Why agent procurement differs from standard AI buying
AI agents combine planning and reasoning loops with tool use and external connectors. That design expands the attack and data-exfiltration surface beyond single-turn model calls. Procurement cannot treat agents as ordinary SaaS or as isolated chat interfaces.
Enterprise buyers need criteria that address autonomous behavior: what the agent is allowed to do, how those limits are enforced at runtime, how identity and credentials are scoped, and how every material action is recorded. Marketing language about “safe” or “aligned” behavior is not a substitute for technical evidence.
Official guidance supports this approach. NIST AI RMF profiles encourage mapping autonomous-system risks to Govern, Map, Measure, and Manage functions for acquisition decisions. OWASP guidance for LLM and agentic applications highlights excessive agency, sensitive information disclosure, and insufficient logging as core supplier risks. CISA and partner secure AI procurement guidance stresses verification of supply-chain security, model provenance, and runtime monitoring beyond self-attested claims. Where use cases involve high autonomy and impact, frameworks such as the EU AI Act can impose risk-management, transparency, and human-oversight obligations that must be reflected in contracts.
Vendor claims that require technical validation
Questionnaires alone are insufficient for agent security evaluation. Claims of safe, governed, or aligned agent behavior need independent technical validation during procurement.
Require evidence of runtime policy enforcement rather than configuration screenshots alone. Ask vendors to demonstrate how policy engines block unauthorized tool use, how sandboxes contain side effects, and how human approval gates behave under failure or timeout conditions. Red-teaming against excessive agency and sensitive data disclosure should be in scope where contracts allow.
Validate logging fidelity by replaying sample agent sessions and confirming that plans, tool calls, and outcomes appear completely and in exportable form. Incomplete logs leave procurement and security teams unable to investigate agent-initiated actions after the fact.
Inspect identity and permission models directly. Confirm that agents are not forced to share broad human or service identities, that tool scopes default to least privilege, and that credentials can be rotated and revoked through enterprise controls.
For multi-agent or agent-to-agent topologies, assess the added identity and authorization complexity. Prefer architectures that enforce policy outside a fully closed vendor runtime so enterprise gateways and monitoring can remain authoritative. Architecture diagrams, model and provider lineage documentation, known limitations, and safety-evaluation summaries should be part of the package reviewers receive before award.
Validation principle: Treat “safe,” “governed,” and “aligned” as claims that require runtime demonstration, log replay, and identity inspection, not questionnaire answers alone.
Assessing shared responsibility for agent actions and data
Cloud and AI service providers typically document shared responsibility models in which customers retain accountability for identity configuration, data classification, prompt and agent policy, and monitoring of agent actions. Procurement should translate that pattern into explicit contract language for third-party agents.
Allocate platform controls to the vendor: runtime hardening, isolation between tenants, secure defaults for tool marketplaces, availability of audit export, and notification of vulnerabilities that affect the agent platform. Allocate use-case controls to the buyer: which workflows agents may run, which data classes they may touch, which tools are approved, who provides human oversight, and how anomalies are escalated.
Liability and incident response when an agent causes unauthorized actions or data exposure should be negotiated against this split. Ambiguity here is a procurement failure, not only a legal one. High-autonomy use cases may also trigger regulated or high-risk treatment under applicable law, which can require documented human oversight design and conformity evidence from providers and deployers.
Align the AI supplier assessment with existing third-party risk and AI RMF profiles already used by the enterprise. That reduces duplicate questionnaires and ensures agent governance requirements sit inside established vendor-risk processes rather than as a one-off exception.
How to run the evaluation in practice
Translate requirements into a short, testable procurement path that security, architecture, and legal can execute together. Keep the path focused on evidence: runtime demos under production-like conditions, exportable audit samples, identity integration proofs, written data-boundary terms, and contract language that mirrors the shared-responsibility split.
Buyer questions to drive the AI supplier assessment
Use the following questions in RFPs, security reviews, and vendor workshops. Prefer answers that include artifacts you can inspect over narrative assurances.
- Runtime limits What technical evidence shows enforceable limits on tools, data access, and external side effects under production-like conditions?
- Identity and credentials How are agent identities provisioned, rotated, and scoped, and how do they integrate with enterprise IdP and secrets management?
- Audit export What immutable artifacts are produced for each plan, tool invocation, and outcome, and how are they delivered to customer systems?
- Data boundary Which prompts, memories, retrieved documents, and logs leave customer control, for what purpose, and under what retention and residency terms?
- Incident allocation How are liability, investigation access, and response roles allocated when an agent causes unauthorized action or exposure?
- Autonomy and oversight Where must human approval occur, how are high-impact actions gated, and what documentation supports regulated or high-risk use cases?