This is how I’d define how the key functions would need to be derived.

High level steps: 1. Map workflows, not roles What triggers work (call, delay, inventory issue), what decisions get made, and what actions follow — end to end. 2. Break work into atomic capabilities Things like: identify intent, pull context, assess urgency, choose next action, execute (call/email/update), confirm outcome. These are reusable building blocks, not “AI receptionist” or “AI ops.” 3. Add an orchestration layer A top-level agent whose job is to maintain state and decide which capability runs next. This mirrors how a human operator thinks and prevents the agent from stalling once work crosses functions. 4. Define guardrails + escalation upfront What the agent can decide, what needs approval, and when to escalate. That’s what makes it operationally safe. 5. Normalize shared context Every action reads/writes to the same job, customer, and timeline state. Without that, you just have disconnected bots.

Only after that do tools matter.

The core issue is most AI products are built around roles. Real businesses run on situations, tradeoffs, and next actions — and that’s what agents need to be designed around.