Vendor evaluation resource

AI Agent Vendor Evaluation Checklist

Preparing an AI agent RFP? Use these eight checks to compare proposals on business fit, integrations, testing, human control, and ongoing costs.

Define the workflow and success criteria. Describe the trigger, users, current steps, volume, exceptions, and desired result. Record a baseline, such as handling time or manual steps, and ask each vendor how improvement will be measured.
Document data readiness and access. List source systems, example inputs, content owners, permissions, and update frequency. Ask what cleanup or preparation is needed, who will do it, and how those tasks affect the scope.
Check the integration plan. Identify the systems the agent must read from or update and the interfaces available. Ask vendors to explain permission requirements, approval steps, how duplicate actions are prevented, and recovery when a connected system is unavailable.
Specify human control and exception handling. Define actions the AI may take, actions that need approval, and cases that must be routed to a person. Ask how users can review, stop, or correct work and what happens when information is incomplete.
Agree on evaluation and acceptance. Provide representative examples and expected results, including difficult cases. Ask how correctness, unsupported answers, action failures, and access limits will be tested, and who decides whether the system is ready for use.
Review data handling requirements. Ask which providers receive data, where processing and storage occur, what is retained, and which access or deletion controls are available. Have the relevant people in your organization check the proposal against your requirements.
Compare the full commercial scope. Separate discovery, development, integrations, testing, and rollout from ongoing model, hosting, and support costs. Ask for assumptions, exclusions, dependencies, and the process for agreeing changes to scope or schedule.
Define responsibilities after launch. Agree who monitors quality, handles incidents, updates source content, and tests model changes. Document the arrangements for code, accounts, documentation, knowledge transfer, and ending the engagement before selecting a vendor.

What would you like AI to do for your business?

Tell us about the challenge, your existing systems, and the result you want. We’ll review your inquiry and get back to you to discuss fit and next steps.

External partner or in-house team?

AI Agent Vendor Evaluation Checklist

Define the workflow and success criteria. Describe the trigger, users, current steps, volume, exceptions, and desired result. Record a baseline, such as handling time or manual steps, and ask each vendor how improvement will be measured.

Document data readiness and access. List source systems, example inputs, content owners, permissions, and update frequency. Ask what cleanup or preparation is needed, who will do it, and how those tasks affect the scope.

Check the integration plan. Identify the systems the agent must read from or update and the interfaces available. Ask vendors to explain permission requirements, approval steps, how duplicate actions are prevented, and recovery when a connected system is unavailable.

Specify human control and exception handling. Define actions the AI may take, actions that need approval, and cases that must be routed to a person. Ask how users can review, stop, or correct work and what happens when information is incomplete.

Agree on evaluation and acceptance. Provide representative examples and expected results, including difficult cases. Ask how correctness, unsupported answers, action failures, and access limits will be tested, and who decides whether the system is ready for use.

Review data handling requirements. Ask which providers receive data, where processing and storage occur, what is retained, and which access or deletion controls are available. Have the relevant people in your organization check the proposal against your requirements.

Compare the full commercial scope. Separate discovery, development, integrations, testing, and rollout from ongoing model, hosting, and support costs. Ask for assumptions, exclusions, dependencies, and the process for agreeing changes to scope or schedule.

Define responsibilities after launch. Agree who monitors quality, handles incidents, updates source content, and tests model changes. Document the arrangements for code, accounts, documentation, knowledge transfer, and ending the engagement before selecting a vendor.

What would you like AI to do for your business?