Tag
monday.com rebuilt their AI copilot Sidekick by evolving from a single general-purpose agent to a system with specialized subagents and LangSmith Sandboxes, enhancing its capability to handle complex, iterative work in production.
The author shares their experience switching from semantic embeddings to BM25 for tool selection in agents, finding that BM25 achieves 81% top-1 accuracy vs. 64% for embeddings on a corpus of 200 query-tool pairs, because tool descriptions are short and keyword-driven rather than semantically rich like documents.
A year-long reflection on the hard parts of shipping AI agents for real service businesses, highlighting that infrastructure and edge cases matter more than the AI layer.
A developer recounts how many challenges in building AI agents actually stem from workflow and state management issues, not model intelligence, emphasizing the need for robust state handling and observability.