Personally incredibly exciting at LangChain Labs to lead a focused, applied research effort into Continual Learning. A big prior is in treat…

Personally incredibly exciting at LangChain Labs to lead a focused, applied research effort into Continual Learning. A big prior is in treating the long horizon agent optimization problem as one that requires understanding signals from Agent Traces, Environments, and Production Systems at data scales that will far exceed what humans have produced before - Traces are a projection how Agents operate in the world we drop them into. They’ll produce more data than humans have in our history and there’s a large efficient data mining effort that needs to happen to make this data usable for agent improvement - The optimization recipe for real Continual Learning on agents will look different for different companies, agents, and situations. It will include things like Eval/Environment Generation, Harness Engineering, Post-Training, large-scale contextual indexing, search & retrieval - We’re very focused on practical applications of Continual Learning in ways that improve real world agents doing real world tasks in domains like Law, Banking, Coding, etc. This makes it even more exciting to be partnering with our partners who are also doing this in the real world today @PrimeIntellect @NVIDIAAI @harvey @FireworksAI_HQ @baseten if these research directions resonate and you’re building towards this future - myself and @jakebroekhuizen and I’m sure @hwchase17 would love to hear what you’re doing 🚀 bullish on LangChain Labs. imo initiatives like this are important because continual learning for agents is fundamentally an infrastructure problem...agents need systems that can collect trajectories, extract learning signal from behavior, optimize prompts/policies and close the …

Source: Harrison Chase (X) | 2026-05-20