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Set approval rules at the exact point where an AI agent can create a side effect: before a tool sends a message, edits production data, spends money, changes permissions, runs a command, or touches a sensitive external resource. Start by inventorying every tool and its effects, classify actions by impact and reversibility, add automatic validation for malformed or unsafe inputs, and require a human decision for operations that deserve judgment. In OpenAI’s Agents SDK, an approval-required tool pauses the run, exposes a pending interruption, and resumes from the same saved run state after an approve or reject decision. Other runtimes use different APIs, so apply the same control design while checking their current documentation.
What are approval rules for AI agents?
Approval rules are explicit policies that stop an agent’s side-effecting tool call until an authorized person reviews the proposed operation. They turn an agent from an unrestricted executor into a system that can plan and draft freely but must obtain permission before consequential actions.
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This distinction matters in multi-step workflows. An agent might receive a harmless request, hand work to another agent, and eventually call a tool that deletes a record or sends an external message. A check on the first prompt does not automatically protect those later calls.
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How do I decide which agent actions need approval?
Require approval when an incorrect call could create a sensitive, costly, irreversible, externally visible, or high-impact result. There is no universal dollar amount or risk score in the official guidance; set thresholds according to your systems and document the rationale.
Actions that normally deserve a human decision
- Changing customer, financial, production, or other business-critical data.
- Deleting records, cancelling orders, closing accounts, or applying irreversible status changes.
- Sending email, messages, notifications, posts, or other communications outside the organization.
- Making payments, issuing refunds, transferring funds, or committing spend.
- Altering permissions, credentials, security settings, deployment configuration, or access policies.
- Running shell commands, scripts, migrations, or infrastructure operations.
- Calling sensitive MCP resources or external services whose effects are not fully controlled by your application.
- Taking an action where legal, safety, privacy, or reputational consequences require a person’s judgment.
Actions that may remain automatic
Read-only lookups, calculations, drafts saved in a private workspace, and reversible formatting changes can often run without an individual approval. They still need authentication, input validation, output checks, logging, and rate limits. “Read-only” is not automatically low risk if the data is sensitive or the tool can leak it to another system.
Classify the effect, not the tool name
A tool named update, sync, or publish can hide very different consequences. Record what the call actually changes, which systems it reaches, whether the result can be undone, and what an error would cost. A friendly label is not a risk classification.
What should a tool inventory contain?
Create one row for every callable tool, including tools exposed by delegated agents and MCP servers. The inventory is the foundation for deciding where an approval gate belongs.
| Field | What to record | Why it matters |
|---|---|---|
| Tool and version | Stable name, owner, implementation version, and environment | Policies can drift when a tool changes. |
| Operation | Read, create, edit, delete, send, execute, or permission change | Describes the real side effect. |
| Target | Customer, account, repository, database, production service, or external endpoint | Identifies who or what could be affected. |
| Arguments | Structured parameters, including amounts, identifiers, scope, and timing | Lets the reviewer inspect the exact request. |
| Reversibility | Easy undo, compensating action, difficult recovery, or permanent | Irreversible effects need stronger controls. |
| Cost and exposure | Money, quotas, data disclosure, downtime, legal or reputational impact | Shows why an approval is warranted. |
| Call path | Direct agent call, handoff, nested agent, shell wrapper, or MCP server | Prevents gaps in multi-agent workflows. |
| Failure behavior | What happens on rejection, timeout, malformed input, or unavailable reviewer | Defines safe behavior when the normal path breaks. |
Update this inventory whenever a tool gains a new argument, endpoint, permission, or side effect. Treat an integration upgrade as a policy review, not just a software update.
How do automatic guardrails differ from human approval?
Automatic guardrails validate data and behavior against rules; human approval decides whether a particular proposed operation should happen. Use both, because neither control covers every failure mode.
Automatic checks
- Validate schemas, types, ranges, identifiers, and required fields before a tool runs.
- Reject disallowed destinations, accounts, environments, or data classifications.
- Check tool arguments for policy violations such as an out-of-scope record or an excessive amount.
- Inspect tool results for unexpected output, sensitive data, or a failed precondition.
- Apply authentication, authorization, rate limits, idempotency keys, and transaction safeguards.
Human decisions
Approval is appropriate when context and judgment matter: whether a refund is justified, whether a production change is safe now, whether a message’s wording is acceptable, or whether a sensitive disclosure is authorized. The reviewer should approve or reject the concrete operation, not merely trust the agent’s summary.
OpenAI’s safety guidance summarizes the division as using guardrails for automatic checks and human review for approval decisions. A passing guardrail is not evidence that a human decision is unnecessary when the action remains high impact.
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Where should an approval check be enforced?
Enforce the rule at every side-effecting tool boundary. Agent-level input and output checks do not automatically surround each tool call in a chain.
Direct tools
Attach approval to each function that can change state. A planner may run without review, while the function that actually writes to a database or sends a message pauses before execution.
Handoffs and nested agents
Trace calls after every handoff. A delegated agent may have its own tools, and a parent-level check may not inspect those calls. Apply the policy to the nested tool itself or to a control layer that is guaranteed to intercept it.
