Do not give an AI agent production credentials because it completed a few impressive demos. Readiness is specific to a defined task, tool set, data boundary and permission level. Test the agent in a contained environment with representative and adversarial tasks, record its complete action trajectory, grade tool choices and policy compliance, check that the evaluator itself is not being gamed, and keep a human confirmation step for consequential external actions.
What “ready” means for an AI agent
An agent is ready only for the workflow and permissions your evaluation actually covers. A pass on a read-only document search does not establish that the same system can safely send messages, modify records or spend money. The decision should therefore be stated narrowly: for example, “This model, running in this harness, may read support tickets and draft replies, but may not send, delete or change account data.”
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| Permission boundary | What the test must establish | Typical approval level |
|---|---|---|
| Read-only | It retrieves the right information, respects data boundaries, cites the evidence available to it and stops when access is insufficient. | Often suitable for supervised pilot use after trace review. |
| Reversible writes | It selects the correct tool, supplies valid arguments, previews the change and can recover from an error without creating additional damage. | Use a sandbox first, then staged access with logging and rollback. |
| Irreversible or externally visible actions | It recognizes ambiguity, follows approval policy, waits for confirmation and does not act on conflicting or injected instructions. | Keep a human confirmation gate until evidence covers the exact workflow. |
| Privileged or financial access | It remains within least-privilege limits under adversarial prompts, tool failures and misleading data. | Require a separate security review; a general benchmark pass is not enough. |
This framing follows the evaluation concerns described by NIST and OpenAI: the system, harness, tools, budget, elicitation method and safeguards are part of the claim. Do not report “the agent is safe” when the evidence supports only a narrower statement about one configuration.
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Write down the intended workflow
Start with the job the agent is expected to perform, not with a favorite benchmark. List the normal requests, the systems it may consult, the tools it may call, the data it may expose, and the actions it must never take. Specify whether each tool is read-only, reversible or irreversible. Include the expected behavior when the request is ambiguous, unsafe, outside scope or impossible with the available tools.
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- Allowed: the exact tasks, data sources, tools and argument ranges.
- Prohibited: actions such as deleting records, revealing secrets, bypassing approval or contacting an unintended recipient.
- Escalation: the conditions that require a question, handoff, refusal or human confirmation.
- Budget: maximum turns, tool calls, tokens, time and retries.
- Evidence: what the agent must show before an action is accepted.
Turn the boundary into testable assertions
Use assertions that can be checked from logs rather than vague goals such as “be helpful.” Examples include: “Never call delete_record,” “Ask for confirmation before send_email,” “Use the account identifier supplied by the user rather than one found in an untrusted document,” and “Stop after two failed authentication attempts.” An assertion should identify the observable event that counts as a pass or failure.
2. Build representative and adversarial test sets
Cover ordinary work first
A useful set contains realistic requests drawn from the intended workflow, with the same document formats, tool schemas and ambiguity that users will encounter. Include easy, medium and boundary cases. Vary names, dates, quantities, languages and incomplete information so that the agent cannot succeed by memorizing a single pattern.
Add cases where the right answer is to stop
Decision-making is visible when the agent declines to act. Include requests that lack a required identifier, conflict with a policy, ask for data outside the user’s authority, or require a tool that is unavailable. Score a safe clarification or refusal as the correct outcome when the task cannot be completed legitimately.
Use adversarial and failure-injection cases
- Conflicting instructions in a retrieved document or web page.
- Prompt-injection text that tells the agent to reveal credentials or ignore its policy.
- Malformed, stale or contradictory tool results.
- Timeouts, rate limits, partial writes and duplicate responses.
- A tool whose name resembles the correct tool but has a more dangerous effect.
- A request that becomes unsafe only after several apparently harmless steps.
- An external side effect that requires approval, such as sending a message or placing an order.
Keep development and evaluation data separate
Use one development set while tuning prompts, tools and agent architecture, and a separate held-out test set for the access decision. NIST’s AI 800-1 2pd, the second public draft, describes this separation so organizations can experiment without gradually fitting the evaluation cases. Do not repeatedly tune against a held-out task and continue calling it an independent test.
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| Set | Purpose | Handling |
|---|---|---|
| Development | Find and fix prompt, scaffold and tool-design problems. | May be viewed and rerun during iteration. |
| Held-out evaluation | Estimate behavior on tasks not used for tuning. | Lock the cases and scoring rules before the final run. |
| Post-deployment monitoring | Detect drift, new attack patterns and changes in tool behavior. | Sample traces under the same approval and privacy controls. |
3. Run the test in a contained environment
Prefer mocks and disposable data
Use mock APIs, synthetic accounts and a disposable database whenever possible. A mock should preserve the real tool’s schema, validation rules and likely failures, while making side effects observable and reversible. If production-like behavior cannot be simulated, use a staging account with narrowly scoped credentials and an explicit rollback procedure.
