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Verify an AI agent by auditing the workflow around the model, not by trusting its prompt or final explanation. Define the job as a state machine, enforce authorization in a policy or execution service, require approvals that name the exact action, grant only short-lived permissions, isolate the runtime, and preserve tamper-resistant evidence of every decision and side effect. This approach lets you answer what the agent attempted, who authorized it, what actually ran, and how to stop or recover it when behavior goes outside scope.
What does it mean to verify an AI agent’s work?
An agent combines a language model with tools, external data, memory, planning, and execution privileges. That combination can turn a plausible-looking answer into a chain of real operations: reading records, calling an API, changing shared data, sending a message, or running code. Verification therefore has two parts: proving that each action was permitted, and checking that the resulting output and side effects are correct.
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A model’s explanation is evidence of what it says it did, not proof that it did it. Treat responses, citations, generated code, parameters, equations, interpretations, and conclusions as unverified until an independent control or a trusted baseline checks them. The authorization decision should also be outside the model. A prompt such as “never delete files” is not an access control; a prompt can be ignored, overridden by another instruction, or misinterpreted.
- Authorization evidence: an identity, policy decision, approval, scope, and time limit that match the requested operation.
- Execution evidence: the tool call, normalized parameters, target, result, and timestamps recorded by a component that the model cannot rewrite.
- Outcome evidence: validation against invariants, domain rules, a trusted source, or a test suite.
When any required check is unavailable, the safe behavior is to stop or remain in a non-executing state. Continuing because a policy service, approval record, or audit sink is temporarily unreachable defeats verification.
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How do I represent an agent job as a state machine?
Give the workflow explicit states and allow only documented transitions. Each transition should name the actor or service allowed to cause it, the evidence required, and the fail-closed behavior if a check fails.
| State | Purpose | Typical transition authority |
|---|---|---|
proposed |
The agent has described an intended action, but nothing is authorized. | Agent planner; no side effect permitted |
awaiting_approval |
The action is frozen while a required human or service decision is collected. | Policy service or workflow controller |
authorized |
Identity, scope, policy, and an unexpired approval match the exact action. | Independent authorization service |
executing |
The approved tool invocation is in progress. | Execution component after a final re-check |
blocked |
A policy, identity, scope, sandbox, or approval check denied execution. | Policy or execution component |
failed |
The approved operation ran but returned an error or violated a control. | Tool wrapper or workflow controller |
completed |
The operation finished and its result passed required validation. | Execution component and validator |
revoked |
Previously granted authority was withdrawn, or a running task was interrupted. | Credential, policy, or incident-response service |
Keep the model in the proposal role whenever possible. The execution path should reject a request whose state, actor, target, parameters, or approval token does not match the stored record. A transition such as proposed to executing should not exist for a high-impact action; it must pass through authorization and, where required, approval.
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Record the previous state, next state, actor identity, on-behalf-of user, policy version, reason, correlation ID, timestamp, and a hash or immutable reference to the action specification. Include the decision result and the condition that caused it. If writing this record fails, do not advance the state: an unlogged action cannot be reliably reconstructed.
How should retries and timeouts behave?
Make transitions idempotent and give each proposed action a unique identifier. A retry must either return the prior result or create a new, explicitly approved action; it must not silently reuse an approval for a changed target or parameter. Expired approvals, interrupted executions, and uncertain network outcomes should move to a review or revoked state until the side effect is reconciled.
Which agent actions need a human approval gate?
Classify every tool operation by consequence before deployment. Narrow, read-only retrieval can often run with standing policy approval. Actions that alter external state, expose information, spend money, or affect safety should require a fresh, specific decision tied to one execution.
| Action class | Examples | Recommended control |
|---|---|---|
| Read-only, bounded | Retrieve an allowed document, query a restricted dataset, calculate from supplied values | Pre-approved scope, read-only identity, output validation, and logging |
| Write or communicate | Update shared records, publish content, send an email or message, create a ticket | Exact-action approval or a narrowly defined business rule; validate destination and content |
| Destructive or irreversible | Delete data, revoke access, overwrite a production object | Specific human approval, one-time authorization, confirmation of target and rollback plan |
| Financial or contractual | Purchase, transfer funds, accept terms, issue a refund | Named approver, transaction limits, independent verification, and replay protection |
| Privilege or environment change | Install software, alter permissions, create credentials, change network rules | Just-in-time elevation, separate administrator identity, approval expiry, and immediate revocation |
| Safety-relevant decision | Actions that could affect health, physical systems, or emergency response | Human-in-the-loop review by a qualified authority and independent domain checks |
Risk is contextual. A read operation may still be high impact if it exposes regulated or confidential data, and a write may be low impact in a disposable test environment. Define the tier from the target, data sensitivity, reversibility, reach, and potential harm—not from the tool’s name.
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An approval gate is a separate decision point between an agent’s proposal and the executor. The approver authorizes a normalized action specification, and the executor independently checks that specification immediately before running it.
