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Do not grant an AI agent production credentials because it completed a few convincing demos. Test the exact workflow, tools, data boundaries and permissions you intend to allow in a contained environment; inspect the complete action trace; challenge it with ambiguous, adversarial and failing-tool tasks; and keep a person confirming consequential external actions. A passing result is evidence for one defined permission set, not a certificate of general safety.
What “ready” means for an AI agent
Agent readiness is a bounded decision. An agent that is reliable at reading documents may be unsafe when it can send messages, alter records or spend money. The relevant question is not “Is this model safe?” but “Did this configured system perform these tasks, with these tools and safeguards, within the limits we tested?”
Write the decision in one sentence before building tests. For example: “Can the support-triage agent read tickets, search the knowledge base and draft replies, but never send a message or change a customer record without confirmation?” That statement fixes the scope of the evaluation and prevents a strong result on a low-risk task from being stretched into approval for a higher-risk capability.
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Separate observation from side effects
Classify every proposed tool by what it can do:
- Read: retrieve data, inspect a file or query a service without changing it.
- Prepare: create a draft, proposed change or transaction that another person must approve.
- Change: write data, modify configuration, delete content or otherwise alter an external system.
- Commit: send, publish, purchase, deploy or grant access. These actions create consequences outside the test harness.
Evaluate each class separately. Read-only success does not establish that the agent will handle a write operation correctly, and a draft that looks correct does not prove that the final submission step is safe.
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Define the permission boundary and failure policy
List the intended tasks, allowed tools, prohibited actions, data the agent may access, and the response required when a request is ambiguous, unsafe or outside scope. Include the expected behavior when a tool returns an error, times out or produces contradictory information.
Specify what the agent must do
- Name the tools it may call and the operations each tool exposes.
- State which records, accounts, files and network destinations are in scope.
- Require confirmation before any action with an external side effect.
- Define when it must ask a clarifying question rather than infer a missing fact.
- Define when it must stop and hand off to a person.
- State data-handling rules, such as never copying secrets into a ticket or transmitting personal data to an unapproved destination.
Specify what counts as a failure
A wrong final answer is only one failure mode. Treat an unauthorized tool call, an unsafe argument, a skipped confirmation, an invented result, an attempt to bypass a restriction, or continued execution after a critical error as a failure even if the final text appears plausible. Conversely, a refusal or handoff can be the correct result when the task is unsafe or underspecified.
Build representative and adversarial task sets
Your test set should resemble the work the agent will actually perform, not a collection of polished demonstrations. Include ordinary requests, edge cases and situations in which the right action is to ask, stop or refuse.
Cover the normal workflow
- Typical requests with complete, well-formed inputs.
- Multi-step tasks that require looking up information, selecting a tool and then producing an output.
- Tasks that require a handoff after the agent reaches the boundary of its authority.
- Cases with several valid choices, so the evaluator can check whether the agent explains its selection.
Add pressure and ambiguity
- Conflicting instructions in a user request and a higher-priority policy.
- Untrusted text that attempts to redirect the agent, such as a document containing “ignore previous instructions.”
- Missing fields, inconsistent records and requests that could mean more than one thing.
- Urgent wording that encourages the agent to skip a required review.
- Requests for information or actions outside the stated data and permission boundary.
Simulate broken tools
Return timeouts, permission errors, malformed responses, stale data and partial success. Verify that the agent notices the failure, avoids claiming that an action succeeded, does not retry destructively, and either recovers safely or hands off. A test that includes only healthy tools cannot reveal these behaviors.
Keep development and test evidence separate
Use one set of similar tasks while tuning prompts, scaffolding and agent architecture. Hold out a separate evaluation set for the final decision. The second public draft of NIST AI 800-1 describes this development-versus-test separation. If the same examples are repeatedly used to tune the system and prove it works, the result can measure memorization or optimization to the test rather than dependable behavior.
Contain the environment before the first run
Run evaluations against a sandbox or mock service that cannot change production data. Give the agent synthetic accounts, fabricated records and reversible state. Keep production credentials, signing keys and personal data outside the evaluation environment.
Use cyber-evaluation isolation guidance where relevant
For cyber evaluations, Anthropic’s published guidance recommends a hardened sandbox with no internet access by default. It also calls for configuration verification before each evaluation, supervised testing for escape, and a check that the challenge is actually solvable. Those controls are specific to cyber testing; for other tool settings, adapt the same principles rather than implying that every evaluation needs identical infrastructure.
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- Verify network routes, DNS, mounted filesystems, identity scopes and tool endpoints before each run.
- Ensure that a test failure cannot reach a production API through a hidden route or shared credential.
- Test the sandbox itself under supervision, including attempts to access disallowed files or services.
- Confirm that the task has enough information and functioning mock tools to be solved; an impossible challenge is not evidence of unsafe reasoning.
- Record the configuration so another evaluator can reproduce the run.
Isolation reduces blast radius; it does not prove that the agent is safe. A permissive network rule, an accidentally mounted secret or a mock that behaves unlike the real service can invalidate the conclusion.
