Five years ago, "which AI model should I use" had a one-line answer. Today there are at least 12 frontier-tier models and the wrong pick will either bankrupt you on tokens or cripple your agent on the tasks that matter most. This is the framework I use when wiring a model into a new agent. Step 1: Define the workload, not the wishlist People pick models based on the leaderboard. That's wrong. What matters is the distribution of tasks your agent runs — which is rarely uniform. Most production agents look like this: 70% trivial calls — formatting, classification, "is this email a calendar invite?", short replies 20% medium calls — summarization, reasoning over a few documents, drafting in your voice 10% hard calls — multi-step planning, debugging, code generation, long-context analysis If you optimize for the 10%, you'll pay 10x more on the 70% you didn't have to. If you optimize for the 70%, your agent will fail visibly the first time it hits a hard task.…