Owner: Department Champion + the person requesting the agent. Input: Someone in the company says "I want an AI agent for X." Sub-steps:
- Find out what they actually do today. Sit with them. Watch the workflow. Take notes.
- Document the current workflow as-is. What systems? What inputs? What decisions? What edge cases? What's the failure mode of the workflow today?
- Identify the pain point. Is it volume? Boredom? Errors? Speed? Cost? Skill bottleneck? You need to know exactly what AI is supposed to relieve.
- Volume check. How many times a week / month does this happen? If <10/month, AI is probably not the answer (rules / templates / API integration may be).
- Apply the "is this even an AI problem?" filter:
- Could a better API integration solve it? → not an AI problem.
- Could a deterministic rule / template solve it? → not an AI problem.
- Does it require probabilistic reasoning over unstructured / ambiguous input? → maybe an AI problem.
- Apply the "is this an agent or just a model call?" filter:
- Does it need to chain actions across systems? → agent.
- Does it need to use tools? → agent.
- Is it a single prompt-response? → not an agent, just an LLM call.
- Briefly estimate value: how many hours saved / errors reduced / dollars saved per month.
- Briefly estimate risk: does it touch PII? Does it make decisions about people? Can it act autonomously?
Output / gate criteria:
- A 1-paragraph workflow description.
- A 1-line value statement.
- A 1-line risk red-flag list.
- A "this is genuinely a candidate for agentic AI" / "no, route elsewhere" recommendation from the Champion.
Decision branches:
- Not a real AI use case → route to the appropriate team (process improvement, IT integration, training) and close.
- Real AI use case → go to Step 2.
Skip-this-step risk: You end up automating a broken workflow at higher speed, or building an agent for something a $50 API call could fix.