Approval gate

An approval gate is the point where the agent stops and waits. It has prepared everything: the action it wants to take, the data it used, the policy rule it applied and why it believes the action is right. A person with the authority for that action sees this in a queue or an inline prompt, and approves, edits or rejects with a reason. Only then does the action execute, and the whole exchange is logged.

Approval gates are placed by action type and risk, not everywhere. A refund above a threshold, a credit decision, a message to a regulator, a change to a customer's contract, a clinical recommendation: these get gates. Routine actions inside policy do not, or the queue floods and people start approving without reading. The design of the approval interface, including what is shown, how long it takes and what happens on timeout, decides whether the gate is a control or a rubber stamp.

An approval gate is distinct from a policy gate: a policy gate is code applying rules automatically; an approval gate is a person. In practice they work together: policy gates pass the routine cases and route the rest to approval. It is also not a permanent state; approvals with consistently high agreement can be relaxed to sampling with evidence from the logs.

• Compliance officer: a named approver on high-stakes actions, with a record of what they saw and decided, is the control regulators recognise.
• Support manager: the queue design decides whether approvals take seconds or become a bottleneck that undoes the agent's benefit.
• CFO: gates on financial actions above a limit cap exposure while letting routine cases clear automatically.
• Founder / CEO: it lets you deploy agents on consequential work now, with autonomy expanded as approval logs show agreement.

Some actions are too costly, too regulated or too irreversible to automate on a model's judgement, but the work leading up to them, gathering data, checking rules and drafting the action, can be automated safely. Approval gates exist to capture that value: the agent does the preparation and a person makes the call. The failure they prevent is both the unreviewed high-stakes action and the fully manual process that ignores the agent's work. The trade-off is that gates cost human time and can become bottlenecks or rubber stamps; placing them by risk and designing the interface well is what keeps them meaningful.

• A refund workflow where refunds above a set amount wait for a team lead with the order history and policy check attached.
• A lending pipeline where the agent prepares a complete credit file and an underwriter approves or declines the recommendation.
• A hospital where an AI-drafted discharge summary is signed by the treating clinician before it enters the record.
• A SaaS copilot that asks the user to confirm before deleting records or changing billing plans.
• A logistics system where changes to priority shipments wait for a dispatcher's confirmation.
• A marketing pipeline where AI-generated campaign messages require a compliance reviewer's approval in regulated sectors.

Technique / practiceA workflow technique that Eazyware builds into every agent system alongside policy gates, including the approval queue, the context shown to the approver and the audit record. Delivered under AI agents and in-app copilot work.

Eazyware service that covers it: AI Agents & Automation. Starting prices are on the pricing page.