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Explainer · By Chris Sowa · Published
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What Is Human-in-the-Loop Approval for AI Agents?

Human-in-the-loop approval for AI agents is, strictly defined, a per action decision by an identified person before execution, never a standing permission or a review after the fact. Where that gate is replaced by advance authorization, an always allow setting or a daily report, the first wrong payment, submission or record change takes effect before anyone can stop it.

Document, chart and database cards queue at a human approval barrier.

Human-in-the-loop approval for AI agents is, strictly defined, a per action decision by an identified person before execution, never a standing permission or a review after the fact. Where that gate is replaced by advance authorization, an always allow setting or a daily report, the first wrong payment, submission or record change takes effect before anyone can stop it. The control is specific: a named person reviews a specific consequential action at the moment the agent is about to perform it, and the action proceeds only if that person approves it. The agent pauses, presents what it intends to do, and waits. Approval for one action does not carry over to the next, and a general instruction to proceed is not the same thing. The mechanism is distinct from approval in advance, post-hoc review and automated policy checks, and it is the action gate inside Supervised Delegation, in which a named person grants the access, chooses the applications, confirms what is tokenized, can watch and pause the run, and approves every consequential action, on the record. The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.

Key findings

  • In the strict sense used here, approval is a per action decision by an identified person before execution, not a standing permission and not a review after the fact (OWASP, 2025).
  • OWASP lists requiring user approval for high-impact actions as a mitigation for Excessive Agency, alongside minimizing extensions, permissions, and autonomy (OWASP, 2025).
  • Provider tools follow a pause and resume pattern: a tool call needing approval interrupts the run, a person approves or rejects it, and only then does execution continue (OpenAI, 2025).
  • Google guidance for computer use treats confirmation as an in-run step: where a safety response requires confirmation, the user should be prompted before the action is taken (Google, 2025).
  • Anthropic computer use documentation presents human oversight, isolation, and restriction of accessible sites as developer responsibilities rather than model guarantees (Anthropic, 2025).
  • NIST SP 800-53 treats accountability as dependent on actions being traceable to specific individuals and roles, which is why identity is part of the definition of approval (NIST, 2020).

What exactly happens when an agent pauses for approval?

A complete approval event has five parts. First, the agent reaches a defined boundary and stops before acting, rather than acting and reporting afterwards. Second, the system presents the proposed action in a form a person can evaluate, including the target system, the operation, and the values that will be submitted or changed. Third, an identified person, not a shared account and not the agent itself, decides. Fourth, the decision is binding: rejection prevents that action, and the agent must find another path, seek different instructions, or end the task. Fifth, the decision is recorded with the name, the time, and the action it covered, so the event can be reconstructed later.

Without a pause, the mechanism is notification, and if the person cannot stop the action, it is advisory. OWASP describes the core pattern in its guidance on Excessive Agency: use human-in-the-loop control so a human approves high-impact actions before they are taken, whether that routine sits inside the application or in a downstream system (OWASP, 2025).

Identity and timing are part of the definition. NIST SP 800-53 treats accountability as dependent on actions being traceable to specific individuals and roles (NIST, 2020). Timing matters because approval must occur while reversal is still simple, before the agent commits the action. A payment reviewed before submission can be stopped at no cost. The same payment reviewed in a daily report can only be investigated or recovered.

Which actions should require approval at the moment they run?

Not every step needs a person. Reading a screen, searching records, drafting a message, or preparing a form are preparatory steps that usually change nothing outside the run. Requiring approval for each would slow work and consume attention that should be reserved for steps that matter. Actions that belong behind an approval gate share at least one property: they are difficult or impossible to reverse, they move money or create a financial commitment, they change a system of record, or they communicate on the record with a third party.

Examples follow from those properties. Submitting an application, paying an invoice, changing a bank detail, amending a customer or patient record, sending a message that commits the organization, deleting data, and granting access are consequential in most settings. Drafting any of those is not. The line is between preparation and commitment, usually drawn where the destination system would accept the action as final.

Classification should be set in policy before the run, not left to the model during it. A model asked to judge whether its own next action is consequential is being asked to police itself, and an injection that has influenced the model can influence that judgment too. OWASP makes a related point under complete mediation: authorization should be enforced in downstream systems and checked against policy, rather than relying on the model to decide whether an action is allowed (OWASP, 2025). In-run approval is the most expensive control in the set, in human time, and earns that cost only where a wrong action would be costly to unwind.

How does moment-of-action approval differ from other control timings?

Four timings are often confused because all involve a person or a rule somewhere in the process. They are not interchangeable.

