Clay evaluates AI agents on quality, throughput and cost
The framework shifts agent evaluation from single accuracy scores to a three-way trade-off, which changes how teams should size models and routing for production workloads. Builders deploying agents in cost-sensitive markets can use LangSmith evals to run the same quality-versus-cost benchmarking on their own pipelines before committing to a provider.
Clay evaluates every AI agent it deploys using three criteria: quality, throughput and cost. Jeff Barg explained how LangSmith's evaluation tools measure agent quality and benchmark it against cost and throughput. The discussion was shared by LangChain in a post on 2 September 2026 via its X account.
Clay evaluates every AI agent on three things: ✅ Quality ✅ Throughput ✅ Cost @jeffbarg on how LangSmith's evals measure quality and benchmark it against cost and throughput. Your browser does not support the video tag. 🔗 View on Twitter 💬 2 🔄 0 ❤️ 2 👀 966 📊 2 ⚡