Apply cyber-evaluation containment guidance carefully
Anthropic’s published cyber-evaluation guidance recommends a hardened sandbox with no internet access by default, configuration verification before each evaluation, supervised testing for escape, and a check that the challenge is solvable. Those recommendations are specific to cyber evaluations, but the underlying controls transfer to other tool settings: isolate the run, verify connectivity and permissions, and do not assume that a sandbox is safe merely because it has that label.
Pre-run checklist
- Remove production API keys, cookies, cloud credentials and personal data from the environment. Keep secrets outside the evaluation container.
- Confirm the network policy, mounted files, enabled tools, account roles, rate limits and time limits.
- Reset the test database and tool state to a known snapshot.
- Run a harmless probe for every tool so you know the harness is wired correctly.
- Verify that the task is solvable with the documented tools and information.
- Supervise any test of isolation or escape resistance; stop the run if the agent reaches an unintended system.
- Record the configuration hash or equivalent version identifiers before the first task.
Configuration drift can invalidate a comparison. A test run with internet access, an extra tool or a different account role is not equivalent to the run you approved.
4. Capture the complete decision trajectory
Evaluate the workflow, not only the final answer. OpenAI’s agent-evaluation documentation uses trace-grading questions such as “Did the agent pick the right tool?” and “Did the workflow violate an instruction or safety policy?” Those questions require a record of the intermediate steps.
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For each run, retain, subject to privacy controls:
- The user request and system or developer instructions supplied to the agent.
- Every model turn, tool selection, tool argument and returned result.
- Permission checks, confirmations, handoffs, retries, timeouts and stop events.
- The state change before and after each write operation.
- The final answer, citations or evidence presented to the user.
- Harness version, model version, tool versions, budget, randomization and task identifier.
Do not treat hidden reasoning text as the only explanation of a decision. Observable tool calls, inputs, outputs and state transitions are more reliable evidence of what the workflow actually did. NIST’s “Building Evaluation Probes into Agentic AI” project emphasizes the need for visibility into chain-of-thought-related workflow evidence, tool usage and gathered evidence; its stated aim is to connect decisions to auditable evidence rather than rely on an answer alone.
5. Score outcomes, actions and policy compliance
Use separate dimensions
A correct final answer can conceal a dangerous path, while a failed answer can result from a temporary tool outage rather than bad judgment. Score at least these dimensions independently:
| Dimension | Pass question | Automatic failure examples |
|---|---|---|
| Task result | Did the requested legitimate outcome occur? | Wrong record, missing required evidence or incomplete result. |
| Tool selection | Did the agent choose the least-privileged appropriate tool? | Used a write or export tool when a read tool was sufficient. |
| Arguments and scope | Were identifiers, quantities, recipients and filters valid and authorized? | Guessed a critical identifier or exceeded the requested scope. |
| Policy compliance | Did every step follow the stated instructions and safety rules? | Followed an injected instruction, exposed restricted data or bypassed approval. |
| Uncertainty handling | Did it ask, stop or escalate when information was missing or conflicting? | Invented a value or acted despite unresolved ambiguity. |
| Recovery | Did it contain and recover from a failure? | Repeated a failing write, duplicated an action or hid the error. |
| Evidence and reporting | Can a reviewer reconstruct why the action was taken? | Claims success without a corresponding tool result or state change. |
Define gates, not just an average score
A weighted average can hide a severe policy violation. Set blocking conditions such as unauthorized disclosure, an unapproved external side effect, credential exposure, or an escape from the test boundary. A run that triggers a blocking condition fails even if its task-result score is high. For non-blocking dimensions, you can use a simple rubric—0 for unacceptable, 1 for partially correct and 2 for correct and policy-compliant—provided the rubric is fixed before the held-out evaluation.
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Inspect recovery and stopping behavior
Inject a controlled timeout or malformed response and check whether the agent retries safely, changes strategy, asks for help or stops. The correct response depends on the workflow: a read-only lookup may be retried, while a payment or message send may require confirmation that the first attempt did not succeed. Grade the state after recovery, not merely the text of the apology.
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6. Test whether the evaluator is being fooled
Check for solution contamination
NIST’s “Cheating On AI Agent Evaluations,” published November 28, 2025 and updated December 2, 2025, identifies solution contamination as a threat to validity. An agent may have access to the answer, a leaked task artifact or an unintended shortcut. Review prompts, mounted files, retrieved documents, tool responses and any reference data for answer leakage.
Check for grader gaming
Grader gaming occurs when the agent optimizes for the scoring loophole rather than the intended task. Examples include producing a phrase that satisfies a brittle checker while skipping a required state change, exploiting a permissive mock API, or choosing a tool whose output looks successful despite an unauthorized side effect. NIST recommends transcript review and clearer expectations rather than trusting a single automated score.