Shell and infrastructure tools
Wrap command execution with an approval gate that displays the command, working directory, environment, identity, and expected impact. Do not rely on a natural-language description of the command; review the exact structured request that will execute.
MCP tools and external services
Keep approvals enabled for sensitive MCP tools. Treat text returned by an untrusted external source as data, not as an instruction that can override your policy. The tool boundary must remain authoritative even when the agent is responding to content retrieved from outside your system.
How do I write an approval rule that reviewers can apply consistently?
Write a rule in operational language with a clear trigger, decision owner, evidence requirement, and failure path. Avoid vague wording such as “ask for approval when risky.”
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- Define the protected effect. State exactly what the tool can change or disclose.
- Define the trigger. Specify the operation, target, scope, amount, environment, or data class that requires review.
- Name the approver. Identify the role or group authorized to decide; do not assume every user of the agent has that authority.
- Specify the review payload. Require the proposed action, target, parsed arguments, identity, and relevant context.
- Define rejection. Record a reason and return a useful, non-sensitive explanation to the agent or operator.
- Define delay and timeout behavior. Decide whether the run remains paused, expires safely, or is escalated. The value is application-specific; official guidance does not prescribe a universal duration.
- Define audit data. Store the run identifier, tool name, arguments, reviewer, decision, reason, timestamps, and execution result.
Keep the trigger close to the code that performs the side effect, while keeping policy definitions centrally reviewable. This combination makes enforcement difficult to bypass and policy changes easier to audit.
How does the approval pause work in the OpenAI Agents SDK?
In the OpenAI Agents SDK, a tool can be configured to require approval. When the agent reaches that call, the SDK pauses the run and returns an interruption rather than executing the tool. Your application presents the proposed call to a reviewer, records an approve or reject decision, and resumes the same run state.
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The documented lifecycle
- The agent plans a tool call and produces structured arguments.
- The SDK detects that the tool requires approval and stops before execution.
- Your application reads the pending interruption and displays the operation and arguments to an authorized reviewer.
- The reviewer approves or rejects the call. Store the decision and, for rejection, a useful reason.
- Your application resumes the original
RunStatewith that decision instead of starting an unrelated new turn. - The tool executes only after approval; a rejection follows the failure behavior you defined.
Persist the run state when review may take longer than a single request. A restart, worker handoff, or delayed human response should not force the application to reconstruct the conversation and risk executing a different operation.
Conditional approval and malformed arguments
If approval depends on inspecting arguments, parse and validate them before deciding. The SDK documentation notes that malformed or uninspectable arguments in conditional approval flows require manual approval. Treat inability to inspect a request as a reason to stop, not as permission to continue.
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What the reviewer should see
- The tool and operation being requested.
- The exact parsed arguments, including identifiers, amounts, destinations, and environment.
- The agent’s stated purpose and relevant preceding context.
- Any automatic checks that passed or failed.
- The expected side effect and whether an undo is available.
- Approve and reject controls, with a field for a rejection reason.
The SDK’s interruption and resume model is specific to its current APIs. Other runtimes may name the objects differently or use a queue, workflow token, or callback, so verify the current official documentation before implementing against an interface.
How should I handle approvals in a multi-agent workflow?
Model every agent and handoff as part of one call graph, then place enforcement on each node that can cause a side effect. A parent agent’s input or final-output check cannot be assumed to cover tools called by a child agent.
Map the call graph
List the planner, specialist agents, handoffs, tool wrappers, shell runners, and MCP servers. For each edge, note whether data or authority is passed along. Mark the first point at which a proposed action becomes executable.
Pass identity and scope explicitly
Carry the requesting user, tenant, environment, and authorization scope into delegated calls. A child agent should not gain broader permissions merely because it was invoked by a trusted parent.
Require approval at the last safe boundary
If a child agent drafts a payment request and a parent agent submits it, the submission tool must still pause. If the child submits directly, the child’s tool must pause. Put the control where execution is guaranteed to stop.
Protect against instruction injection
Untrusted documents, web pages, tickets, emails, and MCP responses may contain text that attempts to redirect the agent. Keep external content separate from policy and require approval based on the structured tool call, not on instructions embedded in retrieved text.
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What should happen when approval is rejected, delayed, or unavailable?
Fail closed for consequential operations: do not execute while the decision is pending or when the reviewer cannot be reached. Define a separate, safe behavior for low-risk work if your product needs continuity.
Rejection
Record the reviewer, timestamp, and reason. Return a constrained result to the agent, such as asking it to revise the proposal, produce a draft, or stop. Do not silently retry the same operation under a different wording.
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Timeout
Choose a timeout based on operational requirements rather than copying a generic value. Expiration should leave the protected system unchanged and make the pending request visible for follow-up or escalation.
Reviewer outage
Use a backup approval group or queue only if its members have equivalent authority. Never downgrade a high-impact action to automatic execution because the normal reviewer is unavailable.
Duplicate delivery and retries
Make approval decisions idempotent. A repeated callback or worker retry must not execute the approved operation twice. Use a stable request or operation identifier and verify that the decision applies to the exact arguments shown to the reviewer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can I test that approval rules cannot be bypassed?
