Agentic AI governance
Govern autonomous AI agents before the action, not after.
Agentic AI governance has to operate where the agent decides. ETOS GateKeeper gives AI agents a governed gate: authority verified, evidence bound, policy tested, outcome recorded — returned as deterministic JSON the agent must respect.
The problem
Why AI agent governance breaks conventional controls.
Controls designed for human-in-the-loop review assume a human pause. Autonomous agents remove it.
Agents act, not advise
An autonomous agent does not stop at a recommendation. Governance that depends on a human reading the output before acting no longer applies.
Authority is assumed
Agents inherit credentials and act inside them. Without an explicit check, nothing establishes that the specific action was ever authorised.
Evidence is generated, not cited
An agent can produce a fluent justification with no source behind it. Assurance requires named, current, relevant evidence per assertion.
Chains hide the gap
Multi-step agent chains bury unmet requirements several hops from the action. Gaps have to be surfaced at the decision point, not after.
The controls
What agentic governance looks like in practice.
Governance an agent cannot talk its way past, because the outcome is a state rather than a sentence.
Registered agents
Each agent is registered to an approved organisation with its own credentials, so every call is attributable.
Agent approval controls
The requested action is tested against the authority actually established in the record before execution is cleared.
Deterministic decision state
Agents receive a machine-readable governed outcome, including insufficient evidence, rather than prose to interpret.
Recorded and metered
Every assurance output produces an execution reference and consumes exactly one assurance credit. Blocked executions are not billable.
Related
Assurance for agents, end to end.
Controlled enterprise access, not an open API. ETOS does not sell favourable decisions.
Put a gate in front of your agents.
We will register a test agent, run your scenario, and show the governed decision state the agent receives — including the case where the evidence does not support the action.