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Evaluate an enterprise AI agent as a controlled business process, not as a chatbot demo. Start by documenting the decision or workflow, the people affected, the data and tools the agent can reach, and the actions it may take. Set measurable accuracy, safety, escalation, privacy, security, and audit requirements before you compare vendors. Then test competing systems with representative and adversarial cases in an environment that resembles production, run a bounded pilot with human controls, and monitor behavior after launch.
This guide is primarily based on United States frameworks and regulatory materials current through October 2026. Employment, financial-services, privacy, accessibility, and consumer-protection duties vary by jurisdiction, sector, and use case; involve qualified legal and compliance professionals for decisions that affect people or regulated activities.
What an enterprise AI-agent evaluation must establish
An AI agent combines a model with instructions, enterprise data, tools, and an operating loop. It may retrieve policy documents, draft a report, route a request, call an HR or accounting system, or initiate an external action. The evaluation question is therefore broader than “How fluent are the answers?” You need evidence that the complete system performs the intended work, stays within its authority, exposes enough evidence for review, and fails safely when data, tools, or instructions are incomplete.
NIST states that “The AI RMF is intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.” The NIST AI Risk Management Framework (AI RMF) was released on January 26, 2023, and NIST’s current page says it is being updated. Its four functions—Govern, Map, Measure, and Manage—provide a useful lifecycle structure, but the framework is voluntary rather than a universal certification or pass/fail standard.
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Use the framework as a spine for your procurement and control record. The AI RMF Core emphasizes documented test methods and metrics, assessment under conditions similar to deployment, accountability, independent expertise where appropriate, third-party risk practices, and monitoring in production. The NIST Generative AI Profile adds procurement, service-level, transparency, and change-management considerations for generative systems.
1. Define the workflow, decision and authority before seeing a demo
Write a one-page scope statement for each proposed agent. Do not evaluate “an HR agent” or “a finance copilot” as if either were a single risk category. An agent that answers a benefits question from approved documents has a different failure profile from one that recommends termination, changes payroll, approves a vendor, or releases a payment.
Record the operating boundary
- Workflow: State the concrete process, such as answering policy questions, drafting a monthly variance report, routing a leave request, reconciling transactions, or preparing a credit decision for human review.
- Users: Identify employees, managers, recruiters, payroll staff, accountants, treasury staff, customers, or other users, and note their training and authority.
- Affected people: List workers, applicants, customers, suppliers, borrowers, or other parties who may bear the cost of an error.
- Data sources: Name the systems, document repositories, records, and external feeds the agent may read. Classify sensitive personal, payroll, health, tax, banking, or confidential business data.
- Connected tools: Enumerate APIs and applications the agent can query or write to. Include identity providers, ticketing systems, payroll, general ledger, expense, procurement, CRM, payment, and communication tools where relevant.
- Permitted actions: Separate retrieval, summarization, drafting, recommendation, and execution. List every write, message, approval, transaction, or credentialed action.
- Approval gates: Name actions that always require a human decision, the role that may approve them, and the evidence that person must inspect.
- Prohibitions: State actions the agent must never take, such as changing compensation, deleting records, bypassing segregation of duties, making an unsupervised employment decision, or releasing funds.
- Owner and escalation: Assign a business owner, technical owner, compliance contact, incident lead, and an emergency authority able to revoke credentials or disable the agent.
Classify consequence and reversibility
Rate each workflow using consequence, scale, reversibility, data sensitivity, and decision impact. A low-consequence informational answer may tolerate a different error rate than an automated payment or an employment recommendation. Record the rationale rather than assigning an unexplained “low,” “medium,” or “high” label.
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|---|---|---|
| Policy retrieval and citation | Read-only; no external action | Source grounding, current content, handling of ambiguity, privacy, and accessibility |
| Drafting a report, message, or case note | Creates a draft for review | Factual support, disclosure of uncertainty, protected-data handling, editability, and audit trail |
| Recommendation or triage | Ranks, routes, or flags work | Consistency, error concentration across groups or cases, rationale, appeal or correction path, and human review |
| Record change or approval preparation | Writes to a system or prepares a decision | Least privilege, dual control, complete logging, reversibility, and approval evidence |
| Transaction or external decision | Can send, pay, hire, deny, or otherwise create an external consequence | Explicit authorization, independent verification, fail-safe stop, incident response, and legal obligations |
Keep this boundary document versioned. A change from drafting to sending, or from read-only access to write access, is a new risk assessment rather than a minor configuration update.
2. Set measurable acceptance criteria before comparing vendors
Build the test plan from actual work samples and failure modes, not a vendor’s general benchmark. Freeze the test version, configuration, connected data, model or agent version, and evaluator instructions so results can be reproduced. Compare vendors on the same cases and permissions whenever feasible.
