Six frameworks I use to put AI agents into production. From the floor, not the demo. I run AI delivery in regulated operations — this is how I think about it.
Ramp's AI Index: paid AI adoption hit 55.9%, but token spend has been flat for two months — and 80% of OpenAI and Anthropic spend comes from 1% of firms. Growth is no longer new logos. It is depth. Price the completed task, not the token.
A leaked EU Digital Fairness Act draft would regulate AI shopping agents: user-chosen purchase approval, sponsored suggestions disclosed. It lands tomorrow. The takeaway for ops: when your agent can spend money, someone else's rules decide how.
The Agency Gap: most orgs do not have a knowledge problem, they have an execution problem. They know what AI can do. They cannot run it at scale. The gap lives in governance and handoffs — not in the model.
Snowflake's bet: agents fail in production because they lack business context — 55% of executives say so. So it is giving every agent the same approved definitions and one gateway for every tool call. The control plane is the product. The model is just the engine.