Buying enterprise software has always required careful evaluation. But AI agents introduce a category of procurement risk that traditional SaaS evaluation frameworks were not designed to handle. When the software you are purchasing can autonomously take actions in your business systems, process sensitive customer data, generate customer-facing communications, and make decisions that carry legal and reputational weight — the due diligence requirements are fundamentally different.
This checklist is drawn from procurement best practices at organizations that have successfully deployed enterprise AI agents at scale — and from post-mortems of deployments that went wrong. Use it as a structured evaluation framework when assessing any AI agent vendor for enterprise purchase.
For a deeper evaluation framework, see our AI Vendor Selection Guide and our How to Evaluate AI Agents article. For a downloadable version with scoring rubrics, see our Enterprise AI Agent Evaluation Guide.
Why AI Procurement Is Different from Traditional SaaS Buying
Traditional SaaS procurement focuses on features, pricing, integrations, and vendor stability. These matter for AI agents too — but AI agents add several evaluation dimensions that have no equivalent in traditional software purchases:
Non-determinism. AI agents produce different outputs for the same input. You cannot fully specify the software's behavior through feature requirements. The evaluation must include behavioral testing and quality assessment, not just feature checklists.
Data privacy at inference time. Every interaction with an AI agent involves sending data to a model — either the vendor's own or a third-party API. You need to understand precisely what data leaves your environment, where it goes, and what happens to it.
Autonomous action risk. Agentic AI can take actions without human confirmation. The risk surface is not just bad outputs — it is unauthorized actions in connected systems. Your procurement evaluation must include tool permission scope and human-in-the-loop controls.
Rapidly evolving landscape. AI vendor capabilities, pricing, and business stability are changing faster than any other software category. Your evaluation framework needs to include a "future-proofing" component and appropriate contractual protections against provider changes.
Common Procurement Pitfall
Many enterprise AI purchases are initially made by individual teams or departments bypassing IT and procurement. By the time security and legal review the deployed system, sensitive data has already been processed under unfavorable terms. Establish AI procurement policy before business units make individual purchases — not after.
Related Guide
AI Agent RFP Template — Free Download
A complete RFP template for AI agent procurement — structured questions for vendors, evaluation scoring criteria, and contract negotiation guidance.
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Category 1: Data Handling and Security
Data Handling Questions
Is my data used to train or fine-tune the AI model? Require a contractual zero-training commitment on enterprise plans. Verify this covers conversation history, uploaded documents, and all data processed by the agent.
What foundation model does the vendor use? If a third-party LLM (OpenAI, Anthropic, Google) is used, what are the data handling terms with that provider? Does the vendor have a zero-data-retention API agreement?
Where is data stored geographically? Confirm data residency options for EU personal data under GDPR. Verify whether cloud infrastructure is in your required regions.
What data does the agent process and retain? Map all data flows: what is sent to the model, what is stored in the knowledge base, what is logged, and for how long.
How is memory and conversation history stored and secured? Verify tenant isolation in multi-tenant deployments. Ask specifically: can one customer's memory appear in another customer's agent responses?
What encryption is applied to data at rest and in transit? Enterprise standard is AES-256 at rest and TLS 1.2+ in transit. Verify key management practices.
What is the data retention policy? How long are conversation logs, uploaded documents, and user data retained? Can you configure retention periods?
Who in the vendor organization can access your data? What access controls and employee background check requirements apply to vendor staff who could access customer data?
Category 2: Compliance and Certifications
Compliance Questions
Does the vendor have SOC 2 Type II (not just Type I)? Type II is the enterprise standard — it covers an audit period, not just a point-in-time assessment. Request the current SOC 2 Type II report.
Will the vendor sign a Data Processing Agreement (DPA)? A DPA is legally required for processing EU personal data under GDPR. Vendors who refuse or delay DPA signing are a red flag.
Is the vendor compliant with applicable industry regulations? HIPAA BAA for healthcare, PCI DSS for payment data, FedRAMP for government, ISO 27001 for security-sensitive deployments.
How does the vendor handle data subject rights requests? Verify the vendor can support GDPR right-to-erasure requests within 30 days, CCPA deletion requests, and data access requests for any personal data stored in the system.
What is the vendor's vulnerability disclosure and incident response process? How quickly are security incidents disclosed? What SLA exists for critical vulnerability patching?
Has the vendor conducted third-party penetration testing? Request the most recent external pen test summary and evidence of remediation for high/critical findings.
Category 3: Pricing and True TCO
Pricing and TCO Questions
What is the total cost including usage overages? Many AI agents have per-user base pricing plus usage-based charges for tokens, API calls, or AI interactions. Model your expected usage volume and get cost estimates at 1x, 2x, and 3x projected volume.
Are there minimum commitments or volume discounts? Annual vs. monthly pricing. Minimum seat commitments. Volume tiers. Negotiate based on your projected usage.
What are the implementation and integration costs? Professional services fees, integration development time, API connection costs, and ongoing customization/prompt engineering costs.
What is the true cost of switching away? Data export costs, transition service availability, minimum notice period for cancellation, and cost of rebuilding integrations on a new platform.
How has pricing changed historically? AI vendors have raised prices significantly as the market matures. Ask for pricing history and negotiate contractual price protection clauses.
What features are gated behind premium tiers? Map which features from your requirements matrix are in which pricing tier. Many essential enterprise features (SSO, audit logging, advanced security) are only in the most expensive plans.
