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Keep control of an AI agent by enforcing boundaries outside the model: assign it a separate identity with least-privilege access, require a human decision before consequential side effects, and record every request, tool call, policy result, approval, and outcome. An approval dialog is useful, but it cannot compensate for an agent that already has unrestricted credentials or network access.
This guide shows how to design that control system, select an approval policy, implement a pause-and-resume workflow, and audit what happened afterward.
What “control” means for an AI agent
An agent is more than a language model. Anthropic defines an agent as “an AI model that directs its own processes and tool use when accomplishing a task—that is, deciding for itself how to achieve what users want, rather than following a fixed script.” That autonomy is useful only when each layer around the model limits what it can actually do.
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Think of authority as the combination of four layers:
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- Model: chooses a plan and proposes tool calls.
- Harness or policy layer: decides which calls are allowed, denied, or paused.
- Tools: expose operations such as reading a database, sending mail, changing a ticket, or deploying code.
- Execution environment: supplies credentials, files, network routes, operating-system permissions, and production access.
A prompt can influence the model’s behavior, but it is not an access-control boundary. If a tool accepts a destructive request and the runtime credential can execute it, a sentence such as “be careful” does not prevent the call. Real control comes from independent limits at the identity, tool, environment, approval, and logging layers.
Google Cloud’s guidance states the principle directly: “To limit an agent’s ability to take dangerous actions, create an agent identity, and follow the principle of least privilege to grant the agent only the roles and permissions necessary to complete its tasks.” See the Google Cloud AI security and safety guidance.
1. Define the work before granting access
Write a short contract for each agent before creating credentials. The contract should be specific enough that another engineer can determine whether a proposed tool call is inside or outside scope.
State the task and the systems in scope
- Name the business task, such as classifying support tickets or preparing a deployment plan.
- List the systems and data the agent may read.
- List the operations it may perform, separating read, create, update, delete, send, and publish actions.
- List explicit prohibitions, including systems, data classes, regions, and environments it must never access.
- State which outcomes require a person, such as sending an external message or changing production data.
Keep user-supplied documents, web pages, emails, and retrieved records as data to analyze. Do not let text inside those sources silently become new instructions or permissions. Google identifies prompt injection as a risk and recommends separating data from instructions.
Define a safe completion
Describe what “done” means and what the agent should do when it cannot complete the task safely. A safe completion might be a draft, a report, or a queued request awaiting review. “Continue until successful” is not a safety condition; it can encourage repeated attempts, broader searches, or escalating side effects.
Record assumptions and escalation paths
Document ambiguous inputs, maximum scope, time limits, and the person or team that handles exceptions. If the agent encounters a missing permission, a conflicting instruction, or an unfamiliar tool, the default should be to stop and request review rather than discover a workaround.
2. Give the agent its own, narrow identity
Do not run autonomous work under a user’s all-purpose account. Create a distinct service or agent identity for each materially different task or environment. That identity lets you revoke access, attribute actions, and apply a policy without changing a person’s permissions.
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Apply least privilege to roles and credentials
- Grant only the resources and operations required by the written task.
- Separate read credentials from write credentials where the workflow allows it.
- Use different identities for development, staging, and production.
- Prefer short-lived, workload-bound credentials over long-lived secrets.
- Restrict API keys by application and API when keys are unavoidable.
- Keep secrets outside prompts, retrieved documents, and model-visible logs.
Google describes service accounts, agent identities, workload identity federation, and API-key restrictions as ways to bind access to a workload. The exact mechanism depends on your platform, but the design rule is constant: the credential must remain safe even if the model proposes a malicious action.
Limit the environment as well as the API
An API role is only one part of authority. Restrict filesystem paths, network destinations, package installation, process execution, and access to environment variables. A read-only database role does not make an agent safe if it can also reach a production shell or copy secrets from the host.
