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Discuss agent architectures and share learnings with the community.
Discuss agent architectures and share learnings with the community.
Generation is solved. The bottleneck is judgment, and the specific, learnable, scalable form of judgment is saying no to confident AI output, and knowing exactly why. Most teams let every one of those noes fall on the floor.
Agent observability needs span-level tool attribution, critic decisions, and replayable traces, not aggregate token dashboards that hide the fork where everything went wrong.
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.
LangSmith gives you tracing, datasets, and online evals out of the box. The teams that get value wire production failures back into golden datasets. Here's the loop, end to end.