Construct a representative test set
Include routine cases and difficult cases that staff actually encounter:
- Complete, well-formed requests and normal high-volume work.
- Incomplete, ambiguous, contradictory, stale, or duplicated inputs.
- Documents with conflicting instructions, hidden prompt-injection text, misleading content, or unsupported claims.
- Requests from users with different roles, including attempts to access another person’s records.
- Tool timeouts, authentication failures, rate limits, unavailable records, and partial updates.
- Cases requiring a refusal, a clarifying question, a named human approver, or a safe rollback.
- Representative language, accessibility needs, job families, transaction types, regions, and business units relevant to the deployment.
Measure outcomes that matter
| Metric | What to record | Why it matters |
|---|---|---|
| Task completion | Whether the intended task was completed correctly and within the permitted scope | Separates useful work from fluent but incomplete responses |
| Factual support | Claims supported by an authoritative record, with source location where applicable | Reveals unsupported policy, accounting, or case assertions |
| Unsupported claims | Invented facts, citations, calculations, or actions | Shows hallucination risk and review burden |
| Incorrect actions | Wrong tool calls, unauthorized writes, duplicate actions, or unsafe recommendations | Captures severity beyond answer quality |
| Escalation quality | Whether the agent stops at the right time, explains why, and routes to the correct owner | Tests safe handling of uncertainty and exceptions |
| Consistency | Variation across repeated runs, equivalent cases, roles, and relevant groups | Finds unstable behavior and uneven error patterns |
| Latency and cost | Response time, tool time, human review time, and usage cost under expected load | Shows whether the workflow is operationally viable |
| Uncertainty disclosure | Whether missing data, conflicting sources, and confidence limits are made visible | Prevents false certainty in consequential decisions |
Do not invent a universal passing percentage. Set thresholds according to the consequence, volume, reversibility, and risk tolerance of the workflow. For a read-only policy assistant, a low-severity error may trigger correction and retraining; for a payment or employment action, a single unauthorized execution may be a deployment blocker. Document the test set, metric definitions, sampling method, evaluator qualifications, results, known limitations, and the decision rule.
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Use domain and independent reviewers
Have HR practitioners review employment workflows and controllers, accountants, treasury staff, or other qualified finance users review financial workflows. Add an independent reviewer for high-impact or regulated cases when practical. Reviewers should inspect the underlying records, not only the final prose, and should label severity, not merely “right” or “wrong.” Preserve disagreements and adjudication notes so the acceptance decision is auditable.
3. Inspect data, privacy, security and vendor dependencies
Require written answers and evidence; treat an unverified “yes” as unknown. Your questionnaire should cover the whole system, including the model provider, agent framework, hosting environment, connectors, subcontractors, and support personnel.
Data-use questions
- What prompts, retrieved records, outputs, tool arguments, and telemetry leave your environment?
- Where are data and backups processed and stored, and which jurisdictions may receive them?
- How long are prompts, outputs, traces, and support records retained? Can retention be configured by data class?
- Is customer content used to train, fine-tune, evaluate, or improve a shared model? What opt-out or contractual restriction exists?
- Can you export and delete records, including indexes, caches, embeddings, backups, and incident copies?
- How are sensitive fields masked, minimized, encrypted, and segregated between customers and environments?
Identity and tool controls
- Use enterprise identity, strong authentication, role-based access, and short-lived credentials where supported.
- Apply least privilege separately to the user, agent runtime, connector, and service account. Do not give an agent a broad administrator token to simplify integration.
- Require explicit approval for high-impact tool calls, with the approver, timestamp, inputs, and resulting record captured.
- Restrict destinations, file types, query scope, and transaction limits. Test attempted privilege escalation and cross-tenant access.
- Log prompts, retrieved sources, model and tool versions, tool arguments, outputs, approvals, errors, and final actions in a tamper-evident system appropriate to the record.
Vendor and change-management evidence
Request architecture and data-flow diagrams, component and model documentation, security-assurance materials, vulnerability and incident processes, service levels, support escalation, subprocessors, portability terms, and change-notification commitments. The NIST Generative AI Profile identifies procurement due diligence, service-level agreements, and third-party transparency as possible risk-management practices; tailor the request to the product and data involved rather than collecting documents that no one will review.
Define what happens when the underlying model, prompt, connector, policy corpus, or safety filter changes. Require a notice period where feasible, a regression test, version pinning or rollback option, and an owner who can suspend the agent until the new configuration is approved.
4. Make human oversight operational
“Human in the loop” is not a control unless the person has authority, time, context, and a practical way to intervene. For every action, specify whether it is automatic, review-required, or prohibited.
Design the approval and escalation path
- Define the trigger: uncertainty, missing evidence, conflicting records, sensitive population, high dollar value, legal hold, or another stated condition.
