Choose an AI agent platform by proving three things before you buy: a sensitive tool call can be stopped before it causes a side effect, permissions limit the agent to the identities, tools and resources it actually needs, and the audit trail can connect the request to the policy decision, approval, execution, changed resource and final result. Treat publication approval as a separate deployment gate from runtime approval during an agent run, and use deterministic policy checks for high-impact actions rather than relying on a model to decide when to ask a person.
What you are actually choosing
An agent platform is more than a model endpoint with a chat interface. It combines an identity, planning or reasoning, tool access, policy enforcement, human intervention, execution infrastructure and records of what happened. The platform you select determines where those controls live and which ones your organization must build.
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The most important question is not whether a product advertises “human in the loop” or “audit logs.” Ask where the control is enforced and what an investigator can prove afterward. A useful design pauses a tool call before execution, identifies the person or policy that allowed it, resumes or rejects the run predictably, and records the resource and outcome. A trace that merely shows model tokens or application messages is not an audit record for a consequential action.
Define risk before comparing platforms
Make a list of every action the agent might take, then classify the effect of an error. This prevents a low-risk read operation from receiving the same friction as a payment, deletion or external communication.
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| Risk tier | Typical operations | Minimum control |
|---|---|---|
| Informational | Search a knowledge base, read a ticket, summarize a document | Read-only identity, resource scoping and searchable execution logs |
| Operational | Create a draft, change an internal record, open a support case | Explicit tool policy, input validation, rollback or correction path, and an attributable actor |
| High impact | Send an external message, submit an order, change access, delete data, run a shell command or modify production | Deterministic deny or escalation rule plus approval before the side effect; record the approver and exact payload |
Write down the business owner, data owner, acceptable delay, recovery method and required evidence for each operation. Those answers become procurement requirements instead of vague promises about safe agents.
Check where approval is enforced
Require a pause before the side effect
An approval control is meaningful only when the run pauses before the tool executes. Verify that the platform can show the proposed operation, target resource and relevant arguments, identify who may approve or reject it, and prevent execution while the decision is pending. Ask what happens when approval is denied, expires, times out or is unavailable. A safe default for a high-impact action is deny, not continue.
The OpenAI Agents SDK guide describes human review before side effects such as cancellations, edits, shell commands and sensitive MCP actions. Its guidance is about SDK workflow design, not a comparative enterprise procurement assessment; you still need to test the exact runtime and tool integration you plan to deploy. See the Agents SDK guardrails and human review guide.
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Understand approval modes
Platforms expose different semantics. Some ask for every call, some let a server evaluate risk, and some allow the application to decide. Anthropic Managed Agents documents always_allow, always_ask and server-evaluated auto policies. The documentation states that Managed Agents permission policies are beta, that auto is not itself a human checkpoint, and that application-owned custom tools remain under application control. Read the current permission policy documentation and verify whether every tool, including custom tools, passes through the same enforcement point.
| Mode | Useful for | Risk to verify |
|---|---|---|
| Always allow | Low-risk, read-only calls with tightly scoped resources | A tool may later gain write capability without a new gate |
| Always ask | Rare or high-impact actions where a person must see every request | Approval fatigue, slow operations and rubber-stamping |
| Server-evaluated policy | Consistent rules based on user, tool, resource or operation | A policy decision may escalate or deny without involving a person; confirm the fallback behavior |
| Application-owned decision | Custom workflows with domain-specific checks | The platform may not see, stop or log actions implemented outside its managed runtime |
Separate model judgment from policy enforcement
Models can recognize context, but their behavior is probabilistic. Microsoft’s guidance on computer-use supervision 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 same system can ask unnecessarily or fail to ask when a person would expect a pause. For high-impact actions, put a deterministic rule outside the model: for example, deny all production deletes, require a named approver for external messages, or allow updates only to records owned by the requesting team. Read Microsoft’s human supervision guidance.
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Limit blast radius with identity and least privilege
Give the agent a distinct, governed identity rather than sharing a developer’s account or a broad service credential. Microsoft recommends a dedicated agent identity, least-privilege scope, separate read and write actions, policy checks before tool execution and audit linkage across the request, decision, execution, resource and outcome. Its access patterns and controls guidance provides the reference model.
Use separate capabilities for reading and writing
Design tools so a read operation cannot silently perform a write. A search tool should not also update records; a draft-message tool should not send; a data-export tool should not delete. Separate credentials or scopes make it possible to allow broad reading while restricting writes to a smaller resource set. If a platform exposes only one combined connector, ask whether an external policy layer can split permissions before the call reaches the connector.
