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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThe safest way to evaluate an enterprise AI agent is to assess a specific workflow—not a chatbot demo. Define the agent’s authority, affected people, data, integrations, error consequences and human checkpoints; set measurable acceptance thresholds; test ordinary and adversarial cases in deployment-like conditions; inspect privacy, security, auditability and vendor change controls; then run a bounded pilot with production monitoring and a rollback plan.
Start with the workflow, not the vendor
An “AI agent” can answer a policy question, draft a report, recommend a decision or execute a payment. Those are materially different risk profiles. Before inviting vendors to demonstrate anything, write a one-page scope for each proposed use case.
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- Process: State the exact job to be supported, such as answering benefits questions, routing an employee request, preparing a monthly variance report or initiating a payment.
- Users and affected people: Identify employees, applicants, customers, managers, finance staff, suppliers and anyone else who could be affected by an output or action.
- Data: List source systems, record types, sensitivity, retention requirements and data owners.
- Connected tools: Document the HRIS, payroll, ERP, ticketing, email, identity and payment systems the agent can access.
- Authority: Separate retrieval and drafting from recommendations and actions. Record what the agent may do automatically, what needs approval and what is prohibited.
- Accountability: Name the business owner, technical owner, risk or compliance reviewer and person responsible for exceptions.
Rate each workflow by the consequence, scale and reversibility of an error, plus the sensitivity of its data. A wrong answer to a low-stakes internal question is not equivalent to an incorrect termination recommendation, payroll change, credit decision or payment. This scoping work follows the risk-identification intent of the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF), which is voluntary and organized around Govern, Map, Measure and Manage. NIST says the framework is intended “to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.” See the NIST AI RMF and its AI RMF Core.
Set acceptance criteria before a demo
A vendor’s benchmark or polished demonstration cannot establish that an agent is suitable for your environment. Build a test plan from real tasks and define what “good enough” means before comparing products. Do not use a universal pass percentage: thresholds should reflect the workflow’s consequences and your risk tolerance.
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Build a representative test set
- Routine cases covering the most common requests and records.
- Edge cases, incomplete submissions, conflicting records, unusual job structures and nonstandard accounting entries.
- Ambiguous questions where the correct behavior is to ask for clarification.
- Permission-boundary tests, including attempts to retrieve another employee’s or customer’s restricted data.
- Prompt-injection and misleading-content tests in documents, email, knowledge bases and uploaded files.
- Integration failures, timeouts, duplicate events, stale data and unavailable approvals.
- Recovery cases in which the agent must stop, explain the problem and hand the work to a person.
Measure more than answer accuracy
| Measure | What to examine |
|---|---|
| Task completion | Whether the requested job was completed within the stated business rules. |
| Factual support | Whether each material claim is supported by an authoritative record, with a usable citation or source trail. |
| Unsupported claims | How often the agent invents policy, numbers, approvals, people or transactions. |
| Incorrect actions | Unauthorized edits, messages, approvals, payments, workflow transitions or data disclosures. |
| Escalation quality | Whether uncertainty and exceptions reach the right person with enough context to act. |
| Consistency | Whether materially identical inputs receive materially consistent results across runs and users. |
| Latency and cost | Response time, human handling time, tool-call volume and usage cost under expected load. |
Record the test-set version, configuration, model and retrieval sources, metrics, results, failures and limitations. NIST calls for documented test methods and metrics, assessment under conditions similar to deployment, and monitoring of system functionality and behavior after launch. Include domain users and, where appropriate, independent reviewers rather than allowing the vendor or project team to grade its own work.
