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Evaluate the agent you will actually deploy—not a model in isolation. A production agent is a workflow that combines a model, prompts, tools, data, permissions, runtime environment, safeguards, retries and operating procedures. Define the work and unacceptable failures first, test that complete configuration on representative and adversarial cases, inspect its evidence and actions, then release it only with enforceable governance, monitoring and a named owner.
What you are evaluating: a complete workflow, not a model score
An AI agent can interpret a request, retrieve information, call software tools, change records and ask a person to intervene. Its reliability and risk therefore depend on the entire setup. Changing the model, tool permissions, context window, retry policy, prompt, safety filter or execution harness can change both results and cost.
A benchmark score is useful only when its tasks resemble your work and the tested system matches the configuration you intend to operate. Record the model and settings, tools, data sources, permissions, harness, safeguards, retry behavior, number of attempts, time and token budget, and cost for every evaluation run.
“To build confidence that these workflows have executed correctly, users need increased visibility into the chain of reasoning, tool usage, and gathered evidence that led to each agentic decision.”
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1. Define the decision and risk tier before choosing tests
Start with a one-page decision record. It should state what the agent does, who can use it, who can be affected, what information it can access, which actions it can take and what an error would cost. Do not select a benchmark until these boundaries are explicit.
Describe the intended job
- Task: Write the business outcome in observable terms, such as “draft a support response from approved policy documents,” not “use AI to help support.”
- Users and affected parties: Identify employees, customers, applicants, patients, suppliers or other people who may rely on an output or experience an action.
- Inputs and data: List data classes, retention requirements, geographic restrictions and the systems from which information may be retrieved.
- Actions: Separate read, write, delete, purchase, message, approval and configuration privileges. State which actions require a person to confirm.
- Failure boundaries: Name outcomes that are unacceptable even if they are rare, such as disclosing another customer’s record, approving an unsafe transaction or inventing a legal requirement.
Assign a risk tier
Classify autonomy, data sensitivity and business impact together. A low-risk productivity helper can usually have narrower tests than a customer-facing, decision-making or mission-critical agent. The tier should determine test depth, approval authority, human oversight, logging detail and operational support. Microsoft’s maturity guidance describes differentiated expectations for those categories; treat that as vendor guidance rather than a universal law.
| Risk characteristic | Questions to answer | What a higher rating should change |
|---|---|---|
| Autonomy | Does the agent only suggest text, or can it execute multi-step actions without confirmation? | More approval checkpoints, narrower permissions, simulation and rollback testing. |
| Data sensitivity | Can it see public material, internal information, personal data, financial records or secrets? | Stronger access controls, data-loss tests, retention rules and security review. |
| Business impact | Could an error inconvenience one worker, affect a customer, alter a regulated decision or interrupt a critical service? | More representative cases, independent review, escalation coverage and tighter release gates. |
Write the tier and its required controls into the approval record. Do not let a team quietly lower the tier because an agent is labelled a “copilot”; assess what it can actually do.
2. Build task-specific tests with explicit pass and fail conditions
Use representative work, not convenient examples
Create a test set from real or carefully anonymized requests across the intended task distribution. Include common cases, rare but important cases, ambiguous requests, incomplete information, conflicting records, long context, unusual formats and requests that should be refused. Keep a separate holdout set that the development team does not tune against.
For each case, define the expected result or a scoring rubric before running the agent. A rubric can award separate points for factual accuracy, correct tool choice, completeness, policy compliance, tone, latency and whether escalation was appropriate. Mark unacceptable outcomes explicitly; a polished answer that exposes restricted data is a failure, not a partial success.
Use benchmarks only when they fit
Do not transfer a public benchmark score to your deployment unless its tasks, data assumptions, tools and success definition match your workflow. NIST’s CAISSI page lists “Practices for Automated Benchmark Evaluations of Language Models” as an initial public draft, updated September 30, 2026. It is voluntary draft guidance, not a final standard or a certification threshold.
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| Test group | Examples | Evidence to retain |
|---|---|---|
| Normal workflow | Frequent requests using clean, complete inputs | Expected output, actual output, tool calls and score by criterion. |
| Edge cases | Ambiguous intent, missing fields, conflicting documents, unusual language or large context | Whether the agent asked a useful clarification, abstained or chose a safe fallback. |
| Negative cases | Requests outside the role, restricted records and actions without authorization | Refusal or escalation reason and proof that no unauthorized action occurred. |
| Operational cases | Tool timeout, rate limit, stale data, partial failure, duplicate request and retry | Final state, retry count, idempotency behavior, user notice and recovery path. |
Set release criteria without inventing a universal number
There is no evidence-based industry-wide pass percentage in the reviewed guidance. Set criteria per risk tier instead: which failures block launch, which require remediation, what sample size is sufficient for your decision, and who may approve an exception. A single average score must never hide a catastrophic failure category.
