LangChain spotlights a way to turn evals into training data for coding agents
LangChain · x · 2026-07-23
LangChain amplifies a thread about making high-quality evals and environments available for specific coding-agent use cases.
The post argues that evals are effectively training data for agents: to improve behavior, you need to interview users about what the agent should be good at, then iteratively turn that into tasks and tests. Because agent behavior is hard to predict before running it, optimization is inherently iterative.
It also points to an open framework where teams can share skills and workflows that plug into coding agents using Trace data, making it easier for teams to own their own eval process.
Related event: LangChain Launches Eval Engineering Skill for Coding Agents(6 posts)→
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