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1. Create a canonical action specification
Normalize values before approval so that aliases, reordered parameters, or hidden defaults cannot change the meaning after sign-off. Include:
- the agent identity and the user on whose behalf it acts;
- the tool or operation name;
- the exact target resource and destination;
- normalized parameters, including amounts, filters, recipients, and file paths;
- the reason or task identifier;
- creation time, expiry time, and whether the authorization is one-time;
- a unique approval ID and replay-protection value.
2. Present the consequential details to the approver
Show the actual target, data to be disclosed or changed, expected side effect, reversibility, and any uncertainty. Do not ask someone to approve a vague summary such as “finish the task.” A person should be able to compare the displayed action with the canonical specification that the executor will receive.
3. Bind the decision cryptographically or immutably
Store the approval with the action specification, approver identity, timestamp, decision, and expiry. A signed token or immutable record should cover the normalized parameters so that changing one character invalidates the authorization. Mark a one-time approval as consumed atomically with execution.
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4. Re-check at the execution boundary
OWASP’s guidance is explicit: “Separate decision-making from execution. The agent can propose an action, but a policy service or execution component should independently validate scope, privilege, and approval state before execution.” Re-check the caller, on-behalf-of user, current policy, target, parameters, token status, and expiry immediately before invoking the tool. A previously approved plan is not permission to improvise additional calls.
5. Handle denial, expiry, and changed plans safely
A denial is a terminal decision for that action, not a request for the model to keep trying with slight variations. If the plan changes, create a new action specification and obtain a new decision. On expiry, revoke any temporary credential and return the workflow to approval rather than silently extending the old authorization.
How do I enforce least-privilege access at runtime?
Least privilege grants only the files, tools, data, destinations, operations, and time needed for the current subtask. Start with read-only access, then add a narrowly scoped capability only when a specific step requires it.
- Use dedicated identities: give each agent, workflow, or run an identity that is separate from a human administrator and from other agents.
- Scope resources precisely: allow named records, directories, API methods, database views, and network destinations instead of broad roles.
- Separate capabilities: do not combine reading sensitive data, sending externally, and changing permissions in one unrestricted tool.
- Issue short-lived credentials: prefer ephemeral tokens tied to the task and audience over static secrets embedded in prompts or environment files.
- Elevate just in time: grant exceptional privilege only for the approved subtask, then revoke it immediately when the step ends or is cancelled.
- Constrain data movement: prevent an agent that can read an internal source from sending arbitrary content to an external destination.
The Canadian Centre for Cyber Security recommends limiting entitlements to exact resources, operations, and timeframes and replacing static secrets with ephemeral credentials. A policy check should occur both when a capability is issued and when the tool call is made; permissions can change while a plan is waiting for approval.
How should I sandbox an AI agent?
Run the agent in an environment that limits local files, processes, network egress, available tools, and data movement. NCSC advises: “Always run AI agents within a sandboxed environment that controls and manages what resources can and cannot be communicated with, both locally and over a network.”
Control local and process access
- Mount only the working directory and datasets required for the task.
- Block access to host credentials, metadata services, device files, and unrelated processes.
- Use separate containers, virtual machines, or equivalent isolation for untrusted code and higher-risk tools.
Control network and tool access
- Allow-list outbound hosts and methods; deny arbitrary internet access by default.
- Expose tools through wrappers that validate parameters, enforce rate and size limits, and redact sensitive output.
- Prevent a read-only task from discovering or invoking administrative tools.
Control data egress
Classify data before it enters the model context and before it leaves the sandbox. Enforce destination policies at the gateway, not through a model instruction. Log attempted as well as successful transfers so that blocked exfiltration is visible.
How do I verify an agent’s outputs and side effects?
Validate both what the model produced and what the external system actually changed. A successful HTTP response or a confident explanation does not establish that the intended result is correct.
Check against trusted sources and invariants
Compare consequential values with an authoritative record, a second independent calculation, or a defined invariant. Examples include ensuring that a transfer amount matches the approved amount, a database update affects only the named rows, and a generated configuration passes syntax and policy checks.
Test generated code and commands
Run generated code in a disposable environment with test data and restricted permissions. Check dependencies, file paths, network calls, error handling, and resource consumption before allowing promotion. For shell commands, parse and validate the command structure rather than relying on a text filter.
Record uncertainty and abstention
Store confidence signals, missing inputs, validator results, and any refusal or abstention. An agent that declines an unsafe action is producing an important control signal; do not overwrite it with a forced completion.
Reconcile side effects
After execution, query the target system using an independent identity or read path and compare the observed state with the approved specification. If the result is ambiguous because of a timeout, mark the action for reconciliation instead of retrying a potentially duplicated operation.
What must an AI-agent audit log contain?