Use separate network routes or egress policies for agents. Allow only the destinations needed for the task, and make blocked destinations visible in the audit trail. A tool that can reach arbitrary URLs can be turned into a data-exfiltration channel even when its documented purpose is harmless.
3. Put the approval gate at the side-effect boundary
Approval should occur immediately before an operation that changes the outside world, after the agent has produced the exact arguments but before the tool executes them. Earlier approval of a broad plan is not equivalent: the agent may later choose different targets, parameters, or tools.
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Actions that normally need a human decision
| Action type | Typical default | Why the gate matters |
|---|---|---|
| Read-only queries within approved data | Automatic, if access is narrow | No external state changes, although confidentiality still requires access controls. |
| Sending messages or publishing content externally | Ask before execution | A mistaken recipient, claim, or attachment can create reputational or legal consequences. |
| Money movement, purchases, refunds, or pricing changes | Ask before execution; consider a second control | The effect is consequential and may be difficult to reverse. |
| Legal commitments, regulated decisions, or changes affecting a person’s eligibility | Explicit human approval | These decisions require accountable judgment and may be subject to formal obligations. |
| Production writes, deletion, migration, or deployment | Ask before execution | An incorrect target or parameter can cause outage or data loss. |
| Credential, permission, policy, or tool changes | Ask before execution and re-evaluate scope | The change can expand the agent’s future authority. |
Google recommends human oversight and explicit approvals for consequential actions involving money, legal commitments, regulated decisions, production data, and other effects that are not readily reversible. There is no universal numeric risk threshold in the cited guidance; classify actions according to your systems, obligations, and ability to undo them.
Use automatic execution only for bounded effects
Automatic execution is reasonable for low-risk operations whose target, scope, and failure impact are tightly bounded: for example, reading a known document set or adding a label in a sandbox. Even then, enforce the boundary with credentials and tool restrictions rather than trusting the model to self-limit.
4. Choose an approval policy deliberately
Approval systems usually offer several modes. Anthropic’s managed-agent documentation names always_allow, always_ask, and auto; disabling a tool is a separate control.
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| Mode | What happens | Does a person see every call? | Best fit | Important limitation |
|---|---|---|---|---|
always_allow |
Enabled calls execute without confirmation. | No. | Low-risk, bounded operations with independent access limits. | It is not human approval. |
always_ask |
The run pauses until a reviewer allows or denies the specific call. | Yes. | Consequential or hard-to-reverse actions, or workflows requiring review of every call. | Reviewer fatigue can make approvals superficial. |
auto |
A server-side policy evaluates the call and may allow, deny, or pause it for review. | Only when the policy pauses it. | Triage across mixed-risk calls. | Anthropic explicitly says “auto is not a human checkpoint”; an allowed call runs before a person sees it. |
| Tool disabled | The capability is removed from the agent. | Not applicable. | Tasks that do not need the capability at all. | A runtime approval policy does not remove an enabled tool. |
Compare modes using five questions: Is every action seen before execution? Can the policy deny without an override? How serious and reversible is the effect? Are the exact arguments visible? Are credentials and resources independently constrained? The answers, not the label on the mode, determine whether control is adequate.
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5. Make the human approval meaningful
A reviewer cannot make a sound decision from “The agent wants to continue.” Present a concrete, bounded proposal.
- Show the tool and operation. Identify the exact function, endpoint, or command.
- Show the target. Include the account, record, environment, recipient, repository, or resource that will change.
- Show all arguments. Display amounts, filters, file paths, message bodies, attachments, and other parameters that affect the outcome.
- Show identity and scope. State which agent identity is making the call and which permissions it will use.
- Explain the source of the proposal. Distinguish user instructions, retrieved data, and model-generated reasoning so an injected instruction is easier to spot.
- Describe reversibility. Say whether the action can be undone, how, and within what time.
- Record the decision. Capture the reviewer, decision, timestamp, and any comment or condition.
