Before signing with an AI agent development company, ask five things: whether an agent is the right fit for the task, what evidence supports the team’s capability, how data and third-party components will be handled, how acceptance will be measured, and who owns support and exit. Ask for specific answers and evidence tied to your use case—not broad assurances or a polished demo.
1. Have we defined the task, users, data, and risks clearly enough to know an agent is the right approach?
Ask the company to restate the business outcome in measurable terms and describe who will use the system, who could be affected by it, what information it will access, and what actions it may take. This tests whether the proposal is grounded in your operating needs rather than in the novelty of using an agent.
As an Amazon Associate I earn from qualifying purchases.
Ask what the system must not do, which actions require human approval, and how it should respond when information is missing or confidence is low. Put consequential actions—such as changing records, approving transactions, or communicating externally—behind clearly defined permissions and review points.
Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesNIST’s 2021 AI procurement guidance recommends assessing potential impacts before acquisition and considering whether AI or machine learning is necessary to achieve the desired outcome. The OECD’s 2026 guidance on responsible AI due diligence likewise calls on enterprises to identify actual and potential adverse impacts and consider how a system will be used and overseen. Use those questions to sharpen your requirements before comparing proposals.
2. Can the team show it has the skills and process this use case requires?
Ask who will do the work and what relevant experience they bring in both your domain and the technical areas the project needs. A capable answer should explain how the team will design, validate, test, govern, and maintain this particular system—not simply list general AI experience.
Request evidence that you can inspect, such as relevant project examples, evaluation plans, governance materials, test procedures, or a description of the proposed testing environment. Evaluate the evidence against the task and its risks; an impressive demo or broad capability claim does not establish that the team can deliver your system safely and reliably.
Ask who is accountable for validation and audit functions, how maintenance will be handled, and what skills will remain available after delivery. UK government procurement guidance highlights maintenance capability and planning for end of life; see its AI procurement guidelines.
Free tools Windows power users keep installed
One-click scans. No signup required.
3. Who controls the data, project IP, and third-party components?
Ask the company to identify the data it needs, why it needs each category, and what permission or lawful basis supports its use. Establish who can access the data, how confidentiality and integrity will be protected, how long information will be retained, and how it will be returned or deleted when the project or contract ends.
Get a clear answer on whether your data, prompts, outputs, or other project information may be used to train or improve models. Ask the vendor to identify external models, tools, and services in the proposed system, explain what information they receive, and disclose relevant limits or dependencies. NIST procurement guidance addresses consent for data sources, external models and solutions, and confidentiality and integrity during development; OECD guidance addresses data governance and privacy across the AI system lifecycle.
Clarify in the contract who owns or may use project deliverables, code, documentation, configurations, and data. Specify what must be provided to you at termination, and what must be deleted. These are negotiation points to resolve for your circumstances, not universal legal terms; seek local legal advice where needed.
4. What tests will determine whether the system is accepted?
Agree on acceptance criteria before work begins. Ask the company to define representative test cases, measurable outcome thresholds, unacceptable failure modes, robustness and security checks, human-review requirements, and who has authority to sign off. NIST’s procurement guidance specifically asks whether acceptance criteria set appropriate accuracy levels.
Require evaluation methods and results, not only a live demonstration. A demo can show that a system works on selected inputs; it does not establish how it performs across representative cases or how it behaves when conditions change. OECD guidance recommends reviewing test and evaluation information, including data suitability and system trustworthiness, and identifying robustness and security risks.
Make the test set reflect the real users, information, edge cases, and risks identified during scoping. Define how failures will be recorded, what happens when a threshold is missed, and whether fixes must be retested before acceptance. Without agreed measures, the parties may have different ideas of what “done” means.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Who is responsible after launch—and when the relationship ends?
Ask who approves releases, monitors the system, reviews logs, and responds to incidents. Define what access you will have to relevant logs and audit information, how quickly the vendor must notify you of incidents, and who investigates and coordinates a response.
Clarify how changes to models, tools, data sources, or other dependencies will be handled. Ask what support and maintenance are included, how users and administrators will be trained, and how changes or failures can affect agreed acceptance criteria. NIST’s procurement lifecycle includes deployment and maintenance considerations, while its AI RMF Playbook recommends attention to third-party system testing and auditability.
Plan the end of the relationship as deliberately as the launch. Ask how the vendor will help transition the system, what documentation and artifacts will be handed over, and how your data will be returned or deleted. UK government guidance also emphasizes end-of-life planning.
How to compare the answers
Use the same questions with each prospective company and compare the evidence, not just the confidence of its presentation. Look for clear, use-case-specific answers across these areas:
- Relevant domain and engineering capability, supported by inspectable examples or methods.
- Transparency about data use, external models, tools, and limitations.
- Specific, reproducible testing and acceptance criteria.
- Practical security, privacy, governance, and human-oversight measures.
- Defined post-launch monitoring, maintenance, and incident responsibilities.
- Clear ownership, transition, and exit arrangements.
The OECD guidance describes due diligence as an ongoing process: embed responsible conduct in policies and management systems; identify and assess impacts; cease, prevent, or mitigate them; track implementation and results; communicate actions; and provide for or cooperate in remediation where appropriate. That lifecycle perspective is useful when assessing whether a supplier’s proposal addresses more than initial delivery.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
Quick wins for a faster PC:
Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →
