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Choose an AI agent platform by testing what happens immediately before a tool can cause a side effect. The strongest design combines a server-enforced policy gate, a distinct least-privilege agent identity, separate read and write permissions, and an audit trail that links the request, decision, tool call, resource, approver and result. Treat approval to publish an agent as a deployment control, not as proof that sensitive actions will pause during a run.
Start with the control outcome, not the vendor feature list
An agent platform is suitable for consequential work only when it can answer four questions for every sensitive action:
- What policy applied before the tool executed?
- Who or what authorized the action?
- Which identity, tool and resource were involved?
- What happened afterward, including the result or failure?
Those questions are more useful than a checkbox labeled human in the loop or audit logs. A visible approval prompt may be optional, risk-based or controlled by application code. A runtime trace may show that a function ran without recording who approved it or which policy allowed it. Evaluate the enforcement point and the evidence it produces.
Microsoft’s guidance recommends a distinct governed identity for each agent, least-privilege access, separate read and write actions, policy checks before tool execution, and linked records covering the request, decision, execution, resource and outcome. See Microsoft’s access patterns and controls for AI agents.
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Define the actions that need approval
Before comparing platforms, inventory what the agent may do. Classify each tool operation by its potential impact rather than by its technical name. A database update, an email send and a shell command are different implementations of the same governance problem: they can change the world outside the model.
Use four action classes
- Read-only: searching documents, querying a catalog or retrieving a status where the agent cannot alter the source.
- Reversible write: creating a draft, opening a ticket or changing a nonproduction record that can be undone.
- Consequential write: sending, submitting, deleting, publishing, changing access or updating a production system.
- High-impact or restricted: financial transfers, legal commitments, identity changes, security controls, regulated records or actions affecting many users.
Map each operation to an approval rule. A read may run automatically for an authenticated user. A draft may require a policy check but no person. A send or delete may require an explicit approver every time. A restricted operation may need two-person approval, a change window or a manual process instead of an agent.
Record the boundaries in a control matrix
| Operation | Minimum control | Evidence to retain |
|---|---|---|
| Search internal documents | Identity-based authorization and read-only scope | Actor, data source, query and returned-resource identifiers |
| Create a draft email or ticket | Policy evaluation; optional user confirmation | Request, generated content, policy result and draft identifier |
| Send, submit or publish | Deterministic pre-execution gate and named approval | Approver, timestamp, exact payload or version, destination and result |
| Delete or change access | Explicit approval, narrow resource scope and preferably a second control | Before-and-after state, policy decision, approver and rollback status |
| Run a shell or administrative command | Allowlist, isolated identity and approval before execution | Command, parameters, environment, approver, output and exit status |
Keep this matrix platform-neutral. It becomes your acceptance test when a supplier demonstrates a workflow.
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Approval controls differ in timing, authority and failure behavior. Ask the supplier to demonstrate the complete path from proposed tool call to resumed or terminated run.
Approval semantics to compare
| Question | Why it matters | Evidence to request |
|---|---|---|
| Does the pause occur before the side effect? | An approval after execution is notification, not prevention. | A trace showing the tool was not invoked until approval was recorded. |
| Who can approve? | The agent’s requester may not be authorized to approve its own action. | Role, group, tenant and resource rules for approvers. |
| Can a policy deny without asking? | Some actions should be blocked even when a person is available. | Server-side deny and escalation rules outside model output. |
| What happens on rejection or timeout? | Silent retries can turn a rejected action into an eventual side effect. | Run state, retry behavior, notifications and termination record. |
| How does the run resume? | Resuming with changed context can invalidate the original decision. | Immutable request or a re-evaluation after edits, with a new decision logged. |
| Is approval per tool call or per run? | One broad approval can unintentionally authorize later actions. | Granularity, expiry and whether approvals are reusable. |
The OpenAI Agents SDK describes human review that pauses a run before side effects such as cancellations, edits, shell commands or sensitive MCP actions. Its guide separates automatic guardrails from human review; use it as workflow guidance and verify how your application records and enforces the decision. Read Guardrails and human review.
