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Before signing with an AI agent vendor, define the job the agent must do, test it on representative work, trace its data and permissions, assess the supplier and its dependencies, and put suitable safeguards and cost controls into the deal. Compare vendors against the same criteria—not against the polish of a demo—and make the decision fit the consequences of failure in your particular workflow.
1. Define what the agent is being hired to do
Start with the workflow, not a product category or a vendor’s preferred use case. Write down the problem, who will use the system, which tasks it may perform, what information and systems it will need, and what must stay under human control. Set measurable pilot criteria before you see a demo: what counts as an acceptable result, which errors are tolerable, and what outcome means the system should not proceed to production.
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Include the expected value and the cost of a mistake. A drafting assistant and an agent that changes business records or sends messages do not have the same risk tolerance. GSA’s procurement guidance is written for federal agencies, but its advice to focus on mission requirements rather than a particular technology or vendor is a useful starting point for other buyers too: GSA Buy AI.
2. How should you test a vendor’s claims?
Use the same bounded pilot for every finalist
Ask shortlisted vendors to complete representative tasks under comparable conditions. Use a sandbox or limited pilot before committing to a large-scale deployment; GSA recommends testbeds, sandboxes, or pilot programs for evaluation. Keep the user group and data scope controlled, and agree in advance on how results will be judged.
#1 Best Overall
Record whether each task was completed correctly, what failed, how much human review was needed, and whether the agent escalated appropriately. Include ordinary cases as well as realistic exceptions: incomplete source material, unavailable integrations, or a request outside the agent’s defined role. For consequential actions, verify the review and approval point rather than treating a plausible-looking answer as proof that the action is safe.
Make the pass/fail decision explicit
Compare observed results with the acceptance criteria from your use-case brief. Document known failure modes, required human checks, and any workflow changes needed to use the product. A demo can show that a feature exists; a bounded pilot helps establish whether the proposed configuration is suitable for your work. No general procurement framework establishes that a specific vendor or product will pass your test.
3. What data will the agent and its providers touch?
Ask the vendor to map the flow of information through the agent, model provider, connected tools, integrations, logs, and subprocessors. For each stage, establish what data is processed and who can access it. GSA’s buyer guidance specifically raises data flow, protection, storage, and allowable data types; NIST’s Generative AI Profile includes privacy, security, intellectual-property, and third-party risks in supplier due diligence: GSA Buy AI and NIST Generative AI Profile.
- Which prompts, uploaded files, outputs, logs, and connected-system data are processed?
- Where is each category stored, for how long, and under what protection?
- Can customer data be used for other purposes, including model improvement, and how is that use controlled?
- Which subprocessors or other service providers can access the data, and how are relevant changes communicated?
- What deletion, export, and retention arrangements apply when the service ends?
- Which data types are permitted or prohibited, and how are those rules enforced in the product and operating process?
Do not rely on broad assurances alone. Request answers that match the proposed product configuration, your data types, and the contract being offered. Whether a particular answer is adequate depends on your organization’s obligations and risk tolerance.
4. How do you assess the vendor and its supply chain?
An AI agent may rely on several providers and software components, so evaluate the supplier as part of a chain rather than as a standalone application. Request current, scope-specific evidence about security practices, relevant dependencies, vulnerability and incident handling, resilience, and how material changes are disclosed. Map that evidence to the systems and data in your use case instead of treating a general assurance or badge as a complete answer.
NIST SP 1326, published July 8, 2026, organizes ICT supplier due diligence around foreign ownership, control, or influence; provenance; resilience; foundational cyber practices; and supply-chain tiers. It is a structure for asking questions, not a certification of any vendor. See NIST SP 1326. NIST’s Generative AI Profile also recommends assessing and monitoring third-party risks, including privacy, security, and intellectual property concerns.
5. What authority will the agent have?
If the agent can invoke tools, update records, send communications, or act through connected accounts, determine exactly how that authority is represented and controlled. NIST NCCoE identifies identity, authorization, and governance as important considerations for agents capable of autonomous tasks: NIST NCCoE Agentic AI Identity and Authorization.
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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 glitches- Which identity does the agent use when it accesses a connected system?
- Can permissions be limited to the specific task and resources required?
- Which actions require human approval, and can that approval be configured by action or risk?
- What activity is logged, and can your team review it?
- How quickly can an administrator suspend or revoke the agent’s access?
- What happens if an instruction is unexpected or a connected tool is unavailable?
Verify the answers with both the vendor and your security or IT team. A capability description is not evidence that controls are enabled in your proposed setup; test approval, logging, and revocation in the pilot where feasible.
6. What should the contract and operating plan cover?
Translate the agreed operating model into contract and service terms where appropriate. Depending on the use case, discuss data use and handling, security and privacy expectations, intellectual-property concerns, access to relevant evaluation information, change notification, incident communication, and service exit. NIST recommends contract clauses that let an organization evaluate third-party generative-AI processes and standards. UK government procurement guidance likewise addresses data sharing and carrying relevant requirements into contract terms: NIST Generative AI Profile and UK AI procurement guidance.
Those sources inform procurement; they do not determine what a specific contract must say. Requirements vary by jurisdiction, sector, data, and intended use. Have counsel adapt proposed terms to the deal, and avoid relying on a verbal assurance for a commitment that matters to your decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. How can you keep usage costs and oversight in bounds?
Estimate consumption using pilot usage and plausible production volumes, then agree on a budget, alerts, and limits that fit your billing model. GSA notes that AI services are often billed like SaaS and advises buyers to set usage limits and review consumption regularly: GSA Buy AI. Revisit the estimate if the number of users, task volume, model configuration, or connected workflows changes.
Before launch, assign owners for monitoring results, reassessing risk, approving new use cases, reviewing vendor changes, and addressing incidents. A purchase decision does not settle those responsibilities for the life of the service.
Best Value
How should you compare two or more vendors?
Use a shared scorecard and the same pilot conditions for each finalist. Weight criteria according to the harm or disruption a failure could cause in the planned workflow; a low cost or strong demo should not outweigh a material gap in a control your use case requires.
| Comparison axis | What to assess |
|---|---|
| Use-case performance | Results on representative tasks, failure modes, escalation, and human review required under comparable test conditions. |
| Data handling | Data access, storage location, retention and deletion, protection, permitted uses, and subprocessors. |
| Supplier and dependency risk | Scope-specific evidence for security, provenance, resilience, third parties, and material-change handling. |
| Agent authority | Identity, permission scope, approval points, activity records, and access revocation. |
| Assurance and contract | Whether your organization can assess relevant practices and whether material operating expectations are documented. |
| Implementation and cost | Integration effort and expected usage cost, including the controls for monitoring and limiting consumption. |
This scorecard is a practical synthesis of procurement and risk guidance, not a published ranking or a substitute for your organization’s own assessment. GSA guidance is aimed at federal agencies, UK guidance applies in its procurement context, and NIST’s AI Risk Management Framework is voluntary; applicable legal and regulatory duties depend on your jurisdiction and use case.
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