An agent can turn workshop notes into a polished requirements document, then generate stories and prepare changes for a team's backlog. Each output may look plausible. The important product question is where the workflow should pause before a proposed interpretation becomes an accepted commitment or an external action.

For teams moving from AI assistants to agentic workflows, human approval needs a defined place in the process. A useful checkpoint identifies what is being decided, who may decide it, and exactly what happens next. This article proposes a practical design for that checkpoint, using a hypothetical product discovery workflow.

Put approval at a meaningful decision boundary

Begin with the consequence of continuing. Summarizing an interview for private review, accepting a product requirement, and creating work in a shared backlog are different decisions.

Anthropic's foundational guidance describes agents that use environmental feedback, return to people at checkpoints or blockers, and operate with stopping conditions. It also recommends adding complexity only when it improves outcomes. Its architecture guidance provides a useful starting point for deciding how much autonomy a workflow needs.

For a product workflow, consider three questions:

  • Will the next step commit the team to an interpretation that is still uncertain?
  • Will it change a shared system, disclose information, or communicate a decision externally?
  • Does the proposed action exceed the authority already granted to this workflow?

Use the answers to place review. Routine preparation can continue within its agreed scope. A change in business commitment or execution authority deserves an explicit decision. This is a design recommendation; the appropriate boundary depends on the task and the organization's operating rules.

Separate approval of content from authorization to act

Consider a hypothetical team exploring improvements to customer onboarding. An agent reads approved workshop notes, extracts observations, flags assumptions, and prepares a PRD.

The product owner reviews the problem statement, intended users, evidence, proposed scope, and unanswered questions. Approving this document establishes a working baseline. The workflow may then prepare draft stories and acceptance criteria from that baseline.

That content decision does not automatically authorize adding those stories to a shared backlog, assigning engineers, or sending a delivery commitment to stakeholders. Those are separate actions with different effects.

For this example, the workflow presents the reviewed stories and a specific proposal: create six unassigned draft items in the selected project, with no release date or notification. A reviewer with the relevant authority can approve that action. If standing authorization already covers this exact operation, the system can apply that policy without asking again.

Making this distinction explicit prevents a broad instruction such as “approve the PRD” from becoming an undefined permission to perform every downstream step.

Give the reviewer a concrete proposal

A useful review screen should let someone assess the result without reconstructing the entire conversation. For the hypothetical backlog action, present:

  • Proposed change: the six items, their exact titles and descriptions, and the destination project.
  • Evidence: links to the approved PRD and the observations supporting each requirement.
  • Open assumptions: unresolved points and how the draft handles them.
  • Execution scope: whether the action creates, updates, assigns, notifies, or publishes.
  • Decision owner: the role authorized to approve this particular change.
  • Expected result: what will exist after approval and how the workflow will verify it.

Treat this as a suggested review format, not a mandatory framework. A smaller action may need fewer fields.

Automatic validation can check required fields and allowed values before review. Human judgment can then focus on intent, evidence, and acceptable consequences. OpenAI's guardrails and human review guidance distinguishes automatic checks from approval decisions and recommends controls at the relevant tool boundary. A document that passes schema validation can still contain an unsupported requirement.

Make revision a clear branch

Offer decisions that have understandable effects: approve the presented proposal, reject the action, or request a revision.

A revision should carry actionable feedback. “Reduce scope” is difficult to execute consistently. “Keep the account-verification stories; remove the referral feature because it was not supported by the workshop” gives the workflow a concrete direction.

The revised result becomes a new proposal. Keep its relationship to the earlier version so the reviewer can see what changed. If the revision materially changes the action, destination, or parameters covered by an approval, evaluate the new proposal under the applicable approval policy.

Requesting a revision is an application behaviour. An SDK's approve and reject operations do not, by themselves, implement document versioning, revision instructions, or the policy for reusing an existing authorization.

Preserve the pause as workflow state

Review may finish after the original browser session closes. Design a pending decision as stored workflow state rather than relying on an open conversation.