Capture the complete decision trace
Store the user request, system and policy instructions, tool definitions, tool calls and arguments, tool responses, model messages, handoffs, retries, confirmations and final result. Redact secrets before people review the records, but retain enough structure to reconstruct what happened.
NIST’s “Building Evaluation Probes into Agentic AI” project describes the need for visibility into “the chain of reasoning, tool usage, and gathered evidence that led to each agentic decision.” In practice, you do not need unrestricted exposure of private internal reasoning to audit a workflow. You do need the observable sequence of decisions, supplied inputs, retrieved evidence and policy checks.
Grade the trajectory, not only the answer
OpenAI’s agent-evaluation documentation gives two useful trace-grading questions: “Did the agent pick the right tool?” and “Did the workflow violate an instruction or safety policy?” Extend those questions across the whole run:
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| Evaluation dimension | What to inspect | Evidence of a pass |
|---|---|---|
| Tool selection | Whether the chosen tool was appropriate for the task and permission boundary | The agent uses the least-privileged suitable tool and does not call unrelated services |
| Arguments and inputs | Identifiers, filters, destinations, quantities and data supplied to the tool | Arguments are accurate, authorized and consistent with the user request and policy |
| Instruction and policy compliance | Priority handling, data restrictions and prohibited actions | No instruction or safety-policy violation occurs, including during recovery |
| Evidence use | What records or tool results support the decision | The agent distinguishes retrieved facts from assumptions and does not invent a result |
| Handoff and confirmation | Whether the agent pauses at the defined boundary | It asks for clarification or confirmation when required and supplies enough context for the reviewer |
| Failure recovery | Behavior after timeout, denial, contradictory data or partial completion | It detects the failure, avoids unsafe retries and reports the actual state |
| Stopping behavior | Whether it ends when the task is complete or out of scope | No unnecessary calls, escalation of privileges or persistence after a stop condition |
Use a written rubric for each task. A reviewer should be able to mark a specific trace event as the reason for a pass, failure or “needs review,” rather than relying on whether the final prose sounds confident.
Check that the evaluation itself is valid
An agent can appear successful without doing the intended work. NIST identifies solution contamination and grader gaming as threats to evaluation validity.
Look for solution contamination
Answer leakage occurs when the task, prompt, fixture or tool response reveals the expected solution. Review the task package and transcripts for hints that would not exist in real use. Remove accidental answer text, regenerate compromised fixtures and rerun the held-out set.
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Look for grader gaming
Grader gaming occurs when the agent exploits a scoring loophole rather than fulfilling the task. Examples include producing a keyword that satisfies a brittle checker, writing a draft while silently skipping a required verification, or selecting a tool output that makes the score look good without changing the intended record. Use transcript review and, where possible, independent checks of the resulting state.
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State the available tools, restrictions and success conditions clearly. Do not penalize an agent for failing to use a capability it was never given, and do not allow an unstated shortcut that a real deployment would not provide. Check that the task is solvable with the supplied information and that the grader measures the intended outcome and policy behavior.
Run a repeatable evaluation procedure
- Freeze the configuration. Record the model version, system and policy prompts, tool schemas, harness, sandbox configuration, data fixtures, budget, retry limit and elicitation method.
- Verify containment. Check identities, network access, mounted resources, logging and reset procedures. Confirm that no production credential or personal record is reachable.
- Load the held-out tasks. Mix ordinary, adversarial, ambiguous and tool-failure cases without revealing which category each task belongs to.
- Run under the same controls intended for deployment. If production will require confirmation, include confirmation in the test. If a reviewer will be available, define when that reviewer may intervene and record the intervention.
- Capture every event. Preserve requests, responses, calls, arguments, state changes, handoffs and timing in an auditable trace.
- Score independently. Have a reviewer assess the final result and the trajectory against the rubric. For high-impact workflows, use a second reviewer or an independent state check.
- Investigate failures. Classify each failure as task error, tool-selection error, argument error, policy violation, unsafe recovery, evaluator flaw or infrastructure problem.
- Patch only the development setup. Change prompts, tools or scaffolding using the development set, then rerun the untouched evaluation set.
- Make a narrow access decision. Approve only the tested tasks, tools and safeguards. Record exclusions and unresolved risks.
Compare testing approaches by the evidence they produce
A quick demo, a benchmark and a controlled workflow evaluation answer different questions. Use these axes when deciding whether a test is strong enough for a proposed permission set.
| Approach | Task resemblance | Side-effect containment | What is scored | Development/test separation | Reporting quality |
|---|---|---|---|---|---|
| Product demo | Usually a small number of curated examples | May be unclear | Mostly final output | Usually not established | Often omits harness, budget and validity checks |
| Held-out task evaluation | Representative and adversarial tasks matched to intended use | Sandbox or mock tools with verified restrictions | Final result plus tool trajectory and policy compliance | Development set is kept separate from the test set | Reports system, harness, budget, elicitation method and limitations |
| Live production trial | High resemblance | Real consequences unless separately controlled | May include real outcomes, but failures can be costly | Often difficult to preserve | Must document interventions, incidents and scope changes |
For a first access decision, the middle approach provides the most useful evidence with the least uncontrolled exposure. A live trial is a separate risk decision, not a substitute for a contained evaluation.
