LLMs, Assemble! Why One Model Is Not Enough

Get a concerning diagnosis, and the first thing anyone tells you is to get a second opinion. Ship code to production, and the person who wrote it isn't the one approving the pull request. We've spent decades baking "two heads are better than one" into how we work, because a single perspective misses things, and those misses have a way of compounding. Yet somehow, when it comes to AI, we throw all of that out. We pick a model, usually based on whichever lab we happen to vibe with, and ride it from prompt to production. One model plans the work. The same model writes the code. The same model reviews it. The same model decides whether it's done. It's the equivalent of asking a developer to peer-review their own pull request and trusting the result. In this talk we'll explore multi-model pipelines. We'll look at where single-model loops quietly break down, what changes when you stop treating "which model" as a one-time decision, and how the right composition can land you better results and a smaller bill at the same time. You'll leave with a clearer picture of how to compose models the way you'd compose a team, and why the future of AI-assisted work probably doesn't belong to any single vendor.