Human approval fails when it arrives too early, too late, or without enough context.
A person clicks “Approve” on a request they cannot properly inspect. The agent then acts on changing information. The control exists on paper, but it does not meaningfully control the action.
A better design is simple: let the agent prepare the work, but pause before it creates a commitment the business cannot easily reverse.
Consider an AI agent preparing a supplier payment. It can collect the invoice, match it to a purchase order, check the requested amount, and prepare the transaction. That work is low-risk and mostly reversible.
The payment itself is different. It moves money and creates an external commitment. That is where the agent should stop and show:
- What it plans to do
- Which systems and records it used
- Why the action crossed the approval threshold
- What could change if the action is delayed
- Who is authorized to approve it
The approval should be attached to that exact proposed action, not to a vague request such as “process this invoice.”
There is another detail teams often miss: context can change between preparation and execution.
The invoice may be updated. A user’s authority may be revoked. A policy may change. A connected account may show a new warning. If the agent executes the original plan without checking again, the human approval is stale.
That means approval needs two control points:
- A decision threshold
Define which actions require a person. Financial transfers, legal commitments, production changes, external communications, and reputationally sensitive actions are obvious candidates. The threshold should be based on impact, not on whether the agent feels confident.
- A final context check
Before execution, confirm that the proposed action still matches the approved request, the actor still has authority, and the relevant policy still allows it. If any of those conditions changed, send it back for review.
This is how human approval stops being a queue that slows every workflow. The agent handles preparation. The human owns the consequential decision.
The buyer test is straightforward:
Can your system show the reviewer exactly what the agent will do, why it is allowed, what changed since the request began, and who approved it?
If not, adding another approval button will not solve the problem. You need a control point with clear thresholds, visible context, recorded authority, and a re-check before the action leaves the system.
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