Improving Agents is a Data Mining Problem — Vivek Trivedy, LangChain
LangChain frames agent improvement as a data mining problem over production trace data
“there's a very tight coupling between what observability is and what continual learning is”
Vivek Trivedy from LangChain proposes a four-step recipe for continuously improving agents: ship to production, collect large-scale trace data, mine that data for failure patterns, then run data-driven experiments to validate prompt/tool/orchestration changes. The core thesis is that observability and continual learning are tightly coupled—agent traces are the raw material for improvement loops. This is a practical engineering framework rather than a novel research finding, but it reflects a maturing consensus in the agentic AI space.