The Enterprise AI Agent Deployment Checklist

Our deployment checklist covers identity, tool scope, data handling and recovery. This guide brings together public guidance for enterprise buyers and operators, with source-linked recommendations for evaluation, permissions and incident response rather than claims of product testing or guaranteed security outcomes.

Ownership, scope and governance

NIST describes its AI Risk Management Framework as intended for voluntary use, and notes that its Core actions are not a checklist and are not necessarily an ordered set of steps NIST. The Core outlines govern, map, measure and manage functions, with governance infused throughout NIST AI RMF Core.

Our editorial assessment: before purchase, name an accountable owner, record the intended task boundaries in writing, and confirm how the agent is inventoried and eventually retired. Ask how third-party tool and data supply-chain risks are assessed. Microsoft frames NIST mapping through accountable ownership and explicit trust relationships, which is vendor documentation rather than independent testing Microsoft.

Related reading: related coverage.

Identity, credentials and tool permissions

OWASP frames excessive agency as arising from excessive functionality, excessive permissions or excessive autonomy, and recommends minimum extensions, minimum permissions, execution in the user's context and human approval for high-impact actions OWASP. Its agentic threat reference discusses emerging threats and mitigations OWASP.

Microsoft's documentation recommends unique agent identities, scoped and short-lived tokens, and fresh confirmation for irreversible actions. Copilot Studio documentation describes authentication options, including an unauthenticated mode that allows anyone with a link to chat, and a setting to require sign-in Microsoft. OpenAI's tools documentation catalogues hosted and local tool types with approval behavior OpenAI.

Buyer checklist, our assessment: request a per-tool permission inventory, confirm which actions require approval, and ask how credentials are scoped and expired.

Data, untrusted inputs and prompt injection

OpenAI's agent safety guidance describes prompt injection and private data leakage, recommends passing untrusted input via user messages rather than developer messages, and states that structured outputs and isolation reduce but do not fully remove risk OpenAI. This is vendor guidance.

Our recommendation: treat external documents, email and web content as untrusted, and ask vendors how tool approvals and guardrails are configured. Do not assume any control guarantees prevention.

Related reading: related coverage and related coverage.

Evaluation, tracing and trace privacy

OpenAI's evaluation guidance recommends starting with traces when debugging behavior, then moving to datasets and eval runs for repeatability OpenAI. Its Agents SDK tracing documentation states that tracing is enabled by default, that certain spans may capture sensitive data, and that tracing is unavailable for organizations using APIs under a Zero Data Retention policy OpenAI.

Buyer checklist, our assessment: ask where traces are stored, what sensitive fields are captured by default, and whether redaction happens before export. Vendors describe these behaviors; buyers should verify configuration in their own environment.

Related reading: related coverage.

Stop, recovery and periodic review

OWASP notes that logging, monitoring and rate limiting limit damage rather than prevent excessive agency. Microsoft documents approval gates and per-tool authorization as design controls, and NIST's Core describes ongoing monitoring, periodic review and safe decommissioning as governance outcomes.

Our recommendation: agree kill-switch authority, incident escalation and rollback paths in the contract, and schedule recurring reviews of permissions and tool inventories. Compare that against how other deployers handle change control: related coverage. Our recommendation is not to assume any product prevents every incident; this checklist is editorial synthesis for procurement discussion.