For especially sensitive operations, make the human perform the final step themselves rather than approving an opaque automation. Google notes that an approver can mistakenly accept a malicious or destructive suggestion, so approval must be one barrier among several, not the sole safeguard.
6. Implement pause, review, and resume safely
OpenAI Agents SDK pattern
The OpenAI Agents SDK human-in-the-loop guide documents a concrete flow:
- Mark a tool as requiring approval.
- Run the agent. When it reaches that tool, the run records an interruption instead of executing it.
- Present the stored tool name and arguments to a reviewer.
- Approve or reject the interruption.
- Resume the run from its stored state, allowing execution only after the decision.
The approval can be conditional on parsed arguments, so a policy can require review only when an amount, target, environment, or other field crosses a defined boundary. Apply guardrails before the approval request and repeat the relevant check after approval, immediately before execution. This protects against arguments or external conditions changing while a request waits in a queue.
Server-evaluated policies
Anthropic’s permission-policy documentation describes automatic evaluation that can allow, deny, or pause a call. Treat an allowed result as a policy decision, not as evidence that a human reviewed the action. If the risk requires a person to see every call, configure an always-ask path or remove the tool from the agent.
Prevent approval bypasses
- Ensure the tool cannot be called through an unapproved alias or a second integration.
- Bind the approval record to a hash or canonical representation of the arguments.
- Revalidate identity, target, permissions, and policy immediately before execution.
- Expire stale approvals when the request, credentials, data, or environment changes.
- Make rejection terminal for that call; do not let the agent silently retry with a broader or altered request.
7. Keep approval from becoming the only defense
Humans can miss a dangerous detail, approve the wrong request, or be pressured by a high volume of prompts. Independent controls should still stop an unsafe call.
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- Use allowlists for resources and network destinations.
- Cap transaction amounts, record counts, execution time, and retry attempts.
- Require a specific environment or branch for write operations.
- Use immutable or recoverable storage for important data.
- Separate duties for creating a policy, approving a call, and changing credentials.
- Alert on unusual destinations, volumes, privilege changes, and repeated denials.
These controls reduce the blast radius when a model is manipulated or a reviewer makes a mistake. They also make automatic execution safer for the narrow operations that genuinely do not need a person.
8. Treat input and tool composition as security boundaries
Defend against prompt injection
Untrusted content can contain instructions aimed at the agent rather than information needed for the task. Parse and label external text as data, constrain the tools available while it is being processed, and require approval for any action whose justification comes from that content. Do not let a document grant itself permission to send, delete, or retrieve additional data.
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A harmless-looking combination can create a dangerous path: one tool reads secrets, another posts arbitrary text, and a third accesses an external endpoint. Evaluate the data flow between tools and restrict what each step can pass onward. Google warns about insecure tool chaining and about newly added MCP tools that can expand capability.
Re-review additions and schema changes
A permission policy governs the tools that are enabled; disabling a tool is a different control. When a new MCP server, function, parameter, credential, or execution route is added, repeat the scope review. Anthropic notes that existing sessions may retain their original configuration while future sessions receive updated settings, so test both running and newly created sessions during a change.
9. Build an audit trail that explains every decision
Log enough information to reconstruct what the agent attempted, what the system allowed, and what actually happened. OpenAI describes retaining the user request, tool activity, approval decisions, tool results, and network-policy outcomes in Codex logs; those events can support security review and centralized operational monitoring. See Running Codex safely at OpenAI.
Minimum event fields
| Event | Fields to retain |
|---|---|
| Request | Original user input, requesting identity, session, timestamp, and task scope. |
| Proposal | Agent identity, model or policy version, tool name, canonical arguments, target, and originating data references. |
| Policy result | Rule evaluated, allow/deny/pause result, reason, and policy version. |
| Human decision | Reviewer identity, decision, timestamp, conditions, and the argument version reviewed. |
| Execution | Credential or role used, start and finish times, tool result, error, retry, and side-effect identifier. |
| Boundary events | Network blocks, access denials, changed tools, changed credentials, and environment transitions. |
Protect logs from alteration and restrict who can read sensitive arguments. Centralize security-relevant events in the monitoring or SIEM system your organization already uses. Review both expected behavior and mistakes: a successful call can still reveal an overbroad permission, while a denied call can expose a missing business rule.