Keep people in the loop for consequential actions
Require human confirmation immediately before an action that sends, publishes, purchases, deletes, deploys or changes an external record. OpenAI’s computer-using-agent description uses submitting an order and sending email as examples of actions for which the system asks for confirmation. Treat confirmation as a control that limits the final side effect, not as proof that the preceding reasoning was correct.
The confirmation screen should identify the action, destination, material parameters, evidence used and any uncertainty. A reviewer who sees only “Approve?” cannot meaningfully check a transaction. The agent should not be able to reinterpret a confirmation—for example, by changing the recipient or amount after approval without asking again.
For lower-risk drafts, a reviewer may approve batches if the policy permits it. For irreversible or high-impact changes, require one confirmation per action and preserve the approval event in the trace.
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Report results without overstating them
A defensible report lets another team understand exactly what passed and what the result does not establish. Include:
- The claim being tested and the precise permission boundary.
- Task distribution, including ordinary, adversarial, ambiguous and tool-failure cases.
- Model, prompts, tool schemas, harness, sandbox and reset process.
- Data fixtures, identity scopes, network rules and safeguards.
- Budget, retry limits, time limits and elicitation method.
- Scoring rubric, reviewer process and independent state checks.
- Failures, near misses, interventions and unresolved validity concerns.
- What the test did not cover, such as a different data domain, model version or production integration.
- The approved access level, required confirmations and a date or trigger for re-evaluation.
OpenAI’s 2026 “A shared playbook for trustworthy third party evaluations” emphasizes reporting the claim, content, system, budget, elicitation method and validity checks. Use that level of specificity when publishing an internal decision or sharing results with a customer.
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“It got the right answer, so the test passed.”
Inspect the path. The agent may have used an unauthorized tool, guessed a value, ignored a policy or reached the answer through a shortcut unavailable in deployment. Grade the trace and the resulting state.
“The sandbox means we can stop checking.”
Verify the sandbox before every run. Check routes, credentials, mounts and tool endpoints, and supervise escape testing. Treat isolation as damage containment, not a safety guarantee.
“We tuned the prompt until it passed our benchmark.”
Keep a held-out test set. If tuning and evaluation use the same tasks, contamination can hide regressions and reward benchmark-specific behavior.
“The grader says success, so the workflow is correct.”
Review transcripts and external state for leakage and gaming. Strengthen the grader, close loopholes and rerun the held-out evaluation after changes.
“A confirmation button solves the risk.”
Confirmation protects the final side effect only when the reviewer can see what will happen and the agent cannot alter the approved parameters afterward. Continue testing tool choice, arguments, evidence and policy compliance.
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“One model result applies to every deployment.”
Re-evaluate when the model, prompt, tool schema, data source, permission scope, network path or human-review process changes. Read-only and write access are different claims, even when the underlying model is unchanged.
When to grant access—and when to withhold it
Grant the smallest permission set for which representative and adversarial traces show reliable tool selection, authorized arguments, policy compliance, correct stopping and safe recovery. Keep the agent in a sandbox or draft-only mode when failures remain unexplained, the evaluator has validity problems, or the tested tasks do not match the intended work.
Withhold access if the agent hides failures, continues after a stop condition, bypasses confirmation, reaches outside its data boundary, or succeeds only because the grader leaks the answer. A clean final response cannot offset an unsafe trajectory. Expand permissions gradually, rerunning the evaluation whenever the workflow crosses from reading to changing or committing external state.
Sources and scope of the guidance
The practices above draw on NIST’s “Cheating On AI Agent Evaluations” (published November 28, 2025; updated December 2, 2025), NIST’s “Building Evaluation Probes into Agentic AI” project description, OpenAI’s current “Evaluate agent workflows” and “Evaluation best practices” documentation, Anthropic’s published cyber-evaluation containment guidance, OpenAI’s 2026 third-party evaluation playbook, OpenAI’s computer-using-agent system description, and the second public draft of NIST AI 800-1. The cited NIST document is a draft, so treat its development-set recommendation as draft guidance rather than a finalized standard.
Frequently Asked Questions
How many test cases are enough before an agent gets tool access?
There is no universal count established by the cited guidance. Use enough representative and adversarial cases to cover every intended task, permission boundary, failure mode and handoff, then hold out separate tasks for the final evaluation.
Should an evaluation have internet access?
For cyber evaluations, Anthropic recommends no internet access by default in a hardened sandbox. For other workflows, choose connectivity based on the real task, verify the configuration before each run and keep production systems unreachable.
Can a successful benchmark prove an agent is safe in production?
No. A result supports only the tested model, harness, tools, safeguards, tasks and permissions. Production access requires a separate, narrowly stated decision and re-testing after material changes.
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Show the exact action, destination, parameters, evidence used and uncertainty. Preserve the approval event, and require a new confirmation if any material parameter changes.
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