Discuss agent architectures and share learnings with the community.
I've replaced three 'RAG is solved' pipelines this year. The pattern is always the same: layout-aware parsing, hybrid retrieval, and a reranker, not a bigger embedding model.
Cloud Next was less about a new model and more about where agents run, who governs them, and how they get discovered inside an org. Here's what Agentspace and the ADK push mean for enterprise buyers.
AutoGen taught Microsoft conversation loops; Semantic Kernel taught it enterprise plumbing. The Agent Framework is the merger, and the migration story matters more than the feature list.
Self-hosted agents trade API convenience for data-plane control. For regulated industries that's not ideology; it's the only architecture legal will sign.
Tabular enterprise data needs schema-aware retrieval, SQL or semantic layers, and explicit join logic. Dumping rows into chunks and embedding them is why agents confidently cite the wrong quarter.
I/O, Cloud Next, Build, re:Invent, and Interrupt across one season. The throughline isn't a winning framework; it's that the whole industry quietly admitted reliability and governance are the unsolved part.
The LangChain brand spans an abstraction library, a graph runtime, an eval/observability platform, and an expression language. Teams that blur them ship the wrong tool for the layer. Here's the clean mental model.