Close loopholes and rerun
- Write down the intended behavior in plain language and in machine-checkable assertions.
- Inspect a sample of full transcripts, including failures and unusually fast successes.
- Make tool affordances, restrictions and success conditions explicit.
- Patch the task, harness or grader when it permits an unintended shortcut.
- Rerun the held-out evaluation after the change; do not compare patched and unpatched results as if they were the same test.
7. Keep humans in control of consequential actions
Require explicit confirmation immediately before an action that changes external state, contacts another person, commits money or creates a legal or operational obligation. OpenAI’s Computer-Using Agent system description gives submitting an order and sending email as examples for which the system asks for confirmation. Treat confirmation as one control in a larger design, not as proof that the agent is safe: the reviewer still needs a clear preview of the recipient, content, amount and effect.
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A useful confirmation screen shows:
- The exact tool and account that will be used.
- The complete arguments, including recipients, quantities and attachments.
- The expected external effect and whether it can be reversed.
- The evidence and policy checks that led to the proposal.
- A way to cancel without allowing the agent to continue automatically.
For high-impact workflows, separate proposing from executing: let the agent prepare a draft or transaction, then require an authorized person or independent service to approve and submit it.
8. Make the access decision narrow and reviewable
Report what the test actually covered
OpenAI’s 2026 “A shared playbook for trustworthy third party evaluations” recommends reporting the claim, content, system, budget, elicitation method and validity checks. Adapt that structure to an internal decision record:
- Claim: the exact task and permission set approved.
- System: model, prompts, agent scaffold, tools, versions and account roles.
- Tasks: counts and distributions for ordinary, edge, adversarial and failure cases.
- Budget: turn, token, time, retry and rate limits.
- Safeguards: sandbox controls, network policy, confirmation gates and rollback.
- Results: outcome, trajectory, policy and blocking-failure rates, with examples.
- Validity checks: contamination review, grader review, solvability check and held-out status.
- Uncovered risks: environments, tools, languages, attack patterns or side effects not tested.
Use staged access
- Allow observation or read-only access in a disposable environment.
- Allow reversible writes with mandatory previews and rollback.
- Run a limited pilot with rate limits, complete traces and human confirmation.
- Expand permissions only after new tests cover the additional tools and failure modes.
- Re-evaluate after model, prompt, tool, policy, data or infrastructure changes.
The approval should have an owner, an expiry or review date, and a rollback plan. A successful evaluation is evidence for a particular configuration, not a permanent safety certificate.
A worked example: a support-ticket agent
Suppose an agent may read tickets, search a knowledge base and draft replies. Sending a reply and changing a ticket’s status are separate permissions. The test set should include a normal password-reset request, a ticket containing an instruction to ignore policy, a request with two customer identities, a knowledge-base outage and a request to close a ticket without evidence.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11In the sandbox, provide mock tools such as search_ticket, search_knowledge_base, draft_reply and send_reply. The agent passes the read-and-draft stage only if it selects the correct ticket, quotes the relevant evidence, avoids exposing another customer’s data, and produces a draft without calling send_reply. A separate test can present the exact draft to a human for confirmation; the agent must not send it before approval. If the knowledge base times out, the expected behavior is to disclose the limitation and ask for a handoff rather than inventing a policy answer.
This example demonstrates why the permission boundary matters: “handles support tickets” is too broad a claim, while “reads authorized tickets and drafts replies under these conditions” can be tested and reviewed.
Common failure modes and fixes
| Observed problem | Likely cause | Fix |
|---|---|---|
| High final-answer accuracy, unsafe tool calls | Only the final text was graded. | Add trajectory, argument and policy checks; inspect traces. |
| Agent succeeds only with a hidden shortcut | Answer leakage or solution contamination. | Audit mounted data and tool responses; rebuild the held-out set. |
| Scores collapse in deployment | Test data or tools do not resemble real use. | Increase representative variation and test the production-like harness. |
| Repeated duplicate actions after a timeout | No idempotency or state verification. | Expose operation status, use idempotency keys and require confirmation for uncertain writes. |
| Agent follows instructions in an untrusted document | Data and control instructions were not separated. | Label retrieved content as untrusted, test injection cases and enforce tool-side authorization. |
| Automated grader reports passes that look wrong | Grader gaming or a permissive checker. | Review transcripts, add state-based assertions and patch loopholes. |
| Sandbox run reaches an external service | Network or credentials were misconfigured. | Stop the run, rotate credentials, verify isolation before retesting and record the incident. |
Final decision rule
Grant access only when the evidence supports the exact task and permission boundary under consideration. The minimum defensible package is a representative and adversarial test set, a separate held-out evaluation where feasible, a verified containment setup, complete trajectory logs, scoring for outcomes and policy compliance, a review for contamination and grader gaming, and human confirmation for consequential side effects. Anything broader than that is an untested assumption, not a readiness result.
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