One gate for every agent action
Every tool call, from every member of the organization, passes one policy gate: evaluated against your rules, allowed or denied, and written to an immutable audit ledger.
Governance in your organization — the story replays live. Click any other product to explore it.
AI adoption without control
- Every new agent widens the surface: credentials, network egress, customer data in prompts.
- Policy lives in documents that no model reads and no tool enforces.
- When auditors ask what your AI did, all you have is scattered logs and good intentions.
In an AI-Native org, governance is structural
- One gate stands before every agent action: policies evaluate inline, allow or deny.
- PII filtering, identity isolation, egress control, and cost gates are enforced, not suggested.
- Every outcome lands in an immutable ledger, with signed attestations you can hand to an auditor.
This is the difference between adding AI to your organization and being AI-Native: Governance is not a tool on the side — it is how the organization runs.
Adopt AI without signing up for the risk
Write one policy and watch the gate enforce it — that is the whole learning curve.
Get Started →Every prompt is an egress event. The uncomfortable fact of LLM adoption: prompts leave your boundary. Whatever an agent places in its context — including the customer record it helpfully fetched — travels to a provider's API. Without an enforcement point, nothing stands between a well-meaning agent and a data leak. The agent is not malicious; it is unguarded.
Policy in a PDF governs nothing. Models do not read your security policy, and tools do not obey documents. Enforcement has to be architectural — the same reason networks run firewalls instead of circulating memos. In an AI-Native org there is exactly one gate in front of every agent action: policies evaluate inline — who is asking, touching what, going where — PII is stripped from outbound payloads, and the action is allowed or denied before it runs.
One standard for the whole hybrid workforce. Cloud workers, developers' operated agents, autonomous members — all pass the same gate under the same rules. Trust is earned on a ladder, and some ceilings hold regardless of trust: the org decides which actions certain member kinds may never take. No member is unowned; every action attributes to someone accountable. Uniform governance is what makes a mixed human-and-AI org coherent instead of chaotic.
Regulators ask for evidence, not intentions. Frameworks like the EU AI Act and SOC 2 converge on the same demand: show what your systems did, and prove the record was not edited. Both outcomes at the gate — every allow and every deny — land in an immutable, hash-chained ledger, with signed attestations you can export and hand over. The audit becomes a download.
Cost is a governance problem too. An agent stuck in a retry loop can make thousands of LLM calls overnight — a bill that arrives as a surprise precisely because nothing was watching. Budgets and cost gates are enforced at the same gate as everything else, so autonomous work stays inside ceilings you set, and cannot exceed them quietly.
What Governance gives you
Policy Gateway
One enforcement point for every outbound call: filter, evaluate, allow or deny.
Access policies
Fine-grained rules over who and what can touch which resources, human or AI.
Identity isolation
Every member acts as itself; no shared credentials, no ambient authority.
Audit ledger & receipts
Immutable, hash-chained records of every allowed and denied action.
Attestations
Signed, chain-verified statements you can export and hand to an auditor.
Cost controls
Budgets and spend gates keep autonomous work inside the lines you set.
Put Governance to work
One gate for every agent action — nothing slips through unlogged.