auto-research style proposal loops should be data driven! they largely work best only when Data/Evals/Feedback give a useful gradient to hil…

auto-research style proposal loops should be data driven! they largely work best only when Data/Evals/Feedback give a useful gradient to hill-climb against increasingly auto-research is a very good tool for general Agent Optimization the LangChain ecosystem is here to help running these data driven self-improvement loops with easy tooling so teams can focus on their problems: - a customizable agent harness in deepagents or create_agent - support for any model provider (Open/Closed/Local models) - BYO tools, prompts, skills, you name it - tooling for developing evals, running them, tracing them, and understanding them at scale in OpenEvals & LangSmith getting started today means getting an earlier feel for the data your specific use-case needs to power the loop 🧠Self-Harness: Harnesses that improve themselves New paper on agents shaping their own harnesses to improve over time. Not from LangChain, but builds on top of DeepAgents! Three key steps: 1/ Weakness mining: find failure modes from traces 2/ Harness proposal: suggest changes to…

Source: Harrison Chase (X) | 2026-06-23