Use a risk-weighted scorecard
Score every candidate on the same workflow, cases and configuration where feasible. Weight categories according to impact: a payment-execution agent should give more weight to authorization and recovery than to conversational style, while an HR policy assistant may emphasize source traceability and privacy.
| Evaluation axis | Evidence to request | Questions for the test |
|---|---|---|
| Performance and error severity | Results on your test set, including failures and confidence handling. | How often is the output wrong, and what happens when it is? |
| Robustness and safe failure | Adversarial, incomplete-input and outage results. | Does the agent stop safely instead of guessing or retrying an unsafe action? |
| Tool-use boundaries | Permission model, approval gates and action logs. | Can it read, write, send, approve or pay? Are those scopes separately controllable? |
| Evidence and traceability | Source citations, retrieved passages, reasoning summaries and immutable event records. | Can a reviewer reconstruct why an answer or action occurred? |
| Data handling and privacy | Data-flow diagram, retention settings, deletion and export procedures, training-use terms. | What leaves your boundary, for how long and for which purpose? |
| Security and identity | Authentication, authorization, tenant isolation, encryption and incident process. | Does the agent enforce the user’s existing permissions at every tool call? |
| Human override | Approval workflow, escalation queues, correction and disablement procedures. | Can an authorized person pause, reverse or correct an action quickly? |
| Integration and recovery | API limits, idempotency behavior, retry logic, backup paths and service levels. | What happens after a timeout, duplicate event or partial transaction? |
| Accessibility and usability | Keyboard, screen-reader, language and notification behavior, plus user research. | Can the intended workforce review and challenge results? |
| Vendor transparency and change control | Model and component documentation, release notices, subcontractors and support commitments. | How will you learn about a model, prompt, policy or integration change? |
| Monitoring and incident support | Telemetry, alerting, audit export, response times and post-incident process. | Can you detect drift, misuse, anomalous actions and near misses? |
| Implementation burden and total cost | Integration work, migration, training, review staffing, usage charges and exit costs. | What ongoing people and controls are required, not just the license? |
Ask the vendor to demonstrate evidence for each answer. If a control or metric is not established, mark it “unknown” rather than awarding credit for an assurance statement.
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Inspect data, privacy and third-party dependencies
Obtain a data-flow description showing every model, retrieval index, logging system and external tool that receives information. Ask specifically:
- Which prompts, retrieved records, outputs, metadata and tool results are transmitted?
- Where are they processed and stored, and in which jurisdictions?
- How long are content, traces and backups retained?
- Is customer content used to train or tune a shared model, and can that use be disabled contractually and technically?
- How are deletion, export, legal holds and tenant separation implemented?
- How are identities, service accounts, secrets and administrator privileges controlled?
- Which subcontractors, foundation-model providers and hosting services can access the data?
- How are vulnerabilities, suspected misuse and data incidents reported and investigated?
Require least-privilege permissions, separate credentials for read and write operations, approval for high-impact tool calls, and logs that link the human requester, agent version, retrieved sources, tool arguments, result and approver. Ask for relevant assurance material and change-notification commitments, but do not treat a certification or questionnaire as a substitute for testing your workflow. NIST’s 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.
Make human oversight operational
“Human in the loop” is meaningful only when the person has time, authority and evidence to intervene. Write an action policy for each workflow:
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- Automatic: Low-impact, reversible actions with narrow permissions, such as classifying a request or drafting—not sending—a response.
- Approval required: Actions affecting pay, employment status, access, customers, financial records, funds or external communications.
- Always escalate: Conflicting records, missing evidence, suspected fraud, uncertain identity, policy exceptions, protected data requests and any case outside the tested scope.
Test whether an approver can inspect the supporting records, understand what the agent did, reject or edit the proposed action, and see whether the correction took effect. Define an owner and response time for exceptions. Maintain a tested procedure to disable the agent, revoke credentials, freeze queued actions and restore manual processing. Keep incident, near-miss and override records so recurring failure patterns can change the workflow or its controls.
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Separate assistance from employment decisions
A policy-search assistant, interview-scheduling agent and applicant-ranking system should not share one risk rubric. For each use, document whether the agent merely retrieves or drafts information, recommends an outcome, or makes or executes an employment decision. Test job-related validity, data provenance, accessibility, language variation, inconsistent source records and uneven error patterns across relevant groups and job contexts. Provide a meaningful human review path rather than treating a score as a final decision.
Check whether third-party background reports trigger FCRA duties
If the agent uses or creates a third-party background report or algorithmic score for hiring, promotion, reassignment or retention, determine whether the information is a consumer report and whether the Fair Credit Reporting Act (FCRA) applies. The Consumer Financial Protection Bureau’s Circular 2024-06 explains that such reports and scores are often governed by the FCRA, but coverage depends on the information source and use. Involve employment counsel to assess notices, permissible purpose, accuracy, dispute handling and any jurisdiction-specific obligations.