- Name the approver role and the maximum time allowed before the task is paused or rerouted.
- Show the input records, retrieved sources, proposed action, tool calls, uncertainty, and policy rule that led to the proposal.
- Give the approver clear choices: approve, edit, reject, request more information, or escalate. Do not force approval when evidence is missing.
- Record the decision, reason, identity, timestamp, and any changes. Preserve the agent’s original proposal for later investigation.
- Provide a stop control that revokes credentials or disables automation without waiting for a vendor support response.
Test whether a reviewer can understand what happened, correct a wrong result, reverse a permissible action, and report an incident. Measure review time and override rates during the pilot; a control that creates an unmanageable queue is not operationally effective.
5. Apply HR-specific evaluation and legal checks
HR use cases differ sharply. An employee self-service agent, a recruiter’s drafting assistant, a scheduling tool, and a system that scores applicants or recommends discipline require separate assessments.
Employment background reports and algorithmic scores
If an agent retrieves, compiles, or creates a third-party background report or algorithmic score used for hiring, promotion, reassignment, or retention, investigate whether the information is a consumer report and whether the Fair Credit Reporting Act (FCRA) applies. The CFPB Circular 2024-06 discusses this potential coverage; it does not state that every HR agent is subject to the FCRA. Identify the source, purpose, permissible use, notices, consent, pre-adverse and adverse-action process, and dispute handling that may apply to the specific workflow, with employment counsel.
HR test cases and controls
- Check job-related validity: test whether recommendations rely on criteria tied to the role rather than proxies or irrelevant personal information.
- Evaluate uneven error patterns across relevant groups, job contexts, languages, and accessibility needs. Examine false positives and false negatives, not only aggregate accuracy.
- Verify data provenance, freshness, correction mechanisms, retention, and access limits for applicant and employee records.
- Require meaningful human review for decisions affecting employment status, pay, promotion, discipline, scheduling, or access to benefits.
- Test explanations that a worker or applicant can understand and a reviewer can substantiate from records.
- Provide a route to challenge or correct information where law, policy, or the workflow requires it; record who investigates and the response deadline.
Other employment-law duties depend on facts and jurisdiction. Do not treat a vendor’s fairness statement as a legal conclusion or as a substitute for a documented job-related validation and review process.
6. Apply finance-specific evaluation and legal checks
State whether the agent supports internal analysis, accounting close, customer communication, credit underwriting, payment execution, collections, fraud review, or another function. The controls, evidence, and approval limits should follow the consequence of that function.
Credit decisions and adverse-action reasons
For credit decisions, confirm that the organization can provide specific principal reasons for an adverse action even when a complex algorithm, AI system, or machine-learning model contributed to the result. The CFPB Circular 2022-03 addresses this duty for creditors. Test the complete notice workflow: the factors actually used, the reasons recorded, the notice generated, and the ability of a reviewer to reconcile the notice with the decision record.
Banking model-risk scope in 2026
On April 17, 2026, the Federal Reserve, OCC, and FDIC issued revised interagency model-risk guidance. Federal Reserve SR 26-2 says the guidance supersedes SR 11-7 and SR 21-8. The current Supervisory Guidance on Model Risk Management expressly excludes generative and agentic AI models from its scope and says organizations should determine appropriate governance and controls for systems outside that scope.
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The guidance is expected to be most relevant to banking organizations with more than $30 billion in assets, but it may also matter to smaller banks with significant model-risk exposure. Do not describe SR 11-7 as the current interagency guidance, and do not claim that the revised guidance directly governs a generative or agentic system merely because the institution is a bank. Map the agent to applicable safety, cybersecurity, consumer-protection, records, and model-governance requirements separately.
Finance test cases
- Use closed-period and current-period records, adjustments, reversals, duplicate invoices, foreign-currency items, and missing documentation.
- Attempt segregation-of-duties violations, limit breaches, duplicate payments, changed bank details, and emergency requests.
- Require source-linked calculations, reproducible formulas, and clear treatment of estimates and uncertainty.
- Test tool failures and partial commits so the agent does not retry a payment or post a duplicate journal entry.
- Verify retention, legal hold, audit export, and reconciliation with the system of record.