Test the blocked path as aggressively as the successful path. A rule is useful only if every route to the side effect encounters it.
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- Send malformed, missing, out-of-range, and ambiguous arguments; verify they are rejected or held for manual review.
- Approve a request and confirm that the exact reviewed arguments execute once.
- Reject a request and verify that no external change occurs.
- Pause a run, restart the worker, and resume from the persisted state.
- Let a request expire or make the reviewer unavailable; confirm the safe failure behavior.
- Invoke the tool through every handoff and nested agent.
- Exercise shell wrappers, MCP tools, retries, and error handlers.
- Inject hostile instructions into external text and confirm they cannot alter the approval policy.
- Inspect audit records for complete identity, arguments, decision, reason, and result data.
These are implementation tests, not a guarantee that all failures are eliminated. Keep monitoring production events and review policies when tools, permissions, or workflows change.
Which implementation features should I compare across agent runtimes?
Compare control guarantees rather than marketing labels. The key question is whether a side-effecting call is technically unable to execute until an authorized decision is recorded.
| Comparison question | What a strong implementation provides | Why it matters |
|---|---|---|
| Granularity | Approval configurable per tool or per call | Different operations exposed by one agent need different controls. |
| Execution guarantee | A pending call cannot run before approval | A UI prompt without enforcement is not a gate. |
| Workflow coverage | Controls for handoffs, nested agents, shell tools, and MCP | Side effects often occur outside the first agent. |
| State handling | Paused state can be persisted and resumed safely | Human review may outlast one request or worker process. |
| Reviewer context | Exact tool, target, parsed arguments, identity, and relevant context | Reviewers need the operation that will actually execute. |
| Decision semantics | Explicit approve and reject outcomes with reasons | Ambiguous or implicit approval is difficult to audit. |
| Failure behavior | Defined handling for timeout, outage, malformed input, and retries | Safety must hold when normal services fail. |
| Auditability | Immutable or access-controlled records tied to execution | Enables incident review and policy improvement. |
Official guidance for OpenAI’s tools and SDKs documents these patterns, but it does not establish a cross-framework standard. Evaluate the current documentation and run your own failure tests before selecting an implementation.
What common mistakes make approval rules ineffective?
- Approving the plan instead of the call. A broad plan can hide the final target, amount, or command. Review the structured operation that will execute.
- Protecting only the first agent. Handoffs and child agents may call unprotected tools.
- Using output checks as a substitute for tool checks. A final response can look safe even though an earlier tool already changed state.
- Allowing pending calls to execute. The system must pause before the side effect, not merely display a notification.
- Hiding arguments behind prose. Reviewers need parsed identifiers, destinations, amounts, and scopes.
- Failing open during outages. An unavailable reviewer should not become automatic permission for a sensitive action.
- Ignoring tool evolution. New endpoints or parameters can create new effects without changing the tool’s name.
- Treating approval as a security boundary by itself. Authentication, authorization, validation, least privilege, logging, and transaction controls remain necessary.
How should I document and maintain the policy?
Store the policy with the tool definition and maintain a human-readable register for operators. For every rule, document the protected effect, trigger, approver, review payload, rejection behavior, timeout choice, and test coverage.
Review the register when an agent model changes, a tool is upgraded, a new MCP server is connected, permissions expand, or a workflow moves between test and production environments. Keep separate policies for development, staging, and production when their consequences differ.
Use audit records to answer four questions after any event: what did the agent propose, what did the reviewer see, who decided, and what actually executed? If those records cannot be reconciled, the approval design is not yet dependable.
Can approval rules eliminate all risk from an AI agent?
No. Approval gates reduce the chance of an unauthorized or unintended side effect, but they do not guarantee that the proposal is correct, that the reviewer has complete context, or that the underlying system will behave as expected. Combine approval with least-privilege permissions, deterministic validation, isolation, monitoring, and rollback or recovery procedures.
The strongest design lets the agent do low-risk work automatically while making high-impact execution explicit, inspectable, and reversible where possible. Reassess the rule whenever the agent’s tools or operating context change.
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What is the difference between approval and authorization for an AI agent?
Authorization determines whether an identity is allowed to call a tool at all. Approval is an additional, per-operation decision that can be required even when the identity has permission, such as a manager confirming a particular refund or production change.
Should read-only AI agent tools ever require approval?
Yes, when a read can expose highly sensitive data, cross a tenant boundary, incur material cost, or trigger an external service with side effects. Classify the actual data and system impact rather than assuming every lookup is harmless.
How do I prevent an approved AI action from being replayed twice?
Bind the decision to a stable operation identifier and the exact arguments reviewed. Enforce idempotency at the tool or transaction layer, and reject stale or already-consumed approval tokens on retries.
Who should be allowed to approve an agent’s production change?
Use a role with authority over that system and change type, preferably independent of the process that generated the proposal. Record the approver’s identity and scope so an audit can distinguish delegated authority from casual confirmation.
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What should an approval screen show for a shell command?
Show the exact command, working directory, environment, credentials or identity, target hosts, files or services affected, and the expected result. A short natural-language summary is useful, but it should not replace the command and parsed parameters.
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