Anthropic’s Managed Agents documentation describes three tool-policy modes: always_allow, always_ask and auto, in which the server evaluates the call. The documentation labels Managed Agents permission policies beta and states that application-owned custom tools remain controlled by the application. Therefore, a platform policy may cover managed tools while your own integration still requires a separate enforcement and logging design. See Permission policies.
Require enforcement outside the model
Language models are useful for interpreting intent, but they are probabilistic. A model can omit a review request, misclassify a target or produce a convincing explanation for an unsafe action. The authorization decision for a consequential operation should be made by a deterministic policy service or an equivalent server-side control after the model proposes the call and before the tool receives it.
Capabilities a policy engine should expose
- Subject scope: user, service account, agent identity, team and tenant.
- Object scope: tool, API, database, folder, record, environment and data classification.
- Operation scope: read, create, update, delete, send, publish, execute or grant.
- Context: time window, network or device condition, ticket number, change window and risk signal.
- Decision: allow, deny, ask for approval or escalate to a specific role.
- Freshness: policy version and timestamp attached to the decision.
Ask whether the model can bypass the policy by calling a different tool, changing parameters or invoking an application-owned connector. Every route to a side effect must pass through the same gate, or the control is only partial.
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Design identity and permissions for a small blast radius
Give the agent a distinct identity rather than sharing a human administrator account. Scope that identity to the resources and operations it needs, and make its permission lifecycle visible to the people who own access governance.
Separate reads from writes
Use separate credentials, roles or tools for reading and changing data where the platform allows it. A document-search identity should not automatically inherit permission to delete the documents it can retrieve. A support agent that can inspect an order may need a different, approval-gated operation to issue a refund.
Check delegated and app-only access
Determine whether the agent acts as the requesting user, as an application identity, or through a mixture of both. Delegated access can preserve user-level boundaries but may inherit broad permissions. App-only access can be easier to isolate and audit but must be tightly scoped because it is not naturally limited by an interactive user session. Record which model applies to each tool.
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- Assign an owner accountable for the agent and its credentials.
- Set an expiry or review date for access grants.
- Rotate secrets and revoke unused connectors.
- Review permissions when the agent prompt, tools or data sources change.
- Block production resources by default and add them only with a documented need.
These practices follow the least-privilege and policy-before-execution approach in Microsoft’s agent access guidance.
Test whether the audit trail reconstructs an action
Do not score a platform as having audit logs merely because it emits runtime traces. A useful record lets an investigator reconstruct the chain from the original request to the final outcome without relying on screenshots or an engineer’s memory.
Minimum event fields
| Event stage | Fields that should be searchable |
|---|---|
| Request | Requester identity, conversation or run ID, timestamp, tenant and original user intent |
| Agent decision | Agent version, model or workflow version, selected tool, arguments and confidence or rationale where available |
| Policy check | Policy version, subject, resource, operation, decision, reason and escalation path |
| Approval | Approver identity, role, decision, timestamp, comments, expiration and the exact request approved |
| Execution | Tool or connector, credential identity, target resource, parameters, start and end time |
| Outcome | Success or failure, response code, changed resource state, retry count and downstream identifiers |
Check linkage, not just fields
Every event should share a durable run or transaction identifier. If the agent makes several calls, the log must distinguish the approved call from later calls. If a person edits a payload before approving it, retain both the proposed and approved versions. If a retry occurs, record whether it was automatically authorized or required a fresh policy evaluation.
Verify access and export
Ask who can search the records, whether investigators can export them, and whether the format preserves timestamps, identities and relationships. Confirm integrations with your security information and event management system or data lake, administrator roles required for access, and retention on the exact edition and contract you would buy.
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Separate publication governance from runtime approval
There are two different gates:
- Publication governance: whether an administrator allows an agent to become available to a group or an entire tenant.
- Runtime approval: whether a particular tool call may execute during a run.
Microsoft 365 Admin Center can route an agent submitted for tenant availability to an administrator, who may publish or reject it and scope its audience. That process governs deployment and distribution; it does not prove that a sensitive action will pause before execution. Details are documented in Agent Requests in Microsoft 365 Admin Center.
Publication checklist
- Is there an inventory or registry of agents?
- Does each agent have an owner and documented purpose?
- Can administrators limit the audience by group, department or tenant?
- Are tool, prompt and permission changes subject to a new review?
- Can an administrator disable or roll back an agent quickly?
Do not treat human review as a fail-safe
A review feature improves control only when the trigger, approver and enforcement path are reliable. Microsoft warns: “Don’t rely on human review or clarification requests as a fail-safe or as a guarantee that the system always requests human input before proceeding.” The warning appears in Human supervision of computer use.
Use deterministic gates for high-impact actions even if the agent also asks a person for confirmation. A practical pattern is: policy denies prohibited operations, policy escalates permitted-but-sensitive operations, and a human approves only the exact request that passed the policy check. Test both false negatives (a review was needed but not requested) and false positives (a review was requested unnecessarily).
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Control ownership changes depending on who operates the runtime and the tool.
| Control area | Managed runtime | Application-owned integration |
|---|---|---|
| Policy evaluation | May be offered as a platform feature; confirm scope and edition. | Your service must enforce the decision before calling the external system. |
| Approval UI and resumption | Often supplied by the platform; test timeout and rejection behavior. | You must implement identity, notifications, durable state and safe resume. |
| Audit records | Review event coverage, export and retention. | Join application logs with platform run IDs and preserve the full payload. |
| Tool coverage | Built-in tools may receive centralized controls. | Custom tools may bypass platform policies unless explicitly routed through them. |
| Responsibility | Shared between platform and customer configuration. | Customer owns the enforcement, failure handling and evidence. |
Anthropic’s documentation explicitly says application-owned custom tools are controlled by the application, while its Managed Agents permission policies are labeled beta. Treat that boundary as a procurement requirement: list every connector and prove that each one reaches the same policy and logging path.
Use a repeatable evaluation process
Step 1: Write the risk and data inventory
List users, data sources, tools, resources, environments and operations. Mark regulated or confidential data, production systems, irreversible actions and external recipients. Identify which teams must approve or investigate activity.
Step 2: Convert risks into testable controls
For every consequential operation, specify the identity allowed to request it, the identity allowed to approve it, the policy result that is required, the exact point where execution must stop, and the events that must be logged.
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Step 3: Ask for a live failure demonstration
Have the agent attempt a prohibited operation, reject an approval, let an approval expire, modify parameters after approval and trigger a tool timeout. Confirm that no side effect occurs, that retries do not bypass the gate, and that each branch is visible in the audit trail.
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Step 4: Test least privilege
Use a dedicated agent identity with read-only access, then attempt a write. Add one narrowly scoped write permission and verify that the agent still cannot reach an unrelated resource. Repeat in production-like and nonproduction environments.
Step 5: Trace one action end to end
Start with a natural-language request and follow it through model output, policy evaluation, approval, tool execution and result. Export the records and ask an investigator who was not involved in the test to explain what happened using only those records.
Step 6: Validate operations and retention
Confirm administrator roles, alerting, pause and stop behavior, recovery after service failure, log export, retention, SIEM or data-lake integration, and the process for disabling an agent. Check these details for the exact geography, edition and contract because they can vary and change.
Step 7: Pilot with bounded impact
Begin with read-only or reversible work, a small audience and nonproduction resources. Expand only after policy, approval and audit tests pass repeatedly and owners accept the residual risk.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Score platforms with weighted evidence
A scorecard prevents a polished demonstration from outweighing a missing control. Use weights that reflect your risk; the following categories are a starting framework rather than a universal ranking.
| Category | What to verify | Suggested weight |
|---|---|---|
| Pre-execution enforcement | Tool call pauses before side effects; deny and escalate decisions are server-enforced. | 25% |
| Identity and least privilege | Distinct identity, delegated or app-only clarity, resource and operation scope, read/write separation. | 20% |
| Audit reconstruction | Request-to-outcome linkage, approval evidence, resource identifiers, searchable export. | 20% |
| Deployment governance | Inventory, owner, administrator publication review, audience scoping and change review. | 10% |
| Reliability and recovery | Rejection, timeout, retry, pause, stop, resume and failure handling. | 10% |
| Retention and integrations | Retention period, continuous export, SIEM or data-lake support and role requirements. | 10% |
| Control ownership and fit | Managed versus application-owned tools, existing identity integration and portability. | 5% |
Do not award points for a capability that is documented only for a different plan, region, beta feature or managed tool. Mark it as unverified until the supplier confirms the scope in writing and your own test reproduces it.
