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Evaluate the complete agent, not just its language model. Before company-wide deployment, test the model together with its tools, data sources, permissions, prompts, orchestration, safeguards, retry behavior, resource limits and operating procedures. Start by defining acceptable and unacceptable outcomes, classify the agent’s risk, run representative and adversarial tests, inspect evidence trails, and require enforceable governance and monitoring before release.
The result should be a documented release decision for a specific configuration. A benchmark score from a different harness or model-only test cannot establish that your deployed workflow is safe or effective.
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What you are actually evaluating
An AI agent is a workflow rather than a model in isolation. Its behavior emerges from the model, system and developer instructions, connected tools, retrieved data, identity and permissions, context management, orchestration code, safety filters, retries, time and token budgets, and the environment in which it runs. Changing any of those elements can change quality, cost and risk.
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For example, an agent that drafts an internal summary has a different risk profile from one that sends customer messages, changes records or makes recommendations used in regulated decisions. The same underlying model can be acceptable in one workflow and unsuitable in another. Your evaluation therefore needs a named task, a defined population of users and affected people, a fixed configuration, evidence of how the agent reached each result, and an operating plan for failures.
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OpenAI’s evaluation guidance recommends reporting enough detail for another team to understand the claim being made, the task distribution, the system tested, the elicitation method and the validity checks. Treat that reporting discipline as part of the evaluation, not as optional documentation.
1. Define the decision and risk tier first
Write a short decision brief before selecting benchmarks or tuning prompts. It should state what the agent is meant to do, who may be affected, what information it can access, which actions it can take and what an error could cause. Include the business owner, technical owner and security or risk contact.
Describe the intended boundary
- Task: Use an observable description such as “classify incoming support cases and draft a response,” not “be a helpful support agent.”
- Users and affected parties: Identify employees, customers, suppliers or members of the public who may rely on an output or be affected by an action.
- Data: List repositories, fields, retention rules and data classifications the agent can reach. Separate read access from write, send, purchase or deletion capabilities.
- Autonomy: State whether the agent only suggests, prepares an action for approval or executes it without a person in the loop.
- Failure impact: Record financial, legal, safety, privacy, security, operational and reputational consequences.
Use these facts to assign a risk tier. The tier should determine test depth, approval authority, human oversight and operational support; it should not be an after-the-fact label.
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| Workflow category | Typical exposure | Evaluation and release emphasis |
|---|---|---|
| Productivity helper | Internal drafting, summarization or search with limited permissions | Representative task quality, source attribution, data boundaries, user feedback and an easy way to correct or disable the agent |
| Customer-facing agent | Direct interaction with customers or the public | Conversation edge cases, identity and privacy controls, policy compliance, escalation to a person, abuse testing and continuous incident monitoring |
| Decision-support agent | Recommendations that influence eligibility, pricing, employment or other consequential outcomes | Evidence quality, completeness, human review authority, bias and error analysis appropriate to the domain, traceability and documented override procedures |
| Mission-critical agent | Actions whose failure could interrupt essential operations or create serious harm | High-fidelity simulation, restrictive permissions, fail-safe behavior, independent security review, tested recovery and explicit executive approval |
These categories reflect Microsoft’s maturity guidance; they are a way to scale controls, not a universal certification scheme. If an agent spans categories, apply the stricter controls to the affected workflow.
2. Turn the workflow into a testable contract
An evaluation is only meaningful when “good” and “unacceptable” are defined in advance. Build a task set from real work, then add difficult cases that expose the boundaries of the design.
Construct a representative task distribution
- Sample ordinary requests across users, languages, document types, transaction states and permission levels that the production workflow will encounter.
- Include rare but consequential cases, such as conflicting records, missing fields, ambiguous instructions, stale documents, tool timeouts and requests that should be refused.
- Reserve a set of cases that will not be used for prompt tuning. Use it as a holdout to check whether apparent improvements generalize.
- Document how cases were selected and what portion of expected traffic each class represents. A score is uninterpretable if the test mix is unlike actual use.