Scope every dimension you can
- Identity: identify the human requester, agent identity and approving principal.
- Tenant or workspace: prevent a run from crossing organizational boundaries.
- Tool: allow only the connectors and functions required by the workflow.
- Resource: restrict records, repositories, environments, mailboxes or folders.
- Operation: distinguish read, create, update, send, submit and delete.
- Time and context: apply expiry, network, device or change-window conditions when your policy requires them.
Ask how permissions are issued, rotated, revoked and reviewed. A platform that can grant a narrow scope but cannot remove it promptly leaves an avoidable exposure after an agent, employee or integration changes.
Demand an audit trail that reconstructs the action
For each consequential operation, an investigator should be able to follow one chain:
- The original user request, including the user and tenant context.
- The agent run and decision that selected a tool.
- The policy evaluated, its version and the resulting allow, deny or escalation decision.
- The human approval or rejection, including approver identity, timestamp and any comment.
- The tool invocation, arguments, connector identity and target resource.
- The execution result, error, retry and any compensating action.
Ask for stable correlation IDs that join these events. Confirm that logs record both successful and denied calls, policy changes, permission changes, retries and manual interventions. A runtime trace that omits the approver, policy or resource cannot establish who authorized a change.
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Check access, export and retention
Determine which administrator roles can search logs, whether investigators can export original records, and whether events can flow to your SIEM or data lake. Verify retention on the exact edition, geography and contract; marketing pages often describe a capability without stating its limits. Check whether sensitive arguments are redacted, whether redaction is configurable, and how the organization preserves evidence when a workspace or agent is deleted.
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Do not confuse publication approval with runtime approval
Organizations often need two different gates. Publication governance decides whether an agent is allowed to become available to a tenant or audience. Runtime approval decides whether a particular tool call may execute during a run.
In Microsoft 365 Admin Center, an agent submitted for tenant availability may be reviewed by an administrator, who can publish or reject it and scope its audience. That process controls deployment; it does not prove that the agent will pause before sending a message, deleting a file or changing a record. Review Agent Requests in Microsoft 365 Admin Center for the publication workflow, then separately test runtime approvals for each sensitive tool.
Compare platform architectures, not just feature lists
| Architecture question | Managed runtime | Application-owned tools or orchestration |
|---|---|---|
| Who runs the tool? | The platform executes a managed connector or action. | Your application executes the code and may call external services directly. |
| Where can policy stop it? | At the platform’s tool gateway, if the connector is covered. | At your application gateway; platform controls may not see the call. |
| Who owns identity and secrets? | Often shared between platform and customer configuration; verify exact responsibility. | Your team manages credentials, rotation and isolation. |
| What must be logged? | Platform run, policy, approval, connector, resource and result. | Those events plus application decisions, downstream API calls and retries. |
| Main trade-off | Faster governance with less implementation control. | More flexibility, but more controls and evidence remain your responsibility. |
Ask vendors to draw the complete request path from user interface to model, policy service, tool gateway, downstream API and log sink. Any arrow that bypasses the policy or audit layer is a potential blind spot.
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Use a repeatable procurement process
- Inventory actions. List every tool, operation, resource type, data class and external side effect. Mark which actions are read-only and which require a person.
- Set policy outcomes. For each action, define allow, deny and escalate conditions. Specify the approver role, maximum waiting period and deny behavior.
- Map identities. Document the human requester, agent identity, delegated or app-only access, service accounts and resource owners. Require revocation and periodic review.
- Trace the event schema. Request the vendor’s fields for policy decisions, approvals, tool calls, resource IDs, results, retries and timestamps. Reject answers that promise “full audit logs” without a schema or sample export.
- Verify coverage. Test managed connectors, webhooks, custom tools, background jobs and direct API calls. Confirm whether each path pauses and logs before side effects.
- Check operations. Observe stop, pause, resume, timeout, retry and human-escalation behavior. Confirm that a rejected call cannot be replayed later without a new decision.
- Confirm compliance fit. Check retention, export, redaction, residency, administrator roles and SIEM integration for the exact edition and contract.
- Run a controlled pilot. Use synthetic records and reversible actions. Require evidence for both successful and denied scenarios before production access.
Test the shortlisted platform with realistic scenarios
Scenario 1: harmless read
Ask the agent to search a permitted repository. Verify that the identity is the agent’s, the resource is in scope, the call is recorded and no write permission is present.
