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Before signing with an AI agent development company, ask five things: whether an agent is the right fit for the task, whether the team can prove its capability, who controls data and project assets, how acceptance will be measured, and who owns support and exit. Get specific answers in writing. These are diligence prompts, not universal contract clauses or legal advice; requirements depend on your use case and jurisdiction.

1. Have we defined the task, users, data, and risks well enough to know an agent is the right approach?

Ask the company to restate the business outcome in concrete terms, identify who will use or be affected by the system, name its information sources, and describe exactly which actions it may take. A proposal that jumps straight to tools or architecture without establishing these boundaries may be solving the wrong problem.

Define which actions require human review or approval, especially actions with significant consequences for people, finances, or access to services. Also ask whether a simpler non-AI workflow could achieve the intended outcome. NIST recommends assessing impacts before acquisition and considering whether AI or machine learning is necessary for the desired result (NIST AI RMF Playbook). OECD guidance calls for identifying actual and potential adverse impacts and considering how the system will be used and overseen (OECD due diligence guidance for responsible AI).

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A useful answer should produce a shared description of the intended use, affected parties, data inputs, permitted actions, human checkpoints, and foreseeable harms. If you cannot agree on these, it is too early to compare vendors on implementation detail.

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2. Can you show that your team has the skills and process this use case requires?

Ask who will do the work and what relevant experience they bring: domain knowledge, agent and software engineering, evaluation, security, governance, and ongoing maintenance. Request concrete evidence—such as a relevant project example, evaluation plan, testing artifacts, or documented development process—and judge it against your use case rather than accepting general capability claims.

Ask how the team will validate the system in an environment that resembles yours, who reviews the results, how issues are tracked, and what audit functions are available. NIST procurement guidance recommends evaluating supplier capability, including skills, methods, validation, testing, governance, and maintenance (NIST, White Paper: Guidelines for AI Procurement). UK government guidance also emphasizes maintenance capability and planning for end of life (UK government Guidelines for AI Procurement).

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Look for a team that can explain not only what it will build, but how it will discover failures, document decisions, maintain the system, and support an orderly handover.

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3. Who controls the data, project IP, and third-party components?

Ask for an inventory of the data the company needs and a clear explanation of the permissions and lawful basis for using it. Establish who can access the data, for what purposes, how it will be protected, how long it will be retained, and how it will be returned or deleted when the engagement ends. Explicitly ask whether your data, prompts, or outputs may be used to train or improve a model.

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Identify external models, APIs, tools, hosting, and other services on which the solution depends. Ask what information each receives, what contractual or technical limitations apply, and what happens if a provider changes its terms, pricing, capabilities, or availability. NIST procurement guidance highlights consent for data sources, external models and solutions, and confidentiality and integrity during development; OECD guidance addresses data governance, privacy risks, and responsible conduct throughout the AI system lifecycle (see the NIST procurement white paper and OECD guidance).

Set out ownership and usage rights for custom code, configurations, documentation, evaluation materials, and other deliverables. Clarify what the company may retain, what you receive, and which third-party components remain subject to separate terms. Have qualified counsel review data, confidentiality, and intellectual-property provisions for your jurisdiction and business.

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4. What tests will determine whether the system is accepted?

Agree on acceptance criteria before development begins. Define representative test cases drawn from real workflows, measurable thresholds for the outcomes that matter, unacceptable failure modes, and who is authorized to approve release. Include robustness and security checks as well as the points where human review must work as intended.

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Ask the vendor to describe its evaluation method and provide results against those criteria. A polished demonstration is not evidence that the system performs reliably across the cases, edge conditions, and data it will encounter in operation. NIST procurement guidance specifically calls attention to whether acceptance criteria set appropriate accuracy levels; OECD guidance recommends reviewing test and evaluation information, data suitability, trustworthiness, robustness, and security risks (NIST procurement white paper; OECD guidance).

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Make the evidence reproducible enough that both sides can understand what was tested, under what conditions, and what happens when a result falls short. Specify whether defects must be corrected and retested before acceptance.

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5. Who is responsible after launch—and when the relationship ends?

Ask who approves releases, monitors the system, reviews logs, and can access audit records. Agree on how incidents are reported and handled, what support and maintenance the vendor provides, and how changes to models, tools, or other dependencies are assessed before they affect production. Decide how users and operators will be trained.

Plan for transition as well as operation: identify what documentation, data, code, configurations, and other artifacts you will receive; what assistance is available to move to another provider or operate the system independently; and what must be returned or deleted at termination. Ask how the company will handle component changes, service failures, or the system reaching end of life. NIST procurement and risk-management guidance addresses deployment, maintenance, third-party testing, and auditability, while UK procurement guidance highlights end-of-life planning (NIST procurement white paper; NIST AI RMF Playbook; UK government guidance).

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How to compare the answers

Use the same questions with each company, then compare the evidence rather than the confidence of the pitch. A strong proposal should connect its promised outcome to the defined use case, disclose important dependencies and limits, explain how performance will be tested, and name the people and processes responsible after launch.

  • Capability: relevant domain and engineering experience supported by examples or artifacts.
  • Transparency: clear disclosure of data use, external models and tools, and known limitations.
  • Evaluation: specific, reproducible tests tied to acceptance criteria.
  • Risk management: credible privacy, security, governance, and human-oversight practices.
  • Operations and exit: named support responsibilities, maintenance plans, and clear transition and asset-return terms.

Write down unresolved questions and make sure the final agreement reflects the answers that matter to your project. The OECD’s 2026 guidance frames responsible AI due diligence as embedding responsible conduct in management systems, identifying and assessing impacts, preventing or mitigating them, tracking results, communicating actions, and providing for or cooperating in remediation when appropriate (OECD guidance). This is a useful lens for evaluating how seriously a supplier treats responsibility over the system’s lifecycle.

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