3. Test the complete deployed configuration
Run evaluations through the same interface, orchestration code and permissions that production will use. If production has retrieval, function calls, a safety layer, retries or a human handoff, include each one in the test harness. OpenAI’s evaluation playbook emphasizes that results depend on the environment and evaluation budget as well as the model.
Record the configuration for reproducibility
| Component | Record | Why it matters |
|---|---|---|
| Model and inference | Model identifier, version or snapshot, system instructions, temperature or equivalent settings and context limits | Model updates and settings can change behavior without a code change. |
| Tools and permissions | Available functions, API versions, scopes, service accounts, write access and approval requirements | An otherwise correct plan can become harmful with excessive authority. |
| Data and retrieval | Indexes, filters, document versions, ranking settings, source labels and freshness rules | Grounding quality depends on what the agent can retrieve and how it selects evidence. |
| Harness and orchestration | Planner, tool loop, context handling, memory, state management, timeout and retry rules | Different harnesses can produce different success rates, latency and cost. |
| Safeguards | Input and output filters, policy checks, confirmation steps, rate limits and sandboxing | Controls must be tested as part of the behavior, not assumed from documentation. |
| Budget | Maximum attempts, time, tokens and per-task cost | An agent that succeeds only with an unlimited budget may not be deployable. |
Make runs comparable
Fix the test-set version, randomization policy, budget and external-data snapshot where possible. Repeat stochastic tasks enough to expose unstable behavior, and report the distribution rather than only the best run. When comparing two agents, hold the task set, tools, permissions, time limit and cost budget constant.
4. Inspect grounding, tool use and the evidence trail
For factual answers and business actions, review how the agent reached its result. Preserve a trace linking each material claim and action to retrieved evidence, tool inputs and tool outputs. You do not need to expose private internal reasoning; you do need an auditable record of what information was used and what operations occurred.
Apply three grounding questions
- Faithfulness: Does the cited evidence actually support the claim?
- Completeness: Does the response represent the important message of the source, including qualifications and exceptions?
- Sufficiency: Is the evidence strong and complete enough to justify the claim or action?
NIST’s evaluation-probe project, updated May 5, 2026, describes automated probes that compare output claims with human-curated reference documents and produce machine-readable audit trails. The project remains ongoing experimental research, not a universal pass/fail certification program.
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Review execution, not just wording
- Check that the agent selected an authorized and appropriate tool rather than fabricating a result.
- Verify arguments, filters, units, record identifiers and transaction targets before execution.
- Confirm that a failed or timed-out call did not create a duplicate action on retry.
- Check that stale, conflicting or missing evidence triggers clarification, abstention or escalation.
- Ensure the trace records actor identity, time, request, policy decision, tool call, result, approval and final state.
5. Red-team misuse and high-impact failure modes
Ordinary task evaluations measure intended behavior. Red teaming probes what happens when a user, document or tool behaves adversarially. OpenAI describes this as complementary work intended to probe misuse and security risks before deployment.
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Test the attack surface relevant to the role
- Prompt injection: Place instructions in retrieved documents, web pages, emails or tool output that attempt to override the agent’s role or exfiltrate data.
- Abusive and unexpected requests: Try requests that conflict with policy, use coded language or combine benign steps into a harmful objective.
- Unauthorized access: Ask for another user’s records, hidden system instructions, secrets or data outside the caller’s scope.
- Unsafe actions: Attempt purchases, deletions, messages, permission changes or code execution without required confirmation.
- Boundary confusion: Switch languages, identities, channels or tenants to test whether controls follow the user and data rather than the interface.
- Resilience: Induce tool errors, malformed responses, rate limits and repeated retries to look for unsafe fallback behavior.
Run authorized tests only against systems you own or have permission to assess. Record the attack, the path it took, the control that should have stopped it, the actual impact and the fix. Re-run the scenario after remediation and after material configuration changes.
6. Put enforceable governance in place before broad release
A written policy is not a control unless the system can enforce it. Microsoft’s governance guidance describes a baseline that every agent must meet before operation; use it as implementation guidance and adapt it to your obligations.
“A governance and security baseline sets the minimum requirements that every agent must meet before it is allowed to operate.”
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Pre-release gate checklist
- Owner and inventory: Name a business owner, technical owner and security contact. Record purpose, tier, version, dependencies, users, data and approved environments in an inventory.
- Identity and access: Use unique service identities, least-privilege scopes, separated development and production credentials, secret rotation and a documented approval path.
- Data boundaries: Enforce tenant and role filters, retention and deletion rules, sensitive-data handling, regional requirements and restrictions on training or secondary use.
- Security review: Review prompt injection, supply-chain dependencies, tool endpoints, logging contents, isolation, abuse limits and recovery from compromise.
- Approved development pattern: Require code review, dependency management, test evidence, change records and a reproducible deployment artifact.
- Auditability: Retain sufficient traces for investigation, including policy decisions, approvals, tool calls, source references, errors and final state.