10. Troubleshoot common control failures
Reviewers approve everything
Symptom: approval requests are frequent, vague, or nearly identical, and reviewers click through them. Fix: move low-risk operations to bounded automatic policies, keep consequential calls in always-ask mode, and display exact targets and arguments so the reviewer has a meaningful decision.
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The agent has more access than the task requires
Symptom: a read-only workflow can list unrelated projects, reach production, or invoke administrative APIs. Fix: create a narrower identity, split read and write credentials, remove unused tools, and add network and resource allowlists. Do not rely on a future approval prompt to compensate for current overprivilege.
The reviewer approved one thing but another executed
Symptom: the tool call changed while waiting, or the runtime resolved an alias to a different target. Fix: bind approval to canonical arguments, show the target and identity, and repeat policy and authorization checks immediately before execution.
A rejected call keeps coming back
Symptom: the agent retries with altered wording or a different tool. Fix: make rejection terminal for that operation, record the reason, and constrain alternate tools and retry counts. Escalate to a person instead of letting the agent search for a bypass.
A new tool silently expands capability
Symptom: an MCP server or integration adds functions that were not included in the original review. Fix: treat tool additions, schema changes, and credential changes as permission-review events; update policies and test existing sessions as well as new sessions.
You cannot explain what happened
Symptom: logs contain only a final answer, with no arguments, policy result, or approval record. Fix: instrument request, proposal, policy, decision, execution, and network events before expanding the rollout. Without that chain, incident analysis and compliance review will be guesswork.
11. Roll out in controlled stages
- Inventory capabilities. List every tool, credential, network route, file path, and environment available to the agent.
- Write the task contract. Define allowed data, operations, prohibited actions, and required human outcomes.
- Create the identity. Bind a separate, least-privilege credential to the task and environment.
- Remove unnecessary tools. Disable capabilities the task does not need instead of relying on runtime decisions.
- Classify calls. Mark read-only, bounded, consequential, and irreversible operations.
- Configure policy. Use automatic execution only for bounded effects; use an explicit human gate for consequential calls.
- Test adversarially. Try prompt injection, malformed arguments, unauthorized targets, tool-chain abuse, stale approvals, and network exfiltration.
- Verify the audit trail. Confirm that another operator can reconstruct a run from logs without access to the model’s hidden reasoning.
- Expand gradually. Add tools or environments only after reviewing the new authority and updating the policy.
A practical decision checklist
- Could this call move money, create a legal commitment, affect a regulated decision, change production data, expose confidential information, or cause an effect that is hard to reverse? If yes, require explicit human approval.
- Can the task work with read-only data, a sandbox, a resource allowlist, or a lower-privilege identity? If yes, use that narrower setup.
- Will the reviewer see the exact operation, target, arguments, identity, and reversibility before deciding? If not, the gate is too weak.
- Can the system still deny the call if the reviewer or model is wrong? If not, add independent credential, network, quota, or environment limits.
- Can you reconstruct the request, proposal, policy result, decision, execution, and outcome later? If not, improve logging before increasing autonomy.
- Has any tool, schema, credential, or execution environment changed since the last review? If yes, repeat the permission analysis.
Google’s Agent AI Use Guidelines and its security documentation were updated in October 2026; platform controls are version-sensitive, so verify the current behavior of the products you deploy. Anthropic’s trustworthy-agent discussion was published April 9, 2026, and OpenAI’s Codex safety article May 8, 2026. The names and defaults of specific policy systems can change, but the durable design remains the same: narrow identity, least privilege, approval at the side-effect boundary, and an independent audit trail.
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