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The practical method is a three-layer program: a deterministic offline task suite, adversarial multi-turn testing, and field trials in production-like conditions. Compare agents with the same scenarios and constraints, report distributions and failure examples rather than one aggregate score, and apply stricter gates when an agent can change records, move money, control infrastructure, or handle sensitive information.
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What makes an AI agent different from a model benchmark?
A conventional benchmark usually asks a model a question and grades the answer. An agent has a larger operational boundary: it interprets a goal, plans, chooses tools, gathers information, maintains state, and takes actions that can affect systems or people.
Anthropic defines an agent as “an AI model that directs its own processes and tool use when accomplishing a task—that is, deciding for itself how to achieve what users want, rather than following a fixed script.”
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That definition, published by Anthropic in 2026, explains why a static question-and-answer score is insufficient. The evaluated object includes the model, prompts, policies, tool wrappers, retrieval systems, memory, permissions, human approvals, external services, and the resulting side effects.
For example, an account-support agent can produce a perfectly worded response while still failing if it looked up the wrong customer, exposed another customer’s data, issued an unauthorized refund, or promised an action it never performed. Evaluation must therefore grade the complete task trajectory and its consequences.
Define “success” before running a test
Start with a written contract for every scenario. Do not let the agent or the evaluator define success after seeing the result.
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- Task objective: State what the user is trying to accomplish in observable terms.
- Required side effects: List records that must be created, changed, sent, scheduled, or left untouched.
- User acceptance: Define what a reasonable user would accept as complete, including accuracy, completeness, tone, and timing.
- Partial credit: Separate useful intermediate outcomes from a fully completed task. A correct draft awaiting approval is different from a message actually sent.
- Prohibited outcomes: Identify actions that make the run a failure even if the final answer sounds plausible.
For a calendar agent, “find a meeting time” is not enough. A complete criterion might require checking the correct participants’ calendars, honoring time zones and working hours, proposing options without booking, and obtaining confirmation before creating an event.
Set operational boundaries
Record the tools the agent may call, the data each tool can expose, the maximum transaction value, rate and latency limits, approval checkpoints, and rollback method. These constraints are part of the test, not implementation details to ignore.
Grade the trajectory as well as the endpoint
A run can be unsuccessful even when its final text is correct. Grade whether the agent gathered evidence from an authorized source, used current state, selected the appropriate tool, passed valid arguments, and avoided an unnecessary or dangerous action. Conversely, an agent that encounters a service outage and escalates safely may deserve more credit than one that silently guesses.
The evaluation dimensions that data quality cannot cover
1. Outcome validity
Outcome validity asks whether the agent achieved the intended real-world result. Build task-specific checks for both information and action.
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- Did it answer the user’s actual question rather than a nearby one?
- Did it complete every required side effect exactly once?
- Did it preserve invariants, such as balances, permissions, or record ownership?
- Did it communicate what happened, what did not happen, and what requires approval?
- Would the requesting user accept the result without discovering a hidden correction later?
Use independent verification where possible: compare a changed record with a trusted fixture, validate a transaction identifier, or have a reviewer judge the result against a rubric. Grading only the final prose rewards confident descriptions of actions that never occurred.
NIST’s trustworthy-AI materials identify accuracy, explainability and interpretability, privacy, reliability, robustness, safety, security and resilience, and harmful-bias mitigation as distinct characteristics. Treat them as separate acceptance dimensions instead of assuming that a high answer-accuracy score represents all of them.
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2. Reliability and repeatability
Run the same scenario repeatedly and run controlled variants of it. Record the proportion of successful completions, the spread of outcomes, time to completion, unnecessary loops, escalation frequency, and recovery after tool errors.
Useful perturbations include reordered information, harmless wording changes, realistic typos, different but equivalent dates, and changes in the order in which tools return results. If one run books a meeting and another merely drafts an invitation under the same conditions, the agent is not reliable even if both outputs look reasonable in isolation.
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Report distributions—such as median and high-percentile latency, cost per run, and success across repeated trials—alongside representative failure traces. A single average can conceal a rare but unacceptable failure.
3. Tool-use correctness
Inspect every tool decision, not only the final response. Evaluation should cover:
- Choice: Was the selected tool appropriate for the user’s request and available authority?
- Arguments: Were identifiers, units, filters, dates, and limits valid and complete?
- Sequence: Did the agent fetch required state before writing, and verify the result afterward?
- Permissions: Did it operate only within the user’s and service account’s scope?
- Retries: Did it retry transient failures safely without duplicating an action?
- Effects: Did the external system change exactly as intended?
Include negative tests in which a tool returns malformed data, stale state, a permission error, or a timeout. The expected behavior may be to stop, ask for clarification, retry with a bounded policy, or escalate—not to improvise.
