A design walkthrough of how Collection Autofill — a spreadsheet of AI-computed cells — stays fast, fair, and observable from a hundred rows to ten million.

An agent that remembers across sessions can keep its memory as curated markdown files, as an auto-mined structured store, or as trained experience. I measured all of them: files against a structured store under one fixed model, a store-only head-to-head across the structured lineages, and an experience bank on the agentic benchmarks where the state of the art trains memory into the weights.

An agent run is stochastic gradient descent over solution space, and steering is how the true objective enters the loop. An optimization view and a Bayesian view that ground the industry ladder — prompt, context, harness, loop engineering — in foundation theory, and explain when a big model steering a small one beats distillation, and when it does not.

One MacBook Pro M4 Max, an open-source MoE, and a hands-on exercise in optimizing it for inference: why the thing that collapses LLM throughput by 100× is prefill, not decode, and the two prefill-side fixes that got it back.