Define outcomes and scoring
For each task, specify the expected result or a scoring rubric. Rubrics can assess factual correctness, completeness, policy compliance, format, usefulness, action selection and whether escalation was required. For actions, score both the decision and the execution: an agent that chooses the right action but applies it to the wrong record has failed.
Record unacceptable outcomes separately. Examples include exposing another person’s data, inventing a policy, taking an irreversible action without approval, claiming a tool succeeded when it failed, or omitting a material qualification. A single severe failure may block release even when average task quality is high.
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Keep a test-case record
| Field | What to record |
|---|---|
| Case identifier and category | Stable ID, workflow stage, user role and scenario class |
| Input and available context | Prompt, attached files, retrieved records and permissions presented to the agent |
| Expected behavior | Answer, action, refusal, clarification or human escalation required |
| Scoring rubric | Observable checks, severity levels and who is qualified to judge them |
| Observed trace | Tool calls, arguments, retrieved evidence, retries, latency and final output |
| Disposition | Pass, fail, blocked, needs review or accepted exception with an owner |
OpenAI’s evaluation playbook cautions against treating a benchmark result as a model-only property. Use an external benchmark only when its tasks, scoring and operating assumptions match your intended use; otherwise treat it as supplementary evidence.
3. Freeze and describe the complete configuration
Run tests against the interface and configuration that will be deployed. Create a versioned manifest so a result can be reproduced after a model, prompt, tool or permission change.
Configuration fields to capture
- Model and settings: Provider, model identifier, version or snapshot when available, temperature or equivalent sampling controls, system instructions and tool-selection settings.
- Agent harness: Orchestrator version, prompt templates, memory behavior, context-window rules, planning or delegation logic and stop conditions.
- Tools: Tool names, schemas, allowed operations, validation, network destinations, sandboxing and failure responses.
- Identity and permissions: Service account or user identity, least-privilege roles, tenant boundaries and approval requirements for writes or external communication.
- Data and retrieval: Index or database versions, filtering rules, ranking settings, document timestamps and handling of unavailable or conflicting sources.
- Safeguards: Input and output moderation, sensitive-data filters, policy checks, confirmation prompts and action allowlists.
- Retries and budgets: Maximum attempts, timeout behavior, token and time budgets, concurrency limits and fallback models or tools.
- Cost and latency: Measurement method and workload assumptions. Report these beside quality results rather than presenting quality as free of operational constraints.
Hold the harness and budget constant when comparing two agents. If one receives more retries, context or tool opportunities, that is a different system and must be reported as such.
4. Measure task performance and validity
Run the task set repeatedly enough to identify unstable behavior, but do not invent a universal pass threshold. The acceptable level depends on the risk tier, the cost of human review and the severity of residual failures.
Use more than one outcome measure
- Task success: Did the agent produce the required result or complete the action correctly?
- Quality: Was the response accurate, complete, relevant and understandable under the rubric?
- Safety and policy: Did it refuse prohibited requests and avoid disallowed content or actions?
- Efficiency: How much time, tool use, latency and spend did the workflow require under the stated budget?
- Human workload: How often did reviewers need to correct, repeat or escalate the work?
- Reliability: Does behavior remain consistent across retries, equivalent phrasings and temporary tool failures?
Separate automated checks from human judgments. Validate graders with examples, monitor disagreement and retain the original outputs and traces. A high score from an invalid or poorly matched test is not evidence of readiness.
5. Inspect grounding, tool use and the evidence trail
For factual answers and business actions, inspect how the agent arrived at the result, not only whether the final wording looks plausible. Preserve a trace that connects claims and actions to retrieved material, tool responses and approvals.
NIST’s evaluation-probe project describes automated comparisons between output claims and human-curated reference documents, with machine-readable audit trails. Its useful dimensions are:
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- Faithfulness: Does the cited or retrieved evidence actually support the claim?
- Completeness: Does the answer represent the important message of the source, including exceptions and qualifications?
- Sufficiency: Is the evidence strong and specific enough to carry the claim or justify the action?
Check that the agent selected the right source, used the correct record or tool, handled conflicting information and stopped when evidence was missing. A polished answer can still fail if it relied on an unauthorized document or silently filled a gap.
