

    What was attempted, what was allowed, what was blocked, against which rule, at what time, for which transaction.

The agent literally cannot act outside the boundaries you've declared. There is no override path, no "but the LLM said it was fine," no need to trust the model's interpretation. The harness enforces the rules; the agent operates inside them.

Demo

See an agent fail visibly, against a real regulation.

Book a live 30-minute walkthrough.

We'll show you an AI agent attempting a CPS 230 operational resilience violation, the harness blocking it in under a second, and the exact regulatory citation surfacing — running against a real APRA control, not slideware.

Book a Walkthrough
Who It's For

Built for the roles that own AI agent risk in regulated organisations.

  • Head of Operational Resilience Provable enforcement of CPS 230, DORA, or equivalent operational controls whenever agents touch critical operations.
  • Chief Compliance Officer Every agent action evidenced against the rule it was checked against, citation attached.
  • Chief Risk Officer Agent risk moves from "we trust the model" to "the action was structurally impossible."
  • Chief Information Security Officer Agents can only access and act on what the ontology says they may, governed by the same access model that governs human users.
  • Head of Internal Audit Complete, queryable provenance for every agent decision. No sampling required.

If your bank, insurer, or regulated organisation is deploying AI agents into operations and the question "how do we prove it's compliant?" has been answered with "we'll watch it carefully," the Semantic Agent Harness is the answer you've been looking for.

If you're deploying AI agents into a regulated operating environment and need to prove they cannot do the wrong thing - let's have a conversation.

Book a 30-Minute Call
hello@graphresearchlabs.com