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To verify what an AI agent did, verify the workflow around the model—not the model’s explanation. Put every task in an explicit state machine, enforce authorization in a separate policy or execution service, require approvals that name the exact action, limit the agent’s identity and tools to the minimum needed, and retain tamper-resistant evidence for each transition and side effect. This approach lets you prove what was proposed, who authorized it, what actually ran, and how the system responded when a check failed.
AI agents combine a language model with tools, external data, memory, planning, and execution privileges. That combination can produce cascading failures, so a prompt such as “never delete files” cannot be treated as a security control. The controls below apply whether the agent is supervised continuously, approved one action at a time, or allowed to operate autonomously within a narrowly bounded task.
What exactly must be verified when an AI agent performs work?
Verification covers four separate questions: what the agent intended to do, what it was allowed to do, what it actually did, and what changed in the outside world. A model response is only a proposal until an independent component checks policy and executes the operation.
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- Authority: the agent identity, the user it acts for, the applicable policy, and any approval artifact.
- Execution: the tool, target, inputs, result, timestamps, and state transitions.
- Effect: changes to files, records, accounts, messages, purchases, code, or other systems, plus the checks that confirmed those changes.
Keep these records distinct. A transcript can show what the model said, but it cannot prove that a database accepted a command, that a permission was valid at execution time, or that an approval was not replayed. Those facts belong in policy and execution logs.
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How do I model an agent workflow as a state machine?
Define states and permitted transitions before connecting tools. Each transition should have an allowed actor, required evidence, and a fail-closed result if policy, approval, or logging is unavailable.
| State | Meaning | Who may cause the transition | Required evidence |
|---|---|---|---|
proposed |
The agent has described an action but nothing has run. | Agent planner | Task ID, initiating user, tool, target, normalized parameters, and reason. |
awaiting_approval |
The action needs a human or an approved policy decision. | Policy service | Risk tier, requested scope, approver requirements, and expiry. |
authorized |
Checks passed and a single execution is permitted. | Policy service or authorized approver | Approval artifact bound to the exact action, identity, scope, time, and correlation ID. |
executing |
The executor has started the operation. | Execution service | Fresh policy decision, credential lease, start time, and immutable action hash. |
blocked |
A policy, identity, scope, or environment check denied the operation. | Policy service or executor | Decision code, violated rule, and attempted parameters. |
failed |
The operation started but did not complete successfully. | Executor or target system | Error class, partial-effect status, retry decision, and rollback status. |
completed |
The target system confirmed the intended result. | Executor after validation | Result, postcondition checks, output hash, and completion time. |
revoked |
Authority or a running operation was withdrawn. | Credential service, policy service, or human operator | Revocation reason, time, affected leases, and interruption outcome. |
Do not let a model jump directly from proposed to completed. The executor should reject unknown transitions, stale state versions, duplicate correlation IDs, and attempts to resume an expired approval. A policy lookup failure, approval-store outage, or audit-write failure should move the action to blocked rather than silently allowing it.
How should transitions be represented?
Give every task and transition a correlation ID. Store the previous state, new state, actor, decision, timestamp, and a hash of the normalized action. Normalization matters: sort object keys, canonicalize paths and URLs, represent quantities and units consistently, and remove ambiguous aliases before an approval is requested. The executor then compares the approved hash with the action it is about to run.
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Which agent actions require a fresh approval?
Classify operations by consequence, not by how confident the model sounds. Read-only work can often use standing, narrowly scoped authorization; operations that change external state should require a specific decision immediately before execution.
| Risk tier | Examples | Typical control | Additional verification |
|---|---|---|---|
| Low | Read a named document, query a fixed reporting view, calculate a value locally. | Pre-approved policy with read-only identity and fixed resources. | Check target and query shape; log the returned object identifiers. |
| Moderate | Create a draft, open a ticket, write to a private workspace, call an allow-listed API. | Task-scoped token and policy check at execution. | Validate destination, payload schema, and rate limits. |
| High | Send an external message, publish content, change configuration, install software, or write shared data. | Fresh, action-specific human approval or dual control. | Show a rendered preview, compare parameters with the approved version, and verify the postcondition. |
| Critical | Delete records, transfer funds, alter permissions, expose sensitive data, or make a safety-relevant decision. | Explicit approval with short expiry, least-privilege elevation, and independent execution service. | Require separation of duties, replay protection, transaction limits, and an interruption or rollback plan. |
DOE GEAR states the principle plainly: “A prompt such as ‘never delete files’ is not an access control.” The control must exist in the application, operating system, scheduler, database, API gateway, or policy service that can actually deny the operation.