A useful audit trail connects the human request, model proposal, policy decision, approval, tool invocation, and observed result through one correlation ID. Store it in append-only or tamper-evident storage that the agent cannot modify.
| Evidence | Why it matters |
|---|---|
| Agent identity and version | Shows which principal and software produced the proposal |
| On-behalf-of user | Links automation to the requesting human or service |
| State transition and policy decision | Explains why execution was allowed, blocked, or revoked |
| Approval ID, approver, decision, timestamp, expiry | Proves who authorized the exact action and whether authority was current |
| Tool, target, normalized parameters | Captures the operation that was actually requested, not only a natural-language summary |
| Result, error, validator output, and side-effect record | Distinguishes attempted, successful, and verified completion |
| Correlation ID and timestamps | Allows reconstruction across services and detection of ordering anomalies |
Protect logs with restricted write and read roles, clock synchronization, retention rules, and integrity checks. NIST highlights tamper-proof audit, non-repudiation, and binding agent actions to human authorization as central identity questions. Test whether an operator can reconstruct a run after a partial outage, not merely whether a log line exists during normal operation.
Which human-oversight model fits an agent?
Choose oversight according to consequence and reversibility, then document the boundary in policy.
| Model | How it works | Suitable use |
|---|---|---|
| Human-in-the-loop | A person approves actions before they happen. | Irreversible, financial, privacy-sensitive, or safety-relevant operations |
| Human-on-the-loop | The agent runs within defined limits while a person monitors and can intervene. | Repetitive, bounded operations with reliable detection and interruption |
| Human-out-of-the-loop | The agent acts autonomously without human review. | Only low-impact tasks with strong containment, validation, and recovery |
“Human oversight” is meaningful only when the person has enough context, time, and authority to stop the action. A notification sent after an irreversible change is monitoring, not pre-action approval.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can I implement verification from scratch?
Build the controls in this order so that an agent cannot gain broad authority before you can observe and stop it.
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- Define risk tiers and red lines. Mark operations that always need approval, operations allowed under a standing policy, and operations that are prohibited. Include stop conditions and a maximum task duration.
- Implement the state machine. Persist state and reject undocumented transitions. Make policy, approval, and audit failures fail closed.
- Separate proposal from execution. Route model output to a policy or execution service that constructs the canonical action specification and performs the final checks.
- Issue scoped runtime credentials. Start read-only, use dedicated identities and ephemeral tokens, and revoke elevation after each exceptional step.
- Add the sandbox and egress controls. Restrict files, processes, network destinations, tools, and data transfer before connecting production systems.
- Add validators and reconciliation. Check outputs, run generated code safely, verify invariants, and compare the target’s observed state with the approved action.
- Make the audit trail durable. Capture identity, approval, policy, parameters, result, and correlation data in tamper-evident storage.
- Exercise recovery paths. Test interruption, credential revocation, approval expiry, replay attempts, failed logging, sandbox escape attempts, and ambiguous tool timeouts.
What are the common verification failures?
Relying on the system prompt
Symptom: The agent is told not to delete, disclose, or purchase, but the tool still has permission. Fix: Enforce the rule in the application, operating system, scheduler, database, API gateway, or policy service and deny the tool call there.
Approving a plan instead of an action
Symptom: A human approves “clean up the account,” and the agent chooses the records and operations later. Fix: Approve normalized targets and parameters, and require a new decision when the plan changes.
Giving one identity too much power
Symptom: The same credential can read sensitive data, change permissions, and send externally. Fix: Split capabilities, use task-scoped identities, and issue just-in-time elevation only for the exceptional step.
Logging only the model transcript
Symptom: The chat says an action succeeded, but there is no tool-level evidence or target-state check. Fix: Log policy decisions, exact parameters, tool results, and independent reconciliation.
Retrying after an uncertain result
Symptom: A timeout causes the agent to submit the same payment, message, or deletion again. Fix: Use idempotency keys, query the target for the first result, and move the action to reconciliation until its state is known.
Allowing the audit path to fail open
Symptom: Execution continues when the audit service is unavailable. Fix: Treat an unwritable evidence record as a blocked transition, with an operational queue for safe retry.
Frequently Asked Questions
How can I tell whether an agent used a tool it was not supposed to use?
Compare the tool-invocation stream with the capability inventory and the policy decision for the run. Alerts should fire on an unregistered tool, a destination outside the allow-list, or a parameter that differs from the approved specification; the corresponding credential should then be revoked while the run is investigated.
What should happen when an approver changes their mind after execution starts?
The approval service should support revocation, and the executor should check revocation for long-running work or before each consequential sub-operation. Stop the task, invalidate temporary credentials, and use a compensating action or rollback where the target system supports one.
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Treat every consequential call as its own action with a parent correlation ID. A plan-level approval can establish context, but each step still needs a policy check, a scope limited to that step, and a recorded result before the next transition.
Is a complete log enough to prove an agent behaved safely?
No. Logs establish what was requested and observed, but safety also depends on enforced permissions, isolation, independent validation, and tested recovery. Periodically replay representative runs in a sandbox and attempt denied actions to confirm the controls still work.
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