Common failure modes and corrective actions
The agent asks for approval after it has already acted
Cause: the approval is a notification or post-action review. Correction: place the policy and approval gate in the server path immediately before the tool call; test with an irreversible operation in a safe environment.
A broad approval authorizes later actions
Cause: approval is attached to the run rather than to a specific operation and payload. Correction: bind approval to tool, operation, resource, parameters and expiry, then require a new decision when any of those change.
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The model can bypass the approved connector
Cause: an alternate custom tool or direct API path is outside the platform policy. Correction: inventory every route to the resource, deny direct credentials, and route custom tools through the same authorization and logging service.
Logs show calls but not authorization
Cause: runtime traces are not governance records. Correction: require policy version, decision, approver and request identifiers, and join them to execution and outcome events before production rollout.
Retries create duplicate side effects
Cause: a timeout is treated as a safe failure even though the remote system may have completed the operation. Correction: use idempotency keys or a status check, record each retry, and require reapproval when the original decision is no longer unambiguously applicable.
Human review is expected to catch every risky action
Cause: probabilistic review requests are treated as a guarantee. Correction: put deterministic deny and escalation rules around high-impact operations, then use human review as an additional control.
Publication approval is mistaken for operational authorization
Cause: an administrator approved the agent for an audience, but no runtime policy governs tool calls. Correction: document the two gates separately and test a sensitive action after publication.
Use the evidence to make a decision
Choose a platform when
- Every consequential tool call stops before execution and can be denied or escalated by a policy outside the model.
- Agent identity, user identity, tool, resource and operation scopes are explicit and narrow.
- Read and write capabilities can be separated and reviewed independently.
- Approval records are bound to the exact action and remain linked to the outcome.
- Administrators can control publication, audience, ownership and changes.
- Retention and export meet your investigation and compliance requirements for the purchased edition.
Keep the agent out of production when
- A sensitive action can execute before approval.
- Custom tools bypass the platform’s policy or logging path.
- The supplier cannot show who approved an action or which resource changed.
- Broad shared credentials are required.
- Rejection, timeout and retry behavior is undefined.
- Logs cannot be exported or retained for the period your organization requires.
What the current evidence says about the market
The MIT AI Agent Index reported that 20 of 30 surveyed agents documented pause or stop mechanisms in its 2025 index. That count indicates that pause controls are becoming common enough to document, but it does not show that every control is enforced before execution or that any particular platform is safe for your workload. Review the underlying 2025 AI Agent Index and test the exact product and configuration you plan to deploy.
Official product documentation illustrates why capabilities must be compared by semantics rather than labels: one guide describes a run pause for human review, another offers always-allow, always-ask and server-evaluated policies, and an administration workflow governs whether an agent is published to a tenant. None of those facts alone establishes complete runtime governance for an unspecified buyer.
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Questions to put in the procurement record
- Which plan, geography and contract include each approval, policy, audit and retention feature?
- Does the control apply to every built-in and application-owned tool?
- Can a policy deny or escalate without model cooperation?
- What exact event proves that execution occurred after approval?
- How are changed parameters, retries, failures and duplicate requests handled?
- Who owns the agent identity, credentials, policy updates and log exports?
- How quickly can administrators disable the agent and revoke its access?
- Can an independent investigator reconstruct a run without access to the model interface?
The best choice is not the platform with the most prominent approval button. It is the one whose controls reliably stop the right actions, limit the agent’s blast radius and leave an evidence trail that explains every decision from request to outcome.
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