In the first post in this series, we made the case that agentic AI demands a fundamentally new architecture built for connected, nondeterministic systems rather than isolated models. Here, we discuss what that architecture looks like. Most enterprise AI platforms were built for simpler systems: single models serving narrow use cases behind static application programming interface (API) endpoints. Data flowed into specific applications through fixed extract, transform, load (ETL) pipelines; orchestration logic lived within individual systems; and governance was typically tacked on after deployment.

Full commentary : Modern AI platforms are converging around a shared architecture built for orchestration, visibility, and governed data access.