The GRL Agent Harness doesn't just stop agents doing the wrong thing, it controls what they can access, what they can do and what they can remember - and records every step for internal audit and external regulators.
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:
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 GRL Agent Harness solves the problem structurally instead.
Every action an agent proposes is checked against the GRL regulation ontology - a declarative model of your rules that also controls what the agent may read and write. Breaches are blocked before they execute, with the rule cited. When there isn't enough data to answer, the harness returns null and signals the gap to a human. Everything is logged at evidential level for control teams and regulators.
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We'll show you an AI agent attempting control violation, the GRL Agent Harness tool blocks it with the exact regulatory citation surfacing — running against a real regulatory policy.
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