Control When the decision happens What it can stop Main limitation
Approval in advance Before the run starts A category of work starting at all Cannot judge the specific values in a later action
Automated policy check During the run, by software Actions that breach a defined rule Cannot judge context outside its rules
Human-in-the-loop approval During the run, at the specific action That specific action, before it commits Costs human time and depends on attention
Post-hoc review After the run ends Repetition, through correction Cannot prevent the first harmful action

Approval in advance is valuable but answers a different question. A manager who authorizes an agent to process a batch of invoices has approved the task, the scope, and perhaps a value limit. That person has not seen the individual payment the agent later constructs, which may contain a wrong amount, a wrong account, or a destination altered by content read along the way. The distinction is sharpest with standing settings that let an agent proceed without asking again: such a setting converts every later action into a pre-approved one and removes the per action judgment that defines this control.

Automated checks answer whether an action complies with an exactly stated rule. They cannot notice that a permitted action is nevertheless wrong in context, such as a payment to an approved supplier for goods never received. Post-hoc review, finally, supports audit and learning and can catch recurring errors, but the action has already taken effect, so harm must be reversed rather than prevented.

What should an approver see before deciding?

An approval prompt is a decision interface, and its quality determines whether the control does any work. At minimum the approver needs the action in plain terms, the destination system and record, the values that will be submitted or changed, the previous value where a change is involved, and why the agent reached this point, including the source material it relied on. For money, the amount, currency, and payee should be visible without opening another screen. For a submission, the final content as it will be sent should be shown, not a summary of it.

Two failures recur. The first is the summary prompt, in which the agent describes its intended action in its own words and the person approves the description. If the agent has misunderstood the task or been misled by injected content, its description may omit the detail that would alarm a reviewer. Showing the actual payload, as the destination will receive it, reduces that risk. The second failure is the context free queue, in which many similar requests arrive with no indication of which are unusual.

An approver also needs a real rejection path: approve, reject, reject with a correction, or pause the whole run. Anthropic guidance on computer use makes a related point: such systems should keep human oversight and run in environments separated from sensitive systems, so the overseer has both visibility and a realistic ability to intervene (Anthropic, 2025). The approver should also have standing to judge the action. A payment should go to a person authorized for payments of that size, and a clinical or legal submission to a person qualified in that domain.

What can go wrong with approval, and how is that risk reduced?

The most familiar weakness is attention. When requests are frequent, similar, and almost always correct, people begin to approve as routine. The control remains in the logs while its substance drains away. Reducing that risk starts with scope, reserving in-run approval for truly consequential actions, and continues with interfaces that surface what is unusual, and sampling approved actions for later review.

A second weakness is manipulation through the agent. Content the agent reads can be crafted to make a harmful action look routine, which is the indirect prompt injection pattern OWASP identifies as a trigger for Excessive Agency (OWASP, 2025). Defenses include presenting the payload independently of the agent narrative, verifying destinations against allowlists automatically before the prompt appears, and implementing the approval mechanism outside the model, so injected instructions cannot disable it or approve on the person's behalf.

A third weakness is delay. A waiting run may wait for hours if the named approver is absent, and pressure builds to widen who can approve or switch the gate off. A fourth weakness is ambiguity about what was approved. If the agent can modify an action after approval and before execution, the record no longer matches the decision. Implementations should bind approval to the exact action presented, so any material change to amount, destination, or content invalidates it and generates a new request.

How do frameworks and provider tools treat approval?

Published guidance converges on approval as one element within a larger oversight arrangement. The NIST AI Risk Management Framework organizes its work into Govern, Map, Measure, and Manage, and treats human oversight, roles, and accountability as matters established under Govern and applied when risks are managed, rather than as a single feature (NIST, 2023). ISO/IEC 42001, the international standard for AI management systems, takes a similar approach, specifying that organizations establish, maintain, and improve policies and processes for responsible AI use (ISO, 2023).

Provider documentation shows the mechanism at implementation level. In the OpenAI Agents SDK, a developer can mark a tool as needing approval, always or conditionally. When the model calls such a tool, the run pauses and surfaces the pending call as an interruption. The application records the state, a person approves or rejects, and the run resumes from that state (OpenAI, 2025).

Google computer use guidance uses a safety decision returned with an action, which can indicate that user confirmation is required before execution, and its safety best practices instruct developers to enforce that confirmation alongside allowlists, secure execution environments, and logging (Google, 2025). Anthropic computer use documentation likewise presents human oversight, isolation, and restriction of accessible sites as developer responsibilities rather than model guarantees (Anthropic, 2025). Across these sources the division of labor is consistent: the model proposes, an external mechanism pauses, and a person decides.

Where does RedactSure sit?

The classification discipline described above is how RedactSure implements approval under Supervised Delegation. The Planner sets, before the run, which values on each screen are tokenized and which actions the agent may take, and a named person confirms that policy. Every consequential action, whether a payment, a submission, or a record change, then stops at the moment it runs for a named person to approve, and there is no always allow option that would turn the gate into standing permission. The approver sees the action as the destination will receive it, with identifiers such as ACCT_001 and VENDOR_001 held as tokens; real values resolve only at the approved destination at the moment of the approved action, so the model that proposed the action never held them.