7. Compare agents with a weighted scorecard
Use one scorecard per workflow, with weights reflecting impact. A high-quality answer should not compensate for an unauthorized transaction. Require evidence for every score and mark unsupported claims as unknown rather than awarding points.
| Evaluation axis | Evidence to request |
|---|---|
| Task performance and error severity | Results on your fixed test set, severity labels, failure examples, and reviewer agreement |
| Robustness and safe failure | Behavior with ambiguity, conflicting documents, prompt injection, unavailable tools, and malformed inputs |
| Tool-use boundaries | Permission model, allowlists, approval gates, transaction limits, and blocked-call logs |
| Evidence and traceability | Source passages, calculations, tool traces, version identifiers, and reproducible records |
| Data handling and privacy | Processing locations, retention, training use, deletion, export, encryption, and tenant isolation |
| Security and identity | Authentication, authorization, secrets handling, vulnerability response, and incident notification |
| Human override and escalation | Named roles, approval UX, stop controls, correction path, and measured review burden |
| Auditability | Immutable or access-controlled logs, time stamps, approvals, changes, and export format |
| Integration and recovery | Rollback, idempotency, retries, outage behavior, sandboxing, and reconciliation |
| Accessibility and user experience | Keyboard and assistive-technology support, language coverage, understandable notices, and training needs |
| Vendor transparency and change control | Subprocessors, model and connector changes, notice periods, documentation, and rollback options |
| Monitoring and incident support | Operational metrics, alerting, support response, incident exports, and suspension procedures |
| Implementation burden and total cost | Integration effort, data preparation, review staffing, usage charges, support, and ongoing testing |
Keep the raw evidence beside the score. Record who tested, which configuration ran, the date, and any assumptions. Re-score after material changes rather than treating procurement approval as permanent.
8. Run a bounded pilot, then monitor continuously
Pilot controls
- Select a limited population, workflow, time period, and data set. Exclude irreversible actions until the agent has demonstrated safe behavior.
- Use deployment-like identities, integrations, permissions, queues, and exception volumes. A presentation sandbox cannot establish production safety.
- Require human review at the level justified by risk. Log every override, near miss, false escalation, and user complaint.
- Define rollback criteria before launch, including unauthorized action, critical data exposure, repeated unsupported claims, unexplained performance drift, or an unmanageable review queue.
- At the end of the pilot, compare results with the pre-set thresholds, investigate failures, and approve, redesign, restrict, or stop the deployment.
Production monitoring
Monitor both functionality and behavior: task success, severe errors, unsupported claims, blocked and successful tool calls, escalation and override rates, latency, cost, access anomalies, data-quality changes, and incidents. Segment results by workflow, user role, region, language, job or transaction type, and other relevant contexts so aggregate averages do not hide concentrated harm.
Set review intervals and event triggers. A model, prompt, retrieval index, connector, policy corpus, permission, or business process change should trigger regression testing. Define who can pause the agent, revoke tokens, notify affected parties, preserve evidence, investigate root cause, and authorize restart. Maintain a current inventory, risk assessment, test reports, approvals, incidents, and version history.
Common evaluation mistakes and how to correct them
Choosing the most fluent demo
Symptom: The agent gives polished answers but has not been tested against your records or tools.
Correction: Require the same representative cases, permissions, failure conditions, evidence display, and handoff behavior for every vendor.
Testing only happy paths
Symptom: The test set contains complete, friendly requests and no conflicting records or outages.
Correction: Add ambiguity, stale data, malicious or misleading retrieved text, unauthorized requests, tool failures, and recovery cases.
Granting broad credentials for convenience
Symptom: One service account can read and write across HR or finance systems.
Correction: Separate identities, least privilege, allowlists, approval gates, transaction limits, and independent reconciliation.
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Symptom: Staff approve suggestions without seeing sources, uncertainty, or the proposed tool call.
Correction: Give reviewers context, authority to reject or edit, sufficient time, a named escalation owner, and a recorded decision path.
Ignoring changes after launch
Symptom: A passed pilot is treated as permanent despite model, connector, data, or policy changes.
Correction: Version the system, monitor production behavior, require regression tests, and maintain a suspension and rollback procedure.
Release checklist
- The workflow, affected people, data sources, tools, authority, prohibited actions, owner, and escalation path are documented.
- Risk thresholds and metric definitions were set before vendor comparison.
- Routine, edge, adversarial, permission, outage, and recovery cases were tested with deployment-like conditions.
- Domain reviewers and, where appropriate, independent reviewers assessed the evidence.
- Data processing, retention, training use, deletion, security, subprocessors, service levels, and change notices are understood.
- Least-privilege access, approval gates, logging, rollback, and credential-revocation controls are verified.
- HR use cases were checked for possible FCRA coverage and other fact-specific employment obligations.
- Finance use cases were classified, credit adverse-action reasons were tested where relevant, and banking guidance scope was dated correctly.
- A bounded pilot has explicit stop criteria, and production monitoring, incident response, and re-evaluation owners are assigned.
The strongest procurement decision is not necessarily the agent with the highest conversational score. It is the system whose demonstrated performance, authority limits, evidence, human controls, vendor transparency, and operating cost fit the consequences of the specific HR or finance workflow—and whose controls remain workable after deployment.
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