A sequenced framework for taking an agentic AI deployment from pilot to production-governed. Not a waterfall — each gate answers one question, and the gate answer determines whether the next stage starts.

Reading this as a tool? The interactive version lives at the AI readiness checklist — it scores your team against these gates and returns a gate-by-gate verdict. This page is the long-form framework reference behind it.

Gate 1 — One named workflow

Before any vendor contact. Pick a single workflow with a named owner and a documented failure mode. "Improve customer service" is not a workflow. "Route incoming tier-2 tickets to the right queue based on issue-type classification" is. If you can't name it in one sentence, go back.

Gate 2 — Evidence the pattern works somewhere

Published primary-source evidence (vendor case study with customer permission, analyst report, peer-reviewed paper) that the same workflow pattern has shipped elsewhere at a comparable scale. Not "AI can do X" — "X has been done, by a named org, using a named vendor, at N scale, for Y months." If you cannot find one, you are the reference case, which is a different risk profile.

Gate 3 — Non-human identity and data scope

The agent runs under its own identity, with explicit scope-limited access to only the data sources the workflow needs. This is the Task 3 of every procurement RFP. If the vendor's default deployment pattern is "use a human employee's credentials", fail the gate.

Gate 4 — Break-even on paper

Per-action economics work even at 2× the current vendor price, at realistic volume, with realistic substitution against a cheaper tier. If the case only works at list price and full adoption, it's fragile. See the GPT-5 Pro ROI calculator for the pricing-tier version of this gate.

Gate 5 — Governance charter names the runtime

The agent charter (not the general AI governance policy — the agent-specific charter) explicitly names: who approves changes, what the MTTD target is, what the kill-switch is, what the correction process is when the agent is wrong in production. Most charters predate the in-the-wild attack evidence of Q1 2026 — see AM-013 on the Q1 2026 enterprise reality check.

When each gate should open

Gate 1 before any vendor demo.
Gate 2 before any pilot commitment.
Gates 3–4 before any production data touches the agent.
Gate 5 before any external user or customer touches the agent.

Agentic AI implementation roadmap — the five gates