LangSmith Evaluation: Assessing Agent Quality with Real Production Data
LangChain · x · 2026-07-07
LangChain introduces the Evaluation feature in LangSmith: when an agent fails, developers can use real production data to evaluate its performance, pinpoint the exact failure point, and improve the agent's quality accordingly.
This capability is positioned as agent-oriented observability and evaluation, helping developers continuously enhance agent reliability.
Related event: LangChain Introduces LangSmith Agent Evaluation Features(2 posts)→
More from coding & agent
- Cognition's SWE-2 uses a KKT duality argument in RL to shift the effort Pareto curve — YouJiacheng · 2026-09-11
- First-ever Three.js Conference lands in Paris, with a panel on AI-shortened design workflows — OdinLovis · 2026-09-11
- Data engineering, not agent frameworks, is the real bottleneck for enterprise AI agents — dhruv2038 · 2026-09-11
- RTK Terminal Compression Cuts Tokens but Leaves Your AI Coding Bill Unchanged — Bartaseth · 2026-09-11
- GPT-6 Astra beats Factorio with enemies in 44 in-game hours at ~$4,500 API cost — liminal_bardo · 2026-09-11
- Investment Analyst Asks How to Build a Claude-Based Diligence Agent Stack — Careless_Tie2286 · 2026-09-11