The OpenAI Agents SDK human-in-the-loop documentation describes interruptions and serializable run state for pausing and resuming work. It also requires application-level care: keep approval state under server control, authenticate the reviewer, authorize access to the pending action, and prevent replayed decisions from resuming the same work twice.

Microsoft's workflow human-in-the-loop documentation provides another implementation pattern. Pending requests are retained in checkpoints and re-emitted when the workflow is restored.

For the product example, store the proposal version, intended destination, pending decision, and execution status together. Define what happens when a request expires or its context changes. An unavailable reviewer should lead to an explicit waiting, escalation, or cancellation path under the chosen policy.

Keep execution permissions narrow

Approval and execution permissions solve different problems. A reviewer may approve a proposed change, but the identity performing it still needs permission to make that change in the destination system.

Anthropic's current deployment guidance recommends enabling only the tools an agent needs and restricting credentials and network access. It also distinguishes automated policy evaluation from a human checkpoint: a system that automatically allows a request has not obtained a person's review.

In the onboarding example, the component creating backlog items needs access to the selected project and permitted operations. It does not need authority to administer users or change unrelated projects.

Validate the destination and action parameters immediately before execution. If permissions or relevant project state have changed since review, resolve that mismatch through the workflow's policy. Avoid treating the earlier approval as permission to substitute a different action.

Plan for an uncertain result before retrying

Suppose the backlog service creates the six items, but the connection fails before the workflow receives confirmation. Repeating the request immediately may create duplicates.

Approval does not provide exactly-once execution. The AWS Builders' Library explanation of idempotent APIs describes using a stable request identifier to distinguish a retry from a new intended action. The destination must coordinate that identifier with the change it performs.

Use the destination's idempotency contract when one exists, including its parameter and retention rules. An identifier stored only inside the orchestrator cannot make an arbitrary external API safe to retry.

For our example, an uncertain result should trigger reconciliation through the destination's supported recovery process. A missing item in an ordinary lookup is not proof that a retry is safe: reads can lag and an earlier request may still complete. If the outcome cannot be established authoritatively, keep it marked unknown and stop automatic replay. Record confirmed outcomes separately from attempts.

Evaluate the decisions and the review experience

A fluent PRD is only one part of success. The workflow must also select the correct action, preserve the approved scope, and wait when a decision is required.

OpenAI's evaluation guidance recommends task-specific objectives, representative test cases, continuous evaluation, and assessment of tool selection and argument accuracy. Apply those principles to the checkpoint itself.

For this product workflow, useful evaluation cases include unsupported requirements, an incorrect destination project, a material revision after approval, an unauthorized reviewer, and a timeout after a successful external change.

Suggested operational measures include the proportion of proposals requiring substantial correction, time spent waiting for review, reasons for rejection, and duplicate-action attempts. Also inspect unnecessary interruptions: a system that repeatedly asks for decisions already covered by a valid policy creates avoidable work.

Choose targets from the team's actual risk and workload. There is no meaningful universal approval-rate target for every agentic product.

Keep an understandable decision record

Record enough to explain what was proposed, which version was reviewed, who decided, what was executed, and what the destination confirmed. Include rejection or revision reasons when they affect later work.

Technical traces can help connect the execution steps. However, OpenAI's tracing documentation notes that model and tool spans can contain sensitive inputs and outputs. Configure capture and export deliberately instead of copying every workshop note or tool response into logs.

A business decision record and a technical trace serve related purposes. Define retention and access for each, and connect them with identifiers that do not expose unnecessary information.

Start with one checkpoint the team can assess

For the first implementation, choose one bounded transition: reviewed draft stories becoming items in a shared backlog. Make the proposal visible, define its authority, preserve the pending state, verify the result, and test an uncertain outcome.

Once that transition works, use the evidence to adjust where human judgment adds value. The same approach can then support other product decisions, with review shaped by the actual commitment being made.

Sources

Documentation checked on 31 October 2026. The Anthropic architecture article was published in December 2024 and the AWS retry guidance is foundational material; neither is presented as a new release.