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Protect employee confidentiality
Verify that the agent cannot expose medical, compensation, investigation, immigration or performance records to users who lack permission. Test indirect leakage: ask questions that combine allowed fields to infer a restricted fact. Log access and retrieval decisions, not just the final answer. Give employees and applicants a route to report inaccurate records or an inappropriate automated recommendation where applicable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Finance-specific evaluation
Classify the financial function
State whether the agent supports internal analysis, accounting close, forecasting, customer communication, credit underwriting, payment execution, fraud review or another function. The same model may be acceptable for drafting a variance explanation but unsuitable for posting a journal entry or releasing funds. For every write action, test segregation of duties, approval thresholds, duplicate prevention, reconciliation and rollback.
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For credit decisions, complex algorithms do not remove the obligation to provide specific principal reasons for an adverse action. The CFPB’s Circular 2022-03 addresses this duty for creditors using complex algorithms, including artificial intelligence and machine learning. Test the full path from input data to decision, notice generation and record retention. A generic statement such as “the model’s assessment” is not a substitute for the required specific reasons.
Date banking model-risk references correctly
On April 17, 2026, the Federal Reserve, Office of the Comptroller of the Currency and Federal Deposit Insurance Corporation issued revised interagency model-risk guidance. Federal Reserve SR 26-2 says it supersedes SR 11-7 and SR 21-8. The accompanying Supervisory Guidance on Model Risk Management expressly excludes generative and agentic AI models from its scope; institutions must determine suitable governance and controls for systems outside that scope. The guidance is expected to be most relevant to banking organizations with more than $30 billion in assets, but smaller banks with significant model-risk exposure may also find it relevant. Do not describe SR 11-7 as the current rule or claim that the revised guidance directly governs generative agents.
Run a controlled pilot
- Choose a bounded workflow: Limit users, records, tools, transaction values and operating hours. Keep a manual path available.
- Freeze the baseline: Record the model, prompts, retrieval sources, permissions, integrations, test set and acceptance thresholds.
- Use deployment-like data safely: Prefer properly governed production samples or realistic masked data, with the same identity and access rules expected at scale.
- Require review at the defined gates: Route high-impact outputs to named approvers and prevent unapproved tool calls technically, not only through written policy.
- Log outcomes: Capture successful tasks, errors, near misses, overrides, user feedback, latency, cost and any source or model changes.
- Exercise failure recovery: Simulate an outage, bad retrieval, revoked credential, duplicate event and unsafe recommendation. Confirm that staff can pause the system and continue manually.
- Make a scale decision: Expand only when results meet the workflow’s thresholds and unresolved risks have an owner, deadline and compensating control.
NIST recommends testing before deployment and regularly during operation, with monitoring of functionality and behavior in production. Re-evaluate after model, prompt, data-source, permission, integration or policy changes—not only on a fixed annual schedule.
Govern the agent after launch
Assign a standing owner for performance, security, compliance and business outcomes. Define alerts for rising unsupported claims, permission denials, unusual tool calls, latency, cost, repeated human overrides and changes in the distribution of requests. Review samples for factual support and disparate failure patterns. Keep an inventory of agents, versions, connected tools, data classes, approval gates and retirement dates.
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Questions to resolve before signing
- Can the supplier prove the controls on your exact workflow, or only describe them generally?
- What happens to your data when the contract ends, an integration is discontinued or the supplier changes its underlying model?
- Can you export complete audit records in a usable format?
- Which changes require notice, customer testing or approval, and how much advance time is provided?
- What service levels, support response times and incident-notification deadlines apply?
- Can you revoke every credential and continue the process manually?
- Who pays for reconfiguration, revalidation and increased usage after a model or policy change?
A strong evaluation produces a documented decision: the workflow is approved, approved with controls, deferred pending evidence, or rejected. That record should include the tested scope, authority granted, results, limitations, residual risks, owners and the conditions that would force re-evaluation.
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