How do approval gates work for agent tools?
An approval gate is a transaction, not a chat message. It authorizes one normalized action for a defined identity, target, scope, and time window; the executor independently rechecks every element just before running it.
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What an approval record should contain
- Agent identity and the initiating user or service acting on whose behalf it operates.
- Tool name and version, target resource, destination, and operation.
- Canonical parameters, including paths, filters, quantities, recipients, and data classifications.
- The reason for the action and the policy rule or risk tier that triggered approval.
- Approver identity, authentication strength, decision time, expiry time, and whether the approval is one-time.
- A nonce or unique authorization ID, an action hash, and the task correlation ID to prevent replay.
What the executor must recheck
- Confirm that the agent identity and on-behalf-of user are still valid.
- Fetch the current policy rather than trusting a cached decision.
- Compare the action hash and every normalized parameter with the approved record.
- Check that the approval has not expired, been revoked, or already consumed.
- Issue a short-lived credential containing only the permitted operation and resource.
- Write an execution-start event before invoking the tool. If that write fails, do not proceed.
OWASP recommends separating decision-making from execution: the agent may propose an action, while a policy service or execution component independently validates scope, privilege, and approval state. This separation also makes it possible to replace the model without rewriting the authorization boundary.
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How do I enforce least privilege at runtime?
Least privilege is multidimensional. Grant only the files, tools, data, destinations, operations, and time required for the current subtask, then remove the grant when that subtask ends.
| Dimension | Restrictive design | Common mistake |
|---|---|---|
| Identity | Dedicated agent identity tied to a specific workload and on-behalf-of user. | Sharing a human administrator account or a broad service account. |
| Resources | Named files, tables, records, buckets, or projects with explicit scopes. | Granting an entire drive, database, or cloud project “for convenience.” |
| Operations | Separate read, create, update, publish, delete, and permission-changing verbs. | Giving a general read-write role when only one verb is needed. |
| Destinations | Allow-listed hosts, queues, repositories, and recipients. | Unrestricted network egress or arbitrary webhook calls. |
| Data | Field-level filtering, masking, and classification-aware transfer rules. | Sending full records when a few fields answer the task. |
| Time | Short-lived credentials and just-in-time elevation with automatic expiry. | Permanent keys stored in prompts, environment variables, or agent memory. |
The Canadian Centre for Cyber Security recommends limiting entitlements to exact resources, operations, and timeframes and replacing static secrets with ephemeral credentials. A temporary elevation should be revoked immediately after the exceptional operation, including when the task fails or a human cancels it.
How should credentials be handled?
Keep secrets outside prompts, model context, conversation history, and long-term memory. The executor should obtain a lease from a secrets or identity service only after policy approval, pass the lease to the isolated tool process, and invalidate it on completion, timeout, or revocation. Logs should contain the credential ID and scope, never the secret value.
How do I contain an agent so a mistake has a small blast radius?
Run the agent in a sandbox that controls local files, processes, network egress, available tools, and data movement. NCSC advises that agents always operate in a sandboxed environment controlling what resources can be communicated with locally and over a network.
- Mount only a task-specific working directory, preferably read-only except for an output area.
- Use a separate process or container identity with no host administration rights.
- Block arbitrary child processes, package installation, device access, and shell commands unless each is explicitly required.
- Permit outbound traffic only to named services and ports; deny direct access to internal control planes.
- Apply file-type, size, and content-classification limits to uploads and downloads.
- Expire the sandbox and destroy temporary data when the task ends, while retaining the audit evidence required for investigation.
