LangChain: run agent evals through the same graph
Testing evals against the production agent graph removes the gap between what benchmarks measure and what users actually experience. Builders of conversational or multi-turn agents can reuse this pattern: simulate personas through the same entry point as live traffic, then trust the same scores in CI. It pairs well with LangSmith-style tracing workflows for regressions.
LangChain describes an approach where every evaluation, from single-reply fixtures to simulated multi-turn conversations with LLM patient personas, runs against the real agent graph. The setup means eval scores measure the exact behavior a real user would encounter. The example uses simulated patient personas for a conversational agent.
Agent evals run through the same graph. Every eval, from single-reply fixtures to simulated multi-turn conversations with LLM patient personas, runs against the real agent, so what they score is exactly what a patient would meet. 💬 1 🔄 1 ❤️ 0 👀 492 📊 1 ⚡
