Notes / Workers
When an AI worker is ready for your decision
How to define a useful handoff between an AI worker's review and the owner's next action.
By Edu Rigonato. Published Oct 12, 2026. 7 min read.
Imagine asking an AI worker to review a proposed change to your service page. It checks the copy, follows the links, and inspects the preview. Then it reports: “Ready.”
You open the page. One sentence promises a level of service your team has never formally committed to. The page works, but you still have a business decision to make.
What did “ready” mean? Was the worker finished checking the page? Had the promise been verified? Was someone expected to make the change live?
Those questions belong in the workflow. If the owner has to reconstruct them from the finished output, the handoff needs more work.
What does “ready” mean in this workflow?
For a managed AI workflow, a useful starting point is to define what the worker is preparing and which actions it may take.
A worker assigned to review website copy might be allowed to inspect a draft, compare it with supplied source material, flag unsupported statements, and prepare revisions. Its assignment can be complete when it presents that work to the owner.
The proposed page change may still need a decision.
Frequently asked questions
Does every step need a new approval?
Define routine actions and exceptions when designing the workflow. A worker should follow the authority already given for that task and return decisions that fall outside it to the responsible owner.
What if the evidence is missing?
Identify the source or fact that is unavailable, explain which conclusion depends on it, and prepare any useful work that remains within scope. Keep the affected decision open.
What if the proposed change is edited after review?
Assess whether the edit changes the decision's basis or permitted scope. Recheck the affected work before treating the earlier decision as applicable.
How do you know the next action was completed?
Agree on the completion check in advance. A recommendation, an authorization, and a verified result describe different stages of the workflow.
Evidence note. This article offers a practical framework for review handoffs. The service-page scenario is hypothetical. It illustrates how to describe evidence, decisions, and authority; it does not report client outcomes or measured performance.
For implementation context, Anthropic describes human checkpoints and environmental feedback ; OpenAI documents pausing for tool-call decisions ; and LangGraph documents review-and-edit interrupts . These are supporting patterns, not evidence of results from this proposed handoff framework.
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