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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Evaluate an AI assistant against a specific government workflow, not as a universally “best” product. Define the task, affected people, data, consequences of error, and accountable owner; confirm the agency’s approval and data rules; then test candidate tools on representative, authorized examples and compare their performance, protections, costs, and ability to be monitored. No single assistant is approved for every government agency or workflow.
Start with the work, not the product
Before comparing assistants, describe the work in operational terms. Specify who uses the tool, who may be affected by its output, what information it receives, what it is allowed to produce or change, and who is accountable for the result. Include the existing systems and human steps around the task.
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Distinguish low-consequence support—such as helping staff draft or retrieve information—from work that could affect eligibility, benefits, enforcement, health, safety, rights, or official determinations. The more consequential the outcome, the stronger the evidence, safeguards, and human review should be. A person’s presence in the process is not enough if that person lacks time, information, expertise, or authority to catch and correct errors.
This framing reflects the U.S. Government Accountability Office’s 2021 accountability framework, which organizes oversight around governance, data, performance, and monitoring. GAO notes that “AI systems pose unique challenges to such oversight because their inputs and operations are not always visible.” NIST’s 2021 procurement guidance likewise discusses proportional assessment, safeguards, and the relationship between automation and oversight. Use these as foundational guidance alongside current agency policy.
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Confirm authority and policy before a pilot
An evaluation is not permission to use a tool. Before staff enter real workflow material—even in a pilot—identify the applicable approval route and officials. Check the current agency AI policy, procurement process, data classification rules, privacy and security review, accessibility requirements, records schedule, and any program-specific rules. Include AI features added to software the agency already uses; existing procurement or deployment does not by itself establish approval for a new AI function.
- Confirm which assistant, deployment, and configuration are approved for the proposed use.
- Confirm what data classes may be processed, and where prompts, outputs, files, and logs go.
- Identify who must review the use and which records, privacy, security, accessibility, and procurement obligations apply.
- Check whether approval covers the specific workflow and intended users, rather than just the product name.
At the federal level, the General Services Administration’s active 2026 directive covers assessment, procurement, use, monitoring, and governance, including risk management, transparency, and accountability. GAO’s September 2025 report identified 94 AI-related requirements with government-wide scope or implications as of July 2025. That is GAO’s count within its stated scope and date—not a count of requirements applicable to every assistant or agency. The landscape is broad and can change, so a generic checklist is not legal or policy clearance.
Run a workflow-specific test
Build a test set from realistic, authorized examples of the actual task. Cover routine cases, ambiguous requests, missing or conflicting information, unusual cases, and cases where the appropriate response is to abstain or escalate. Define the expected result before testing: what counts as correct, complete, supported by the source material, timely, and usable by staff?
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- Prepare representative cases. Use examples that reflect the workflow and its meaningful variations, while following agency rules for data use. Do not use sensitive records in a system that is not authorized to process them.
- Specify acceptable behavior. For each case, record the expected answer or action, acceptable variation, required source support, and conditions that should trigger a clarification, refusal, or escalation.
- Test the same cases in each candidate. Keep the task, instructions, configuration, and review method consistent enough for a fair comparison. Record when a candidate gives an unsupported answer, omits a critical detail, mishandles uncertainty, or fails to escalate.
- Have qualified reviewers score results. Assess both output quality and failure behavior. For a consequential workflow, involve reviewers who understand the work and the effects of mistakes.
- Keep an evaluation record. Capture prompts, model and configuration details, dates, scores, reviewer notes, and known limitations so later tests can be compared.
GAO’s acquisition review identifies testing requirements as a procurement lesson, and its accountability framework emphasizes performance and monitoring. A product demonstration is not a performance study. Do not claim an accuracy rate unless the agency has a defined method, representative data, a sufficient documented sample, and recorded results. Vendor evaluations can inform the assessment, but they do not substitute for testing in the intended workflow. The cited guidance does not prescribe a universal scorecard, percentage weighting, sample size, or pass threshold; the agency should set these before reviewing results, proportionate to the risks.