4. Robustness and adversarial behavior
Test conditions that challenge the agent’s assumptions and authority:
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- Prompt injection in a web page, document, email, ticket, or tool response.
- Conflicting instructions from a user, an embedded document, and a higher-priority policy.
- Ambiguous requests with two plausible targets or missing authorization.
- Malformed, contradictory, delayed, or unavailable tool responses.
- Distribution shifts, such as new terminology, unfamiliar record formats, or changed workflow rules.
- Long multi-turn conversations in which an earlier assumption becomes invalid.
NIST’s AI Risk and Infrastructure (ARIA) approach treats model testing, red-teaming, and field testing as complementary and examines technical and contextual robustness beyond performance and accuracy. Use that same progression: controlled failures first, realistic attacks next, and observed behavior with real users and permissions last.
5. Safety, security, and privacy
Construct explicit abuse cases, not just ordinary happy paths. Ask whether the agent can be induced to:
- Perform an action without the required authorization or approval.
- Reveal personal, confidential, or cross-tenant information.
- Exfiltrate secrets through tool parameters, generated files, links, or messages.
- Construct an insecure query, command, or configuration.
- Continue acting when a human should take control.
- Generate harmful content or materially amplify a dangerous request.
Check data minimization, retention, redaction, tenant isolation, and audit permissions. A refusal is not automatically safe: it should avoid leaking the protected information while directing the user to a legitimate route.
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OpenAI’s Operator system card illustrates deployment-informed testing, including third-party red teaming and risk categories such as persuasion, cybersecurity, chemical, biological, radiological and nuclear risks, and model autonomy. Use those categories as prompts for a threat model appropriate to your own tools and domain; do not assume a test of one product proves safety for another.
6. Human and organizational impact
Measure what changes for people who request, review, receive, or are affected by the agent’s work. Relevant measures include:
- Time and cognitive workload saved or added.
- Reviewer intervention rate and the severity of corrections required.
- Error cost, including downstream rework and irreversible harm.
- Accessibility for users with different abilities, languages, devices, or connectivity.
- Fairness of recommendations and error rates across relevant groups.
- User trust calibrated to actual capability rather than persuasive wording.
- Whether the agent changes decisions in ways operators do not notice.
Field testing matters because a technically successful action can still create an unusable process, excessive review burden, or an incentive to bypass controls. NIST ARIA’s field-testing orientation is designed to expose these contextual effects.
7. Auditability and evidence
Preserve enough information to reproduce a decision and investigate a failure. At minimum, capture:
- The user request, conversation state, system and policy instructions, and relevant retrieved content.
- Model name and version, agent configuration, tool schemas, and policy version.
- Every tool call, argument, response, timestamp, retry, approval, and error.
- The evidence the agent retrieved and the source or record from which it came.
- Policy decisions, authorization checks, human interventions, final outputs, and external outcomes.
Protect these logs as sensitive data and define retention and access rules. NIST’s agentic-probes work states the goal clearly: move beyond “the AI said so” to knowing “what the AI found, where it found it, and how the evidence supports the conclusions.”
Build a three-layer test program
No single test environment captures all agent risks. Use the following layers together.
| Layer | How it works | What it reveals | When to use it |
|---|---|---|---|
| Offline deterministic suite | Replay fixed scenarios with representative data, edge cases, expected actions, and controlled tool fixtures. | Regressions, basic task success, schema errors, policy violations, and repeatability. | Every prompt, model, tool, or policy change; suitable for automated release gates. |
| Adversarial red-team and multi-turn tests | Use hostile, ambiguous, conflicting, and failure-inducing inputs across realistic tool-use loops. | Prompt injection, authority confusion, unsafe retries, data leakage, brittle assumptions, and escalation failures. | Before deployment and whenever permissions, tools, data sources, or threat conditions change. |
| Field trials | Run with production-like users, data, permissions, monitoring, and human approval in a controlled rollout. | Contextual harms, workload, accessibility, trust calibration, operational cost, and unforeseen side effects. | Before broad release and continuously after launch. |
NIST ARIA describes model testing, red-teaming, and field testing as complementary levels. An agent should pass the lower-cost, repeatable checks before it is exposed to the more variable and consequential layers.
Write a scenario specification that evaluators can reproduce
For each test case, create a machine-readable or otherwise versioned specification containing these fields:
- Task objective: The user goal and the starting state.
- Allowed tools and permissions: Exact tool names, scopes, data domains, transaction limits, and approval requirements.
- Prohibited actions: Explicitly forbidden reads, writes, disclosures, and external communications.
- Success criteria: Required answer properties, side effects, evidence, and user notification.
- Partial-credit criteria: Safe and useful intermediate outcomes, such as a draft or escalation.
- Adversarial variants: Injection, ambiguity, stale data, malformed responses, and service outages.