Category 4: SLAs and Reliability
SLA and Reliability Questions
What is the guaranteed uptime SLA? Enterprise minimum is 99.9% (8.7 hours downtime/year). Leading vendors offer 99.95%. Verify the SLA covers the full service stack, not just the application layer.
What are the financial remedies for SLA violations? Service credits that are automatically applied, not credits you have to request. Ask about the history of SLA violations and whether credits were actually issued.
What is the AI model response latency SLA? For customer-facing agents, response latency directly affects customer experience. Verify p50, p95, and p99 latency benchmarks for your expected query types.
How does the vendor communicate service incidents? Status page availability, incident notification process, and how quickly root cause analysis is provided after outages.
What are the maintenance windows? How frequently are planned maintenance windows scheduled, how much advance notice is provided, and is maintenance performed during off-peak hours for your region?
Category 5: Integration and Technical Fit
Integration Questions
What native integrations are available for your tech stack? List the specific systems you need to connect (CRM, ITSM, communication platform, data warehouse) and verify native vs. API-only integration.
What API documentation and rate limits apply? Review API documentation quality, rate limit tiers, and available SDKs before committing to a custom integration.
What are the SSO and identity management options? Enterprise deployments require SAML 2.0 SSO integration with your identity provider. Verify availability and whether it is included in your pricing tier.
What audit logging is available? For compliance purposes, you need auditable records of all AI actions and decisions. Verify log completeness, retention period, and export capability.
What are the admin and access control capabilities? Role-based access control, user provisioning (SCIM), and admin policy controls are enterprise requirements. Verify they are available in your pricing tier.
Category 6: Contract and Exit Rights
Contract Questions
What data portability is guaranteed if you leave? You should be able to export all your data (conversation history, knowledge base content, configuration) in a standard format within 30 days of contract termination.
What is the notice period for termination? Verify the cancellation notice period and whether there are any early termination fees. Annual contracts should have clear mid-term exit provisions for material breach.
What data deletion certification is provided on exit? Require written confirmation of deletion of all your data within 30 days of contract end, with certification from the vendor's security team.
Does the contract include IP protection for your data? You own your data. The contract should explicitly state the vendor cannot use your data, prompts, or outputs for any purpose other than providing the contracted service.
What happens to your configuration if the vendor changes its product significantly? "Material change" provisions that allow you to exit if the vendor makes product changes that materially affect your use case.
Category 7: Vendor Stability and Risk
Vendor Risk Questions
What is the vendor's funding and financial runway? Many AI vendors are VC-funded startups with limited profitability. For mission-critical deployments, assess funding status, burn rate, and path to sustainability.
What is the vendor's foundation model dependency? If the vendor relies on a single LLM provider (e.g., OpenAI API), assess the risk of that provider changing API terms, raising prices, or discontinuing access.
What is the vendor's product roadmap commitment? Get written commitments on specific features in the roadmap if they are part of your purchase decision. Verbal roadmap promises are not contractually binding.
What are the reference customers in your industry? Request references from customers in your industry who have been live for at least 12 months. Specific questions: have there been pricing changes, how was support during incidents, and would they choose the vendor again?
What acquisition/change of control protections exist? If the vendor is acquired or merges, what protections does your contract provide? Can you terminate if the acquirer is a competitor or if service levels decline post-acquisition?
Next Step
Compare the Top AI Agents Side by Side
Use our interactive comparison tool to evaluate AI agents across all the dimensions in this checklist — security, pricing, integrations, and compliance certifications.
Frequently Asked Questions
How long should the AI agent procurement process take?
A thorough enterprise AI agent procurement typically takes 6–12 weeks from initial evaluation to signed contract. Allow 2–3 weeks for initial vendor demos and RFP responses, 2–4 weeks for security review and data handling assessment, 1–2 weeks for legal review of contract terms, and 1–2 weeks for internal approval processes. For mission-critical or high-risk deployments, add 2–4 weeks for a proof of concept evaluation against your actual use case.
What should I do if a vendor refuses to provide SOC 2 documentation?
A refusal to share SOC 2 Type II documentation under NDA is a significant red flag. It typically means the vendor either does not have the certification (most legitimate enterprise-ready vendors do), or the report contains findings they do not want you to see. You can work around a lack of SOC 2 with an alternative security questionnaire and your own security assessment, but this requires significantly more due diligence work on your part. For high-risk deployments, SOC 2 Type II should be a hard requirement rather than a preference.
How do I evaluate AI agent quality during a proof of concept?
Design your POC around specific, representative test cases from your actual use case — not the examples the vendor suggests. Measure: accuracy on questions with known correct answers, hallucination rate on out-of-distribution queries, behavior on adversarial inputs (see our red teaming guide), response latency under expected load, and qualitative quality assessment from the team who will use it daily. Run the same test set against each vendor shortlist to enable direct comparison.
Can I negotiate better terms than the standard enterprise contract?
Yes, for almost every AI vendor. Key negotiation points include: zero-training data commitments (many default contracts reserve training rights that enterprise plans exclude), price protection clauses, data portability guarantees, enhanced SLA commitments, expanded liability caps for data incidents, and specific feature commitments from the roadmap. Procurement leverage is highest before signing for multi-year commitments or large seat counts. Engage a technology lawyer to review the contract before signing.