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Do not treat this experimental NIST work as a universal certification or pass/fail benchmark. It is an approach for improving grounding and traceability while the project remains ongoing.
6. Red-team misuse and high-impact failure modes
Ordinary task evaluations measure intended behavior. Red teaming probes whether the same workflow can be manipulated, misused or pushed into unsafe behavior. OpenAI describes this as complementary testing and summarizes the objective as: “Probe AI systems for misuse and security risks before deployment.”
Build an authorized attack plan
Test only systems and data you own or have explicit permission to assess. Define scope, test accounts, logging, stop conditions and a process for handling any real exposure.
- Prompt injection: Place conflicting instructions in retrieved documents, web pages, emails or tool output. Check whether the agent follows untrusted content over its governing policy.
- Data exfiltration: Ask for secrets, another user’s records, hidden instructions or information reachable through indirect tool paths.
- Privilege abuse: Try to make the agent use a powerful tool for an action outside the user’s role, alter approval metadata or bypass confirmation.
- Unsafe actions: Test destructive, financial, external-communication and irreversible operations, including near-miss wording and chained requests.
- Abusive and unexpected inputs: Include harassment, threats, disallowed content, malformed files, oversized context and ambiguous requests.
- Resilience attacks: Simulate tool errors, delayed responses, partial outages, repeated retries and manipulated return values.
For every finding, record the attack, affected permission or control, severity, reproducibility, evidence and remediation owner. Re-run the case after a fix; a prompt-only patch is not proof that the underlying permission or tool path is safe.
7. Establish enforceable release gates
Do not make broad access conditional on informal confidence. Require an approval record that names the owner, scope, evidence reviewed, residual risks, exceptions and expiry date.
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Minimum controls before release
| Control | Release question |
|---|---|
| Ownership and inventory | Is there a named business owner and technical operator, and is the agent registered with its purpose, version, data and tools? |
| Identity and access | Are users and service identities authenticated, least-privileged and separated by environment or tenant? |
| Data compliance | Are collection, retention, residency, deletion and sensitive-data rules documented and enforced? |
| Security review | Have prompt injection, tool abuse, secrets handling, dependency and network risks been assessed? |
| Approved development pattern | Does the implementation follow your organization’s standards for logging, testing, deployment and change control? |
| Auditability | Can investigators reconstruct prompts, tool calls, retrieved evidence, approvals, outputs and errors without exposing unnecessary sensitive content? |
| Human escalation | Is there a reachable person with authority to review, override, pause or terminate the workflow? |
| Incident response | Are detection, notification, containment, rollback and post-incident review procedures tested? |
Microsoft’s governance guidance describes this idea plainly: “A governance and security baseline sets the minimum requirements that every agent must meet before it is allowed to operate.” Treat that as vendor-authored guidance and adapt it to your own legal, security and operational requirements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Make operations part of readiness
Approval is not the end of evaluation. Production conditions change when the model is updated, a prompt is edited, a connector gains a new field, documents change, permissions expand or user behavior shifts.
Monitor what can change the risk
- Usage volume, user populations, task mix and unusual access patterns
- Task failures, policy violations, tool errors, fallbacks, retries and human corrections
- Grounding defects, unsupported claims, missing citations and evidence conflicts
- Latency, resource consumption and cost against the approved budget
- User feedback, complaints, near misses and confirmed incidents
Define alert owners and response times before launch. Maintain a kill switch or restriction mechanism that does not depend on the agent cooperating. Decide when to route work to people, disable a tool, limit a user group or retire the agent.
Trigger re-evaluation after material change
Repeat the relevant task, grounding, red-team and security tests after changes to the model, system instructions, tools, retrieval data, permissions, safety controls, retry logic, deployment environment or intended users. Keep the previous configuration and results so you can tell whether a change improved one dimension while degrading another.