Sandboxing is not a substitute for authorization. A compromised agent inside a permissive sandbox can still misuse every tool it was given, so environment boundaries and per-action policy checks must operate together.
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How can I verify an agent’s outputs and side effects?
Treat model responses, citations, generated code, parameters, interpretations, and conclusions as unverified. Verification should use independent baselines, invariants, and tests appropriate to the consequence of the result.
For data retrieval and analysis
- Check that the query touched only approved datasets and filters.
- Validate types, units, date ranges, row counts, and missing-value behavior.
- Recalculate critical figures with an independent implementation or trusted reference.
- Record abstentions, uncertainty, and unsupported claims instead of converting them into confident prose.
For generated code or configuration
- Parse the output with a real parser before execution; reject malformed or mixed-language content.
- Run static analysis, dependency checks, unit tests, and policy tests in the sandbox.
- Compare permissions, network calls, and file operations with an allow-list.
- Deploy first to a disposable environment and verify health checks before any promotion.
For external side effects
- Render the exact message, record change, purchase, or publication for the approver.
- Use idempotency keys and transaction limits so retries cannot duplicate effects.
- Read the target system after execution and verify the intended postcondition, not merely a successful HTTP response.
- Capture the target system’s receipt, version, or transaction ID and associate it with the action record.
What should an AI-agent audit trail contain?
Durable evidence should let an investigator reconstruct a task without trusting the model’s narrative. Log the agent identity, on-behalf-of user, state transition, policy decision, approval record, tool, target, normalized parameters, result, timestamps, and correlation ID.
Protect the trail against alteration and deletion with append-only storage, restricted write identities, synchronized time, integrity hashes, and independent retention controls. Include both successful and denied attempts, policy lookup failures, approval expiry, credential issuance and revocation, sandbox violations, human interruptions, and postcondition results. NIST identifies tamper-proof audit, non-repudiation, and binding agent actions to human authorization as key identity questions.
Separate sensitive payloads from searchable metadata when possible. Store a cryptographic digest and a controlled reference to the payload so investigators can prove which content was used without making every log reader a data custodian.
How should the system fail, recover, and support interruption?
Design failure behavior before production. Authorization, execution, and evidence failures should stop the affected action and expose enough detail for a safe decision.
- Policy service unavailable: enter
blocked; do not fall back to the model’s recommendation or a cached allow decision. - Approval expired or already consumed: reject the action and require a new approval with fresh parameters.
- Audit write fails: do not execute; queue only non-sensitive diagnostic information if the logging design explicitly supports it.
- Tool times out after partial work: mark
failed, query the target for actual effects, and run a compensating action only under a separately authorized procedure. - Credential or identity revoked: terminate leases, interrupt running processes, and record which operations were completed before revocation.
- Human interruption: stop at the next safe boundary, invalidate pending approvals, and preserve the interruption event.
Test these paths deliberately: approval replay, stale state versions, sandbox escape attempts, failed logging, network partition, credential revocation, duplicate delivery, and rollback of a partial change. A recovery test is successful only when the records show what happened and the remaining authority is actually removed.
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Which oversight model fits a particular agent?
Choose oversight according to consequence and reversibility, not novelty. NCSC describes three models:
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|---|---|---|---|
| Human-in-the-loop | A person approves actions before they happen. | Deletion, publication, permission changes, financial actions, and safety-relevant decisions. | Approval fatigue and slower throughput if low-risk work is routed through the same queue. |
| Human-on-the-loop | The agent acts within policy while a person monitors and can intervene. | Repetitive, bounded operations with strong monitoring, rate limits, and easy interruption. | Intervention may arrive after an irreversible side effect. |
| Human-out-of-the-loop | The agent acts autonomously without human review. | Only narrow, reversible tasks with strict scopes, tested invariants, and automatic shutdown. | A policy or model error can propagate before anyone sees it. |
You can mix models within one task. For example, allow autonomous read-only discovery, require approval to change a shared record, and require dual approval to alter permissions. The state machine should record the oversight level selected for each action.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I implement verification step by step?
Build the control plane first, then connect tools one at a time. The sequence below avoids creating an agent whose permissions cannot later be reconstructed.