- Resource limits: Maximum latency, tool calls, tokens, spend, and retry count.
- Human checkpoints: The exact point at which approval or takeover is required.
- Rollback procedure: How to undo or quarantine each reversible side effect.
- Evidence requirements: Records and citations the agent must retain for review.
Version the specification with the agent configuration. Otherwise, a score change may reflect a changed test rather than a changed system.
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Measure more than a pass rate
Use a scorecard that keeps safety and operational costs visible instead of hiding them inside one weighted number.
| Measure | What to calculate | Diagnostic question |
|---|---|---|
| Task success | Share of runs meeting all mandatory criteria. | Did the intended job finish? |
| Side-effect quality | Correctness, completeness, and uniqueness of external changes. | Did the system do the right thing exactly once? |
| Reliability | Success distribution across repeated and perturbed runs. | Will behavior hold when wording or state changes? |
| Recovery | Safe completion or escalation after injected failures. | What happens when a dependency breaks? |
| Intervention | Human approvals, takeovers, and corrections per run. | Is review effort lower than the work replaced? |
| Latency | Completion time, including tool waits and approval delays. | Is it fast enough for the workflow? |
| Cost | Model, tool, infrastructure, and human-review cost per completed task. | Does the benefit survive at operating volume? |
| Tool and policy compliance | Invalid calls, unauthorized attempts, unsafe sequences, and policy violations. | Did it stay within its authority? |
| Security and privacy | Injection success, leakage, isolation failures, and unsafe output paths. | Can an attacker turn capability into harm? |
| Traceability | Completeness and accuracy of logs, evidence links, and decision records. | Can an investigator reconstruct the run? |
| User impact | Workload, accessibility, fairness, trust calibration, and downstream error cost. | Did the deployment improve the process for affected people? |
Use hard gates for non-negotiable failures—such as unauthorized money movement or cross-tenant disclosure—and use weighted measures only for trade-offs among acceptable runs. There is no single generalizable performance statistic that can represent every agent and risk profile.
Evaluate tool use and side effects in detail
Check state before action
Give the agent fixtures in which a record changes between lookup and update, a duplicate already exists, or a permission has been revoked. The expected behavior is to revalidate, stop, or ask for approval rather than overwrite blindly.
Check idempotency and retry behavior
Simulate a timeout after the external service accepted a request. A safe agent should determine whether the action occurred before retrying. Count duplicate messages, duplicate tickets, repeated charges, or repeated infrastructure changes as distinct failures.
Check least privilege
Expose only the minimum fields and operations needed for the task. Then test attempts to reach an out-of-scope record or invoke an administrative operation. The agent should receive a clear denial and avoid searching for a workaround.
Check evidence-to-action alignment
Require the agent to identify which retrieved facts justify a decision. An evaluator should be able to tell whether the action relied on current, authorized evidence rather than an unsupported assumption.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Design adversarial tests that resemble real attacks
Red teams should vary the attacker’s position and the location of the malicious instruction. Place an injection in a document the agent is asked to summarize, in a customer email, in a web page returned by a browsing tool, and in a tool error message. Test whether the agent treats untrusted content as data rather than as a higher-priority command.
Use multi-turn attacks: establish a harmless context, obtain a small concession, then request an out-of-scope action. Add social pressure, urgency, conflicting identities, and plausible business reasons. Record not just whether the final attack succeeded, but whether sensitive information was revealed during intermediate steps.
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Use behavioral auditing as a probe, not a safety certificate
Anthropic describes Petri, dated October 6, 2025, as an open-source auditing tool in which an auditor agent makes a plan and interacts with a target model in a tool-use loop. It has been used to investigate behaviors including situational awareness, whistleblowing, self-preservation, and reward hacking.
Such audits can expose behaviors ordinary task suites miss, but a concerning trace is evidence of a possible behavior under particular conditions—not proof that the model is always unsafe. Vary prompts, tools, evaluators, model versions, and environments; reproduce important traces manually; and connect each finding to a concrete mitigation or deployment restriction.
Compare two agents with risk-adjusted gates
Run both agents against identical scenario specifications, fixtures, permissions, tool versions, and review rules. Compare the full operating profile rather than whichever agent writes the more fluent answer.