Scenario 2: external send
Ask it to draft and then send a message. The draft may proceed under a lower-risk policy, but sending should pause before dispatch. Reject the approval and verify that no message leaves the system, then approve and confirm the log contains the exact recipient, approver and result.
Scenario 3: out-of-scope resource
Request an update to a record outside the agent’s assigned team. A server-side policy should deny or escalate it even if the model insists that the action is appropriate. Check that the denial is searchable and linked to the original request.
Scenario 4: failed or duplicated call
Force a downstream timeout after approval. Determine whether the platform retries, whether a retry requires a fresh approval, and how operators distinguish an unknown outcome from a confirmed failure. For non-idempotent actions, require an idempotency key or compensating procedure.
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Invoke an application-owned tool. Confirm whether the platform can enforce the same policy and emit the same audit fields. If not, place a policy gateway and logging layer in the application and document that responsibility explicitly.
Evaluate reliability and human operations
Approval is part of an operating process, not just a button. Measure how long approvals wait, who receives them, how queues are covered outside business hours and what happens when an approver leaves the organization. Require escalation for stale requests and a clear emergency stop that prevents new tool calls.
Inspect pause and resume behavior under network failures, browser closure, worker restarts and model retries. A run should not continue because a user interface disconnected, nor should it execute an old approval against changed arguments. The approval record should bind to the tool, resource and payload that were reviewed.
The 2025 MIT AI Agent Index counted pause or stop mechanisms in 20 of 30 surveyed agents. This is a count of documented mechanisms in that Index, not evidence that each mechanism guarantees safe execution; use it as a prompt to inspect implementation details. The source is the 2025 AI Agent Index PDF.
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Account for ownership and portability
Clarify which controls are provided by the platform and which remain with your team. A managed runtime may handle connector authentication, approval prompts and central logs, while your application still owns custom tools, business rules, data classification and downstream API behavior. An application-owned architecture can be portable, but portability also means carrying policy, identity, logging and incident-response code between environments.
Ask whether policies are exportable, whether logs use an open format, how agents are versioned, and whether changing a prompt, tool schema or permission triggers review. Keep an inventory of deployed agents, owners, data access, publication audience and current policy version.
Build a decision scorecard
Use weighted criteria that reflect your risk rather than awarding points for feature names. A high-impact workflow should give the largest weights to pre-execution enforcement and evidence quality.
| Criterion | Evidence to require | Suggested priority |
|---|---|---|
| Pre-execution approval | Live demonstration showing pause, approver identity, rejection and resume behavior before a side effect | Very high |
| Deterministic policy | Server-side deny and escalation rules scoped by identity, tool, resource and operation | Very high |
| Least privilege | Separate read/write scopes, dedicated identity, lifecycle and revocation controls | Very high |
| Audit reconstruction | Correlated request, policy, approval, execution, resource and outcome events with export | Very high |
| Custom-tool coverage | Proof that application-owned calls receive equivalent enforcement and logging, or documented compensating controls | High |
| Retention and integrations | Edition-specific retention, SIEM/data-lake export, redaction and administrator access | High |
| Deployment governance | Agent inventory, owner, administrator publication review and audience scoping | Medium to high |
| Operational reliability | Timeout, retry, restart, emergency-stop and escalation tests | High |
Do not select a platform because it has the longest checklist. Select the one that can demonstrate enforcement and evidence on your highest-impact workflow, with responsibilities your team can operate continuously.
Questions to put in the contract
- Which tool types and custom integrations are covered by pre-execution approval?
- Can administrators enforce deny and escalation policies outside the model?
- Are approval decisions immutable, attributable and exportable?
- What identifiers join a user request, agent run, policy decision, tool call, resource and result?
- What are retention periods, regional limitations and deletion procedures for each log category?
- Can logs stream continuously to our SIEM or data lake, and are there API limits?
- What happens on timeout, retry, worker restart, connector failure or changed arguments?
- Which controls apply to application-owned tools, and which are explicitly our responsibility?
- Does publishing an agent require administrator review, and can audience scope be changed without republishing?
- How are policy, prompt, tool-schema and permission changes versioned and reviewed?
Bottom line
The right AI agent platform is the one that can stop consequential actions before execution, constrain every identity and capability to least privilege, and produce an exportable record that explains who requested, authorized, executed and received the result. Validate those claims with denied calls, custom-tool tests, failure injection and edition-specific retention terms. Publication review helps control who can use an agent; only runtime policy enforcement and auditable approvals control what that agent can do in each run.
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