An agentic workflow keeps a person in the loop by automating predictable checks, pausing before consequential actions, showing exactly what the agent proposes to do, and resuming only after an authorized person decides. The approval step is one control in a larger system: the workflow also needs clear boundaries, durable state, a secure review path, and a way to handle failure or rejection.
What “keeping me in the loop” means in practice
Human-in-the-loop approval is not the same as asking a person to watch every step. The goal is to reserve human attention for decisions where consequences, uncertainty, or policy call for judgment, while letting deterministic checks run automatically.
OpenAI’s guide to guardrails and human review distinguishes automatic validation from human approval. Guardrails can validate inputs, inspect tool behavior, and check outputs. Human oversight can stop a run before a side effect—such as editing or canceling something, running a shell command, or performing a sensitive MCP action—takes place. These controls complement one another; an approval button does not replace input validation, access controls, or review of what the agent is actually requesting.
Design the workflow around risk
Start by deciding what the agent may read, propose, and change. Draw a boundary around actions that are high-risk, irreversible, financial, or visible to other people. Those actions are candidates for human approval before execution. Lower-risk, repeatable decisions may be automated when the rules are clear and the consequences are limited.
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Singapore government security guidance recommends defining agent behavior boundaries and human roles, requiring approval for high-risk cases or irreversible actions, validating high-risk transactions, and confirming changes to databases, tables, or files. Applied to a workflow, that means the approval policy should be based on the action and its possible impact—not merely on whether a particular tool is being called.
- Automate clear checks: validate required inputs, enforce allowed values, inspect tool arguments and results, and validate or redact output before it leaves the system.
- Escalate judgment calls: pause when context or policy matters, when a transaction is high-risk, or when a change would be difficult to reverse.
- Keep the agent within scope: limit its available tools and permissions to the work it is meant to perform.
Build the approval checkpoint before the action
The person reviewing a request needs to see the proposed operation and the arguments that will be used. A vague prompt such as “The agent needs approval” does not give enough information to make a meaningful decision. The review surface should make the action, target, relevant details, and available choices clear.
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In the OpenAI Agents SDK, a tool call that requires approval creates a pending interruption before the tool executes. The reviewer can approve or reject the call, and the run can then continue from its saved state. LangGraph’s human-in-the-loop pattern uses interrupt() to pause execution and present a payload for approval or edits. The exact interface depends on the application, but the key design point is the same: the consequential operation waits until the decision is made.
- Prepare the proposed action. Gather the tool name, target, arguments, and other details a reviewer needs to assess its consequences.
- Apply the policy. Continue automatically for actions the workflow permits; create a pending approval for actions that cross a human-review boundary.
- Present a useful decision. Show the proposed operation and its arguments. Offer approval, rejection, or editing when the implementation supports those choices.
- Resume or stop based on the decision. Continue the original workflow after approval; do not execute the pending action after rejection.
Persist state so a pause can last
A workflow that waits for a reviewer must preserve enough state to continue safely later. Without persistence, the system may lose the conversation context, pending action, or place in the workflow—or may be unable to establish what the reviewer approved.
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LangGraph documents checkpointed state for resuming after human input. The OpenAI Agents SDK documents resuming from a RunState and serializing that state when an approval may take longer. In either approach, bind the reviewer’s decision to the specific pending action and resume the original workflow rather than reconstructing an apparently similar request from scratch.
For a long-running pause, define what happens if state is unavailable, stale, or no longer valid. A safe recovery path might require revalidation or a fresh approval rather than executing an old request. The application should also decide how to handle rejection and partial approval, such as when a workflow proposes several changes but the reviewer authorizes only some of them.
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Secure the review path
The approval interface is part of the system’s security boundary. OpenAI’s Agents SDK production guidance calls for trusted authentication and authorization, protections for approval requests where applicable, bounded state retention, atomic consumption in shared storage, and a recovery policy. These are application responsibilities; a framework’s pause-and-resume feature does not establish who may approve, what information they are allowed to see, or whether a decision can be replayed.
- Authenticate the reviewer and authorize which actions and details that person may access.
- Protect approval requests and ensure the decision is tied to the pending action it is meant to authorize.
- Keep stored workflow state only as long as needed, with retention appropriate to the application.
- In a shared store, consume or update pending approvals atomically so concurrent workers cannot act on conflicting decisions.
- Test rejection, edits, partial approval, timeouts, unavailable storage, and recovery—not only the successful approval path.
Choose an implementation that fits the workflow
OpenAI Agents SDK and LangGraph document mechanisms for pausing for human input and continuing a workflow. OpenAI Presence is a different option: a managed enterprise offering described as limited GA, with deployment-specific scope, integrations, and data handling that require technical scoping. These materials describe mechanisms and deployment models, not a comparative safety benchmark.
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| Option | Approval and resumption | Operational considerations |
|---|---|---|
| OpenAI Agents SDK | Documents pending approval interruptions, approval or rejection of calls, continuation from RunState, and serialization for longer delays. |
The application still needs to secure reviewer identity and permissions, protect requests, manage retained state, handle shared-storage updates, and define recovery. |
| LangGraph | Documents interrupt() with checkpointed state so execution can wait for human input and resume. |
The workflow still needs application-level controls for who may review, what they see, and how decisions and failures are handled. |
| OpenAI Presence | Managed enterprise workflow deployment; the cited product description does not specify an equivalent developer-facing approval primitive. | Described as limited GA. Capabilities, integrations, and data handling are deployment-specific; each deployment requires technical scoping, testing, review, and approval before launch. |
Choose based on the risk of the actions, the persistence and recovery the workflow needs, its integrations, and the team’s ability to operate and secure the review path. A framework gives a team orchestration building blocks; a managed deployment involves workflow-specific scoping and implementation. Neither description alone establishes that one option is universally safer.
Operate and improve the workflow
Human oversight should continue after launch. For managed enterprise workflows, OpenAI Presence describes simulations and evaluations before release, guardrails and permissions, session and action records, escalation paths, controlled rollout, monitoring, and rollback. Its deployment model calls for scoping, integration, testing, review, and approval before launch.
For any implementation, monitor whether approvals are reaching the right people, whether reviewers have enough context, and whether rejected or edited proposals are handled as intended. Use those findings to adjust action boundaries and checks. Do not expand the agent’s authority simply because a workflow has an approval screen; change permissions only when the controls and recovery behavior support the new scope.
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