Agentic AI Readiness Question Pack

What it de-risks

• ▸Bound what an agent may do autonomously, and how those limits are enforced technically.
• ▸Demand an evaluation methodology instead of a benchmark screenshot.
• ▸Expose per-run cost behavior before an agent loop meets your invoice.
• ▸Test integration and tool-access boundaries against your least-privilege model.

What is inside

4 corpus modules, at both RFI and RFP depth, in canonical order. Every question ships with what a strong answer looks like, the red flags to watch for, a response-format hint, and a default weight you can tune.

• Integration & interoperability
• Agentic safety & autonomy controls
• AI performance, evaluation & monitoring
• AI cost predictability & FinOps

Sample questions

• Do you provide a public API for core product functionality? If so, provide the URL to the public API reference documentation.

  Why it matters — A public, documented API is the baseline for enterprise integration. Without it, buyers are locked into the vendor UI and cannot automate workflows or integrate with internal systems. The location and quality of reference docs is a fast signal of API maturity.

• Describe how customers configure the set of actions an agent is permitted to take, including whether permissions can be scoped per action type rather than granted as a single bundle.

  Why it matters — All-or-nothing permission models force customers to over-grant authority to agents, expanding blast radius from any single failure or prompt injection. Per-action scoping is a baseline expectation for enterprise agentic deployments. This question screens vendors whose permission model is too coarse for regulated environments.

• Describe your evaluation methodology for the AI capabilities offered, including which evaluations are run pre-release versus continuously, and who designs them.

  Why it matters — Buyers need to know whether quality claims rest on a disciplined, repeatable evaluation process or on ad-hoc internal testing. The structure of the evaluation program signals organizational maturity around AI quality.

• Provide a comprehensive list of every billable unit your platform meters, and describe the metering mechanism for each. Include units such as input/output/cached tokens, embeddings, image/audio units, fine-tuning, tool calls, retrieval operations, and other agent-step actions. For each unit, specify whether it appears on the invoice or is visible only in usage reports.

  Why it matters — AI products bill in heterogeneous units that don't map to traditional license SKUs. Buyers need a complete inventory of billable units to forecast spend. Agentic workflows can multiply costs through chained tool calls and retrievals, so visibility into the cost of each step is critical, not just the top-level request, to avoid significant hidden costs.