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Adam Łucek discusses how LangChain uses trace data to build evaluations for production agents.
A practitioner shares lessons from running 30 AI agents in production for 6 months, arguing that framework choice is less critical than a robust memory and observability layer to prevent loops, state loss, and cost spikes.
This paper introduces the concept of the stochastic-deterministic boundary (SDB) for production LLM agents and provides a methodology for selecting architectural patterns to improve reliability and performance.
A reflection on how AI agents fail in production due to accumulated state issues (stale context, expired tokens, conflicting memory) rather than reasoning flaws, emphasizing the need for better state management.
A developer describes the challenge of deploying AI agents in environments where business context is spread across multiple tools with conflicting definitions, and asks the community for solutions beyond manual reconciliation.
A comprehensive guide on building reliable AI agents, explaining core components of perception, decision logic, and action interface, with insights from a former Meta engineer.