How to compare multiple agents or vendors fairly
Compare complete systems under the same representative tasks and budget. Put the harness and configuration beside every result; otherwise a table of scores can reward hidden advantages such as extra retries or broader data access.
| Comparison axis | Questions to answer |
|---|---|
| Representative success | Which system performs better on the same task distribution and edge cases? |
| Harmful and policy failures | How often does each system produce a severe error, unauthorized disclosure or prohibited action, and how serious are those failures? |
| Grounding and traceability | Can reviewers connect claims and actions to adequate evidence and inspect a complete audit trail? |
| Permissions and data boundaries | What identities, tools, repositories and write operations are required, and can access be limited precisely? |
| Adversarial robustness | How does each system respond to injection, abusive requests, malformed inputs and tool failures? |
| Operations | Are monitoring, incident response, feedback, rollback and lifecycle ownership available and usable? |
| Human escalation | Can a person approve, override, pause or take over at the right points? |
| Reproducibility | Can another team recreate the test with the recorded model, prompts, tools, data and budget? |
| Latency and cost | What are the measured resource requirements under the same workload and limits? |
A practical evidence package for the approval meeting
Assemble these artifacts for each release candidate:
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- Task registry: Representative distribution, edge cases, expected outcomes, rubrics and unacceptable failures.
- Configuration manifest: Model, prompts, harness, tools, permissions, retrieval sources, safeguards, retries and budgets.
- Evaluation report: Results by task class, human-review method, validity checks, efficiency observations and unresolved limitations.
- Trace samples: Successful and failed runs showing evidence, tool calls, approvals and final outputs.
- Red-team report: Scope, attack cases, findings, fixes, residual risk and retest status.
- Governance record: Owners, data and security reviews, exception approvals, escalation contacts and launch scope.
- Operations runbook: Dashboards, alert thresholds chosen for the workflow, incident steps, rollback, user communications and retirement criteria.
This package lets an approver understand not only whether the agent worked in a test, but also what it was allowed to do, how it can fail and who will respond.
Common evaluation mistakes and their fixes
| Mistake | Why it fails | Better practice |
|---|---|---|
| Choosing a benchmark before defining the job | The benchmark may measure unrelated capabilities and hide critical workflow failures. | Define tasks, outcomes and unacceptable errors first; use benchmarks only as supporting evidence. |
| Testing the model without production tools | Permissions, retrieval quality, tool errors and orchestration often determine the real outcome. | Test the exact interface, harness, data and controls planned for deployment. |
| Reporting one aggregate score | Averages conceal rare but severe failures and differences between task classes. | Break results down by scenario, severity, evidence quality and action type. |
| Checking only the final answer | A plausible answer can come from unauthorized data or an unsafe tool call. | Review traces, evidence, permissions and execution steps. |
| Skipping adversarial testing | Normal users may never trigger injection, abuse or privilege-boundary failures found by attackers. | Run authorized red-team scenarios tailored to the agent’s tools and role. |
| Approving exceptions without an owner | Known risks become permanent and nobody is accountable for remediation. | Record the exception, compensating control, approver, expiry and retest condition. |
| Treating launch as a one-time event | Models, data, prompts and permissions drift after approval. | Monitor continuously and re-evaluate after material changes. |
What current public guidance establishes
NIST’s CAISSI material describes its practices for automated benchmark evaluations of language models and AI agent systems as an initial public draft. The page was updated September 30, 2026, and the guidance is voluntary rather than a final mandatory standard.
NIST’s evaluation-probe project, updated May 5, 2026, describes ongoing research into grounding and structured audit trails. It is not a mature universal certification program, so use its faithfulness, completeness and sufficiency concepts as evaluation dimensions rather than as a promised pass/fail score.
OpenAI’s evaluation and red-teaming documentation is useful for explaining setup sensitivity, evaluation reporting and adversarial security probing. Availability of managed red-teaming services may differ by enterprise arrangement; it does not remove your responsibility to test your own deployed workflow.
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Make the release decision explicit
Approve broad deployment only when the evidence matches the intended risk tier, severe failures are addressed or formally accepted, permissions and data boundaries are enforceable, traces support investigation, human escalation works and the operations team can monitor and stop the agent. If evidence is incomplete, restrict the audience, tools or autonomy and continue testing rather than treating uncertainty as approval.
The strongest evaluation is therefore a living control system: a clearly bounded task, representative and adversarial tests, a reproducible configuration, evidence-aware review, enforceable governance and monitoring that can trigger correction, restriction or retirement.
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