- Inventory tools and effects. List every operation, target type, data class, destination, reversibility, and possible consequence.
- Define identities. Create dedicated agent identities and map each request to the initiating user or service.
- Write the state machine. Specify states, legal transitions, transition actors, evidence, timeouts, and fail-closed behavior.
- Set risk tiers. Decide which operations are pre-approved, which need one-time approval, and which require separation of duties.
- Canonicalize actions. Normalize paths, URLs, parameters, quantities, and recipient identifiers before presenting an approval.
- Implement the policy service. Keep authorization outside the model and evaluate identity, scope, context, destination, and time at execution.
- Issue ephemeral authority. Mint a short-lived, narrowly scoped credential only after all checks pass; revoke it on completion or interruption.
- Isolate execution. Apply sandbox limits for files, processes, network, tools, and data egress.
- Add independent verification. Check schemas, invariants, tests, trusted baselines, and target-system postconditions.
- Make evidence durable. Record decisions, transitions, parameters, results, and failures in append-only or integrity-protected storage.
- Exercise recovery. Simulate expiry, replay, denial, partial success, failed logging, revocation, interruption, and sandbox violations.
- Expand gradually. Start with read-only tasks, review evidence quality, then add one reversible write operation at a time.
What common designs fail, and how can I fix them?
“The system prompt forbids dangerous actions.”
Prompts influence behavior but cannot enforce permissions. Replace the instruction with a policy rule that the executor can deny, and log the denial.
“The approval covers the whole task.”
Broad approval lets a changed plan inherit authority it was never reviewed for. Approve a normalized action or a narrowly defined set of actions, with an expiry and a one-time nonce.
“The agent uses a powerful account but promises to be careful.”
Reduce the account to exact resources and verbs, use an ephemeral lease, and separate read and write identities. A careful model cannot compensate for administrator-level credentials.
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“A successful tool response proves success.”
Tool responses can be incomplete, forged, or disconnected from the target’s final state. Query the target for a postcondition and retain its receipt or version identifier.
“We log the conversation, so we can audit it.”
Conversation logs omit policy decisions, credential scope, normalized parameters, and external effects. Add structured execution and authorization events linked by a correlation ID.
“Fail-open keeps the workflow available.”
Availability is not a justification for bypassing authorization or evidence. Block the operation, surface the dependency failure, and provide a controlled retry path.
What should I review before granting an agent more autonomy?
- Every tool operation has an owner, risk tier, target scope, and reversible or compensating procedure.
- The model cannot directly grant itself tools, credentials, destinations, or policy exceptions.
- Approvals bind an identity, exact parameters, expiry, nonce, and correlation ID.
- Execution rechecks policy and approval immediately before the side effect.
- Credentials are short-lived, task-scoped, and revoked after the subtask.
- The sandbox limits files, processes, network egress, and data movement.
- Consequential outputs are tested against independent constraints or trusted baselines.
- Logs are complete, integrity-protected, searchable, and linked to human authorization.
- Operators have tested interruption, revocation, replay rejection, partial failure, and recovery.
- Autonomy is expanded only after evidence shows that these controls work under denial and failure, not just on successful runs.
Frequently Asked Questions
Can an AI agent approve its own tool calls?
It can propose a call, but self-approval is not an independent authorization boundary. A separate policy or execution service should evaluate the agent identity, user context, scope, and risk, and require a human or pre-approved rule when the action is consequential.
How long should an agent approval remain valid?
Use the shortest window that accommodates the task, often a single execution or a short-lived lease. Expire approvals when parameters, target, identity, or relevant policy changes, and force a new decision instead of silently extending the old one.
What is the safest way to retry a failed agent action?
First determine whether the target applied the action partially or completely. Use an idempotency key, verify the postcondition, and retry only with a still-valid authorization; never blindly replay a side-effecting request.
Should agent audit logs include the full data the model saw?
Not necessarily. Keep searchable metadata, hashes, classifications, and controlled references in the audit trail, while storing sensitive payloads under separate access and retention rules. This preserves evidentiary linkage without giving every log reader access to private data.
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