Compare the dimensions that affect safe use
Use one set of questions for every candidate. The following comparison areas synthesize GAO’s accountability and acquisition work, NIST’s risk and oversight guidance, and GSA’s lifecycle directive; they are not a mandated government scoring standard.
| Dimension | Questions and evidence to examine |
|---|---|
| Task performance | Does the assistant complete the defined task on representative cases? What errors occur, how often in the documented test, and with what potential consequences? |
| Grounding and traceability | Can a reviewer find and verify the sources behind factual claims? Does the system identify uncertainty, missing information, or unsupported requests? |
| Data protection | What happens to prompts, outputs, uploaded records, logs, and derived data? Ask about retention, deletion, training use, disclosure, and subprocessors. |
| Security and access | Does the particular deployment meet agency security controls, identity and access requirements, and the classification level of the intended data? |
| Human responsibility | Who reviews output, handles exceptions, can correct or override it, and approves any official action? Can the person actually perform those duties? |
| Fairness and impacts | Could errors or uneven performance affect protected groups, public-service access, rights, or opportunities? Who is consulted, and how are impacts assessed? |
| Accessibility and usability | Can staff and affected users operate the system with required assistive technology, and are accessible alternatives available? What accessibility evidence and user testing support the answer? |
| Records and transparency | Are prompts and outputs records under the applicable rules? What must be retained, disclosed, or explained to users? |
| Integration and continuity | Does the tool fit the workflow without exposing data or triggering unreviewed actions? What happens during an outage, vendor change, or model update? |
| Total cost and capability | What are direct and indirect costs, including integration, expert review, training, monitoring, and exit? Does the agency have the technical capacity to evaluate and operate the system? |
| Monitoring and change | How will changes in service behavior, input data, terms, or workflow be detected? Who responds to incidents, and who can pause or end use? |
Examine vendor evidence and contract terms
Ask vendors for documentation specific to the service and proposed configuration, not just general product assurances. Request information on the model and service components, versioning and change notices, data flows, retention and deletion, training use, subprocessors, incident reporting, security and accessibility evidence, known limitations, evaluation methods, and support responsibilities.
Procurement and counsel should address data rights and protection, permitted use, access for audit and testing, incident response, service continuity, and exit or deletion arrangements. The details must be tailored by agency officials; this checklist is not a model contract or a substitute for review.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesIn its April 2026 review of 13 AI acquisitions at the Department of Defense, Department of Homeland Security, GSA, and Department of Veterans Affairs, GAO reported challenges obtaining AI technical expertise and understanding AI-related costs. It also highlighted testing requirements and data-rights terms among acquisition lessons, and recommended systematic collection and sharing of lessons learned. Those findings describe the acquisitions GAO reviewed, not a census or failure rate for all government purchasing.
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Before operational use, set boundaries that staff can follow: what the assistant may do, what requires review, what is prohibited, and how a worker corrects or escalates an output. For consequential work, preserve a meaningful human decision process rather than treating review as a sign-off step.
Set a monitoring plan that tracks quality, exceptions, incidents, changes in input data or service behavior, and effects on users. Assign an owner with authority to investigate problems and pause use. Reassess when the model, vendor terms, integration, policy, or workflow changes; results from an earlier configuration do not automatically establish performance after a material change. GAO’s framework explicitly includes monitoring, while GSA’s active 2026 directive calls for measurement and evaluation of use cases, particularly high-impact AI.
What Oregon’s rules show—and what they do not
Oregon provides a concrete state example, not a rule for other jurisdictions. Its Enterprise Information Services page says the statewide Responsible AI Usage Policy covers generative and agentic AI used for state business by executive-branch agencies, boards, and commissions. The policy describes governance, risk management, human responsibility, transparency, workforce AI literacy, and monitoring as goals. Oregon directs agencies to maintain AI adoption plans and submit proposed new uses for risk evaluation and approval through the state IT investment process. It also says new AI features in existing software require review and approval before use.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchFor Oregon’s covered general generative AI tools, the state says Microsoft Copilot Chat is recommended and approved for general employee use; other tools require separate review. The policy permits only Level 1 “Published” and Level 2 “Limited” data in those tools, and prohibits Level 3 “Restricted,” Level 4 “Critical,” and regulated data. Oregon says prompts and responses that document state business or support decisions are generally public records subject to normal retention rules. Its FAQ states: “AI output must always be reviewed by a human and must not be the sole basis for official decisions or statements.” These are Oregon-specific directions; they do not establish that the same product, data limits, or review rules apply elsewhere.
Why a repeatable evaluation matters
GAO reported that generative AI use cases in the inventories it reviewed rose from 32 in 2023 to 282 in 2024—about a nine-fold increase. Its review involved inventories from 11 agencies and interviews or challenge analysis involving 12 selected agencies. GAO also reported policy, staffing, budget, and pace-of-change challenges. The figures describe those reviewed federal inventories, not every government body.
The practical implication is to make evaluation repeatable: keep the test cases and decision record, name an accountable owner, and connect initial approval to monitoring and change control. For any particular deployment, the agency must still determine its applicable accessibility conformance test, security authorization, records schedule, privacy assessment, and contract requirements with its responsible officials.
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