What is execution control for AI agents?

Execution control asks whether this action, from this agent, for this user and this task, should run now. It needs an enforcement point in the execution path. A log collected after an action can help an investigation, but it cannot stop that action retroactively.

Ackuity is the Agent Execution Control Switch AI builders add to their agents, verifying every action before it runs. Its Agent Security Context Graph gathers the context used for each decision outside the agent.

A refund agent may be authorized to use a payments API. That permission does not establish that a particular refund should go to a different bank account. An execution decision checks the user, delegated task, target and prior actions before the proposed payment runs.

The response can be Allow, Constrain, Human in the loop, Block or Terminate. Continue authorized work only while the agent and task remain trustworthy. This is an illustrative scenario, not a customer result.

Identity controls establish who may access a resource. A sandbox limits the environment an agent can reach. Model guardrails inspect model input and output. Execution control checks a proposed action at an enforceable boundary. These controls can overlap and complement one another; deployment determines actual coverage.

Read the comparison of agent security approaches and the AI agent kill-switch guide.

Ask for supported tools and routes, a denied action with downstream evidence, an authorized action that completes, and a compromised-agent termination test. Test alternate routes, queued work, credentials and policy-service failure. Measure decision time separately from end-to-end overhead.

Begin with 30-day read-only trial access to review existing telemetry. The trial does not block actions; enforcement needs a separate integration and evaluation.