The loop is the product, not the model — two talks this month said it from opposite ends

Two things I watched this month say the same thing and I haven't seen anyone connect them. Stanford's CS 153 opening lecture (Anjney Midha) argues Anthropic compounds because of a context feedback loop: what the model sees next is shaped by what it just did. Sequoia's "Own Your Intelligence" piece (Sonya Huang, 19 Aug) argues that with strong evals, harness engineering, post-training and online learning, open models can now beat frontier models in specific domains. Same claim from two directions: the thing you own is the harness and the loop, not the weights. Here's what that means in a regulated production loop, which is where I run agents: every turn's context is an audit artefact. If you cannot replay exactly what the agent saw at turn 40, you don't own the loop. The vendor does, and so does whoever is asking during the incident review. Genuine question for people running agents in prod: are you persisting per-turn context snapshots, or only the final transcript? And if per-turn, what did it cost you in storage and in debugging time saved? (Sources in the first comment, per the sub's rule.)