Every company can rent the same frontier models, so the edge comes from what a company's agents learn about its own business. Databricks' answer is a system of record built for agents. Before an agent can be trusted with real work, that record has to tell it: 1. What's true: the company's own data 2. What it means: how the business defines things (grounding agents in it raised accuracy from about 50% to 84.5% in Databricks' testing) 3. What it's allowed to do: an identity, a budget, and guardrails 4. Whether it worked: grading against the company's own definition of good Feed every correction back into the record, and the company keeps what it learns even when the model changes. "If you replace the AI model at your company and the new one inherits your definitions, permissions, evaluated cases, customer history, and hard-won exceptions, that is a foundation for a sustainable competitive advantage." From the latest Profiles of the Future by @bhorowitz and @mikemcg0 on Databricks & the Future of the Firm: https://www.a16z.news/p/databricks-and-the-future-of-the