AI agent action control
Action control is the layer that decides whether a proposed agent action may execute. It runs outside the model, on business data and business rules.
What is AI agent action control?
AI agent action control is a layer between an AI agent and the tools it uses. It evaluates proposed actions against deterministic business policies and trusted system data before allowing, blocking or escalating the action.
Why action control is separate from the model
A language model produces the most likely next output. That is useful for reasoning and drafting. It is the wrong mechanism for deciding whether a 640 EUR refund is within policy.
Action control moves that decision into code and data. The same proposed action, with the same evidence, always returns the same decision.
What an action control layer checks
- Evidence: the current state of the records the action depends on
- Policy rules: what is permitted for this action type
- Thresholds: what the agent may do autonomously and what needs a human
- Duplicates: whether the same action or task already exists
- Permissions: which agent is asking and on whose behalf
- Conditions: timing, ownership, consent, contract terms
The decisions it returns
- ALLOW: the agent may execute the action
- BLOCK: a policy rule forbids the action
- REQUIRE APPROVAL: a named human has to decide
- NO ACTION: the action is unnecessary or already covered
- INSUFFICIENT EVIDENCE: required data is missing or stale
Where Gatewise fits
Gatewise is designed to sit between agent runtimes and the tools they use. The agent proposes an action, Gatewise returns a decision, and your application executes or stops.
Questions
Frequently asked questions
Give your agents freedom without giving them a blank cheque.
Tell us what your agents are allowed to do today. We will review whether Gatewise fits your agent workflow.
Works with your agents and existing business tools