LangChain releases Tuned Evaluators: Auto-score agents with 82% cost reduction
LangChain · x · 2026-08-19
LangChain released LangSmith Tuned Evaluators to automatically score agent behavior in production, starting with Perceived Error. Perceived Error is one of the clearest signals that an agent is providing a helpful experience. In LangChain's benchmark, their specialized model outperformed every frontier model tested and reduced evaluation costs by 82%. The tool offers a turnkey, managed service, removing the need for teams to write complex prompts, maintain LLM-as-a-judge models, or manage inference infrastructure.
More from coding & agent
- Coding agents keep opening 3 stacked PRs per chat, and merging them is a nightmare — holdenmatt · 2026-10-16
- Google-led paper uses sequential auctions to organize decentralized LLM agent workflows — _onionesque · 2026-10-16
- Since OpenAI's math release, GitHub contributors keep driving down the integer-multiplication kappa — xennygrimmato_ · 2026-10-16
- Pattern MCP makes coding agents check existing components before writing new code — DonR954 · 2026-10-16
- Zero-keyframe procedural spider walks walls and ceilings using pure raycasts — yshan2u · 2026-10-16
- OpenCode v1.19.2 keeps 1.x installs off the 2.x update path — opencode-agent[bot] · 2026-10-16