Each approval is written, with the approver's name, into the AI Control Record, alongside every screen the agent saw as tokens and every action, human and machine, and the record exports to the customer's own SIEM, so routine unexamined consent would be visible to the reviewers who receive it. The approval mechanism sits in the governed environment, outside the model, which can be any vendor's. Deployments are in pilot. Everyone else controls where data can go. RedactSure controls what the AI can see.

Methodology and limitations

The page is a definitional synthesis based on published primary sources, read as of October 13, 2026: the NIST AI Risk Management Framework (official guidance, 2023) and NIST SP 800-53 Revision 5 (official guidance, 2020); the OWASP Top 10 for LLM Applications entry on Excessive Agency (industry standard, 2025); ISO/IEC 42001 as summarized by ISO (international standard, 2023); and vendor documentation from OpenAI, Google, and Anthropic on approvals, confirmations, and computer use oversight (2025). Each framework and standard is cited for what its text says about oversight, accountability, and approval, and each vendor document for the approval or confirmation mechanism it describes.

The page reports no original testing, no measurement of approval accuracy, and no data on how often approvers reject agent actions, because no primary measurement of those rates was found in the sources consulted. No hands-on testing of the OpenAI Agents SDK, Google computer use, or Anthropic computer use was performed; descriptions of those tools are limited to the approval and confirmation mechanisms their providers document, and vendor features change, so the page carries its date and is revised when the documentation changes. The invoice, payment, and record examples describe action types, not any organization; no customer or prospect is described. RedactSure statements are confined to design facts the company publishes about its own system.

Whether a given approval design meets an organization's obligations, which actions it must classify as consequential, and who is qualified to approve them are determinations for the organization's counsel, assessor, and security and compliance officers, not for this page.

What the record shows

Human-in-the-loop approval is a deliberately narrow control, and its narrowness is the point. It does not govern a whole run, certify a system, or replace policy set in advance. It inserts one identified person's judgment at the moment when a specific consequential action can still be stopped at no cost, before a payment is submitted, a record is changed, or a message is sent. Its value depends on details that are easy to weaken: the approver must see the actual payload rather than the agent's summary, the decision must bind the exact action presented, and the gate must be reserved for actions that are hard to reverse. Standing permissions and always allow settings erase exactly what defines it. The test for any implementation is one question: for the last payment the agent prepared, who approved it by name, what exactly did that person see, and could the agent have changed the action after the approval was given?

Frequently asked questions

How is human-in-the-loop approval different from approving a task before it starts?

Approval in advance authorizes a category of work: the task, its scope, and perhaps a value limit. It cannot judge the individual action the agent later constructs, which may hold a wrong amount, account, or destination shaped by content read during the run. Moment of action approval exists to close that gap, by showing a person the specific action, with its actual values, before it commits.

Should every agent action require approval?

No. Reading, searching, and drafting are preparatory and usually change nothing outside the run, so gating them would consume attention needed elsewhere. Approval earns its cost where an action is difficult to reverse, moves money, changes a system of record, or commits the organization to a third party. The line is between preparation and commitment, and that line should be drawn in policy before the run begins.

What makes an approval prompt trustworthy?

The approver sees the action as the destination will receive it: the target system, the operation, the values to be submitted or changed, and the previous value where one exists. A summary written by the agent is weaker, because a misunderstanding or an injected instruction can shape the summary too. The decision should also bind the exact action shown, so any material change produces a new request.

Is an always allow setting compatible with human-in-the-loop approval?

No. An always allow or do not ask again setting converts every later action of that type into a pre-approved one, which is approval in advance under another name. The per action judgment that defines the control is gone, and the first wrong payment or record change takes effect without anyone seeing it. Under Supervised Delegation there is no such option; every consequential action stops for a named person.

Can the model decide which actions need approval?

It should not. A model asked to judge whether its own next action is consequential is policing itself, and an injection that has influenced the model can influence that judgment too. OWASP's complete mediation guidance places authorization in downstream systems checked against policy. Classification belongs in policy set before the run, and the approval mechanism belongs outside the model, where injected instructions cannot disable it.

Who should be the approver?

An identified person with standing to judge the action, not a shared account and not the agent. A payment should go to someone authorized for payments of that size; a clinical or legal submission to someone qualified in that domain. NIST SP 800-53 ties accountability to actions being traceable to specific individuals and roles, so the approver's name, the time, and the exact action should be recorded together.

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About the author

Christopher Sowa
Chris SowaCo-Founder, President and Chief Commercial Officer, RedactSure

Chris Sowa is a founder of RedactSure and a former CEO of an AI company; he started it years before ChatGPT existed. He previously led AI at Accenture, served as Global VP of Strategy & Innovation at Schneider Electric, was CCO of Sovos, and spent more than a decade at Oracle, with earlier roles at SAP and IBM.

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