- Human escalation: Define when the agent must stop, what context a reviewer receives, the response-time target and who can override or reverse an action.
- Release authority: Specify the approver for the risk tier, the evidence required and the person who can suspend access.
Make gates machine-checkable where possible: block deployment when required ownership, permissions, tests, logging or incident contacts are missing. Do not treat a vendor’s default setting as proof that your organization’s control objective is met.
7. Operate, monitor and re-evaluate after launch
Readiness is conditional, not permanent. Models, prompts, retrieval indexes, tools, permissions, policies and user behavior all change. Define the operating plan before enabling broad access.
Monitor the signals that matter
- Task success by workflow and risk tier, including abstentions and escalations.
- Grounding defects, policy violations, unauthorized-access attempts and harmful actions.
- Tool errors, retries, latency, token use, cost and rate-limit events.
- User corrections, complaints, feedback and cases sent to human reviewers.
- Changes in input distribution, retrieval coverage, data freshness and refusal patterns.
- Security alerts, incidents, near misses and unresolved remediation items.
Define change triggers
Require a new evaluation when the model or model version, system prompt, tool, permission, retrieval source, policy, orchestration logic or budget changes materially. Also re-test after an incident, a new user population, a new geography or a meaningful shift in task distribution. Keep a rollback or disable path and practice it.
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Microsoft’s maturity guidance connects telemetry and lifecycle ownership with decisions to evaluate, optimize or retire agents. Retirement is a normal control outcome when the business need, data source, risk or operating cost changes.
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How to compare two agents or vendors fairly
Use one decision matrix and report the harness and configuration beside every result. A comparison that lists only model names or benchmark scores is not reproducible.
| Comparison axis | What to measure | Decision question |
|---|---|---|
| Representative-task success | Scores by task type, edge case and unacceptable-failure category | Which option performs the work we actually need? |
| Harm and policy failures | Severity and rate of unsafe, unauthorized or noncompliant behavior | What is the worst credible outcome and how often does it appear? |
| Grounding and traceability | Faithfulness, completeness, sufficiency, source references and action traces | Can a reviewer verify why an answer or action occurred? |
| Permissions and data controls | Granularity of scopes, tenant isolation, approval steps and retention options | Can authority be limited to the minimum required? |
| Adversarial robustness | Results from prompt-injection, abuse, exfiltration and tool-failure scenarios | Does the agent fail safely under attack? |
| Operations | Monitoring, incident response, escalation, rollback and lifecycle support | Who operates it when behavior changes or something goes wrong? |
| Reproducibility | Version pinning, configuration export, logs and repeatable test runs | Can another team reproduce the reported result? |
| Latency and cost | Distribution under the same time, token, attempt and concurrency budget | Does performance remain acceptable at the intended scale? |
“Not stated” is a valid result when an option does not document a capability. Do not fill gaps with assumptions or treat marketing claims as test evidence.
A practical evaluation record
Keep one signed record for each release candidate. Include:
- Purpose, users, affected parties, risk tier and prohibited uses.
- Model, prompt, tools, data sources, permissions, harness, safeguards and budget.
- Test-set origin, version, task distribution, rubric and unacceptable outcomes.
- Results by task and failure category, including variance across repeated runs.
- Grounding and execution traces for sampled claims and actions.
- Red-team scenarios, authorization, findings, fixes and retest results.
- Owner, security reviewer, business approver, exception decisions and expiry date.
- Monitoring dashboard, incident contacts, escalation rules, rollback method and next review trigger.
This record lets a later reviewer distinguish a model change from a permission change, reproduce a result and determine whether the original approval still applies.
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Testing the model instead of the agent
Mistake: Selecting the highest score on a general benchmark. Correction: Run your representative tasks through the complete production harness with the same tools, permissions and budget.
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Optimizing an average that hides dangerous failures
Mistake: Reporting one success percentage. Correction: Separate ordinary quality, grounding, security and policy-violation results, and make catastrophic categories blocking.
Checking final text but not actions
Mistake: Approving a response because it sounds correct. Correction: Inspect source evidence, tool arguments, approvals, retries and final system state.
Treating red teaming as a one-time exercise
Mistake: Running an attack workshop before launch and never repeating it. Correction: Maintain a scenario catalogue and rerun relevant attacks after changes, incidents and new integrations.
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Mistake: Assuming the project team will handle incidents indefinitely. Correction: Name an owner, escalation path, monitoring duties, response targets, suspension authority and retirement criteria before release.
The release decision
Approve an agent only when its intended work, risk tier, complete configuration, representative results, adversarial findings, evidence trail, governance controls and operating plan are documented. If a critical failure remains unexplained, a permission cannot be constrained, or no person owns the response, delay deployment or reduce the agent’s autonomy and data access. A controlled pilot with explicit limits is safer than broad access based on an impressive but incomplete score.
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