What is human-in-the-loop for AI agents?
By Identra · Updated
Human-in-the-loop for AI agents means a person must review and decide whether specified actions can proceed before the agent executes them. Effective oversight gives that person the information, authority, and ability to reject or change the proposed action.
How does human-in-the-loop work for AI agents?
The agent proposes an action and the workflow stops. Someone with authority approves it, rejects it or asks for a change. Execution continues only if the decision covers the action that's actually about to run. The gate sits before the consequence: the email going out, the file being deleted, the role being granted.
In AI generally the term is broader. It also covers human feedback during training and people reviewing model output. Here it means control over agent actions. Anthropic's guide to building effective agents treats human checkpoints as part of running an agent.
Whatever executes the action has to enforce the pause. Claude Code's permission prompt works because the tool won't run the command until you answer. A line in a prompt saying ask first does nothing if the agent can call the API directly. AI agent guardrails have to cover every path, delegated work included.
Which AI agent actions should require approval?
Start from consequences. Sending sensitive data out, changing access, moving money, touching production. Think about the target, the recipient, how sensitive the data is and whether it can be undone. Reads can matter too, when what gets read ends up with the wrong person.
AI governance should sort actions into allowed, needs approval and prohibited, and name who decides each. An agent asking permission doesn't hand the reviewer new authority. No approval overrides a prohibition.
Allow
- Example
- Draft a reply from approved reference material
- What happens
- Runs within existing permissions
Require approval
- Example
- Send a confidential report to a permitted outside recipient
- What happens
- Pauses and shows recipient, content and attachments
Require approval
- Example
- Apply a production configuration change
- What happens
- Pauses and shows the exact diff and affected resources
Deny
- Example
- Send credentials to a prohibited destination
- What happens
- Blocked, with no approval option offered
What does meaningful approval look like?
Say a finance agent prepares a supplier payment after reading an email asking to change bank details. It asks for approval. The reviewer sees one line: matches invoice, ready to pay. That review is worthless. It hides the one detail that decides where the money goes.
A useful request shows the supplier, the amount, the destination account, the invoice and exactly what changed from the payment details on file. The reviewer then runs the company's call-back check for bank changes. The email can't vouch for itself.
The approval covers those exact details. If the agent changes the destination afterward, it needs a fresh decision. Approving the plan is not open-ended agent delegation to make future payments or edit supplier records.
Afterward, record whether the payment went through, failed or is stuck somewhere. Approved doesn't mean done.
How do you build a reliable approval gate?
Treat approval as an authorization decision, with real inputs and a real way to say no. The agent's explanation can sit alongside the request. It shouldn't replace it.
- Define which actions need review and who has authority over each resource.
- Show the acting identity, exact target, data, proposed change and expected effect, taken from the actual request.
- Bind the approval to those parameters. A material change means a new decision.
- Make approvals expire, and don't let one be reused for something else.
- Stay paused when approval is missing, denied or expired. Route stuck requests to an authorized backup. Silence is never a yes.
- Recheck permissions and resource state right before execution.
- Test changed recipients, expired approvals, repeated requests, delegation and an absent reviewer.
How do you prevent approval fatigue?
If every prompt looks the same, people click through. Save interruptions for decisions where a person can actually change the outcome. Let routine work run inside narrow, preapproved limits, with least privilege underneath.
Make requests quick to judge. Highlight what changed and name the business resource affected. Group related actions only when the reviewer can see the full scope. Approve all cleanup actions, with targets nobody has listed, is a blank check.
Talk to the people getting the prompts. A stream of repeat requests often points to unclear ownership or an agent with too much scope. If reviewers don't have the time or expertise to judge what they're shown, fix the workflow before counting their clicks as assurance.
What can human review miss?
Plenty. People skim, misread and trust a good story. Prompt injection can shape both the action an agent proposes and the explanation the reviewer reads.
So permission limits, destination rules and data protections stay on after approval. Keep an AI audit trail of the proposed action, reviewer, decision, approved parameters and result, so investigators can tell what a person authorized from what the agent did. When execution fails or the result is unclear, check what happened before retrying. A blind retry can pay the same invoice twice.
How Identra thinks about it
Identra checks AI agent tool calls on macOS and Windows endpoints against policy and denies destructive shell commands when policy is set to block. That limit holds even if someone approves too quickly. Identra's own AI triage advises and doesn't act. Response actions such as revoking sign-in sessions need human approval for a specific target, and the result is recorded.
Go deeper: AI security, built on identity
Frequently asked questions
Does human-in-the-loop mean approving every agent action?
No. Routine actions can run within set limits. Consequential ones pause for review, and prohibited ones are blocked.
How is human-in-the-loop different from human-on-the-loop?
In the loop usually means a person must act before a specified action proceeds. On the loop usually means a person supervises and can step in while automation runs. Usage varies, so write down exactly when execution pauses and who can stop it.
Is approving an agent's plan enough?
Only when the plan names a bounded set of actions and execution stays inside it. Clean up our cloud resources doesn't tell anyone which deletions were approved.
Who should approve an AI agent's actions?
Someone with authority over the affected resource and enough expertise to judge the consequence. Sensitive actions may need an independent reviewer under your existing approval rules.
What should happen if nobody responds?
The action stays paused or expires. It can escalate to another authorized reviewer. It never proceeds by default.