| Comparison axis | Questions to ask | Why it matters |
|---|---|---|
| Completed-task quality | Which agent produces the correct answer and side effects? | Fluency is not completion. |
| Repeatability | How often does behavior change across equivalent runs? | Unpredictability raises operational risk. |
| Tool selection and policy compliance | Does it choose authorized tools and valid arguments? | Incorrect actions can be worse than incorrect text. |
| Recovery and escalation | Does it stop safely, retry correctly, or involve a human? | Failures are inevitable; unsafe recovery is optional. |
| Security and privacy | How does it handle injection, secrets, and tenant boundaries? | Capability increases the blast radius of mistakes. |
| Evidence and logging | Can an operator reconstruct and verify every important decision? | Untraceable success cannot be governed. |
| Latency, cost, and limits | What are the distributions at expected volume? | A lab winner may be operationally unaffordable. |
| Human impact | Does it reduce workload without increasing hidden corrections or inequity? | Deployment changes a social process, not just a model score. |
| Monitoring and rollback | Can you detect drift, disable a tool, and revert a version quickly? | Control after launch is part of quality. |
Match the bar to reversibility and harm
A low-risk drafting agent may tolerate occasional retries if every output is reviewed before use. An agent that changes records, sends money, controls infrastructure, or handles sensitive data needs narrower permissions, explicit approvals, stronger adversarial coverage, reversible operations, and field testing before broad access.
Do not choose an agent by adding all metrics into an opaque score. First eliminate candidates that fail mandatory safety, privacy, authorization, or auditability gates. Then weigh acceptable differences according to the harm, reversibility, and cost of the particular workflow.
Worked example: evaluating a support-ticket agent
Suppose an agent must classify an incoming ticket, retrieve the customer’s plan, draft a response, and apply a tag. The evaluation should separate drafting quality from authorized system changes.
- Define the starting state: Provide a ticket, a customer identifier, plan records, and a known queue configuration. Include cases with duplicate names and missing identifiers.
- Define authority: Allow read access to the matched customer and write access only to the ticket’s classification and tag. Prohibit refunds, plan changes, and access to other customers.
- Define success: Require the correct customer match, an accurate classification, an appropriate draft, exactly one permitted tag update, and a log showing the evidence used.
- Add failure variants: Return a timeout after a successful tag update, inject instructions into the ticket body, remove the user’s permission, and provide conflicting plan data.
- Set checkpoints: Require human approval before any outbound message and mandate escalation when identity cannot be established.
- Verify outcomes: Compare the ticket record with the expected fixture, search for unauthorized reads or writes, inspect the complete trace, and ask reviewers to rate whether the draft is safe to send.
- Report results: Publish success and failure distributions, duplicate-update count, escalation rate, injection resistance, review time, latency, cost, and trace completeness.
This design can reveal a critical difference between two agents: one may write better prose while the other identifies the correct customer, refuses an injected refund request, and recovers from a timeout without duplicating the tag.
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Use regression tests for every material failure
When an agent fails, preserve the scenario and add it to the offline suite. Include the original prompt, relevant state, tool responses, expected safe behavior, and the reason the failure matters. Retest after model, prompt, tool, policy, retrieval, or permission changes.
Monitor the live system for drift
Track completion, escalation, tool errors, policy denials, latency, cost, sensitive-data events, and user corrections over time. Sample traces for human review, with greater sampling for high-impact workflows and newly changed components.
Prepare rollback before launch
Keep a tested way to disable individual tools, require approval for a broader class of actions, revert the agent configuration, and route work to a human process. A rollback that exists only in documentation is not an operational control.
Quick Recap
Common evaluation mistakes
- Using answer accuracy as the only metric: This misses side effects, authority, recovery, and human impact.
- Testing only happy paths: Real systems encounter ambiguity, stale state, outages, and adversarial content.
- Scoring only the final message: A polished explanation can conceal an unauthorized or nonexistent action.
- Ignoring repeated runs: One successful demonstration does not establish reliability.
- Letting the agent grade itself: Use independent checks and human review for consequential outcomes.
- Hiding failures in an average: Report distributions and examples, and keep hard safety failures visible.
- Granting production permissions during early tests: Use fixtures, sandboxes, narrow scopes, and reversible operations.
- Treating a behavioral probe as proof: Reproduce findings across conditions and convert them into controls.
- Changing the test while changing the agent: Version scenarios and configurations so comparisons remain valid.
- Stopping at launch: Field behavior, users, data, and threats change; evaluation must continue.
An operational checklist
- Write task-specific success, partial-credit, prohibited-action, and evidence criteria.
- Map every tool, permission, data source, approval point, limit, and rollback path.
- Build representative offline cases, edge cases, and known expected actions.
- Repeat cases with controlled perturbations and record variance.
- Inject prompt attacks, conflicting instructions, malformed responses, outages, and ambiguity.
- Verify side effects independently and test idempotent recovery after timeouts.
- Measure security, privacy, accessibility, fairness, workload, cost, latency, and traceability.
- Run a controlled field trial with production-like users, data, permissions, and monitoring.
- Use hard release gates for unauthorized actions, data leakage, unsafe autonomy, and missing audit evidence.
- Version the tests and add every important failure to regression coverage.
- Compare agents on the same scenarios and choose according to the use case’s harm and reversibility.
- Maintain monitoring, human escalation, tool disablement, and rollback after deployment.
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