LangSmith Powers Clay’s 300M Monthly AI Agent Runs: Scalable LLM Observability in 2026
Clay leverages LangSmith to debug, evaluate, and monitor 300 million AI agent runs per month, enabling scalable, reliable go-to-market automation for enterprise sales teams.
LangSmith Powers Clay’s 300M Monthly AI Agent Runs: Scalable LLM Observability in 2026
summarize3-Point Summary
- 1Clay leverages LangSmith to debug, evaluate, and monitor 300 million AI agent runs per month, enabling scalable, reliable go-to-market automation for enterprise sales teams.
- 2LangSmith Powers Clay’s 300M Monthly AI Agent Runs Clay, a leading go-to-market automation platform, relies on LangSmith to monitor, evaluate, and deploy over 300 million AI agent runs per month — setting a new standard for production-grade LLM observability in 2026.
- 3Sales teams use Clay to automate lead enrichment, personalized outreach, and CRM routing via complex LLM-driven workflows.
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LangSmith Powers Clay’s 300M Monthly AI Agent Runs
Clay, a leading go-to-market automation platform, relies on LangSmith to monitor, evaluate, and deploy over 300 million AI agent runs per month — setting a new standard for production-grade LLM observability in 2026. Sales teams use Clay to automate lead enrichment, personalized outreach, and CRM routing via complex LLM-driven workflows. Without robust AI agent monitoring, even minor prompt drift could collapse entire revenue pipelines. LangSmith provides the essential infrastructure to maintain precision at scale.
Real-Time Observability: Tracing Every Agent Interaction
LangSmith’s observability layer enables Clay’s engineering team to trace every AI agent interaction in real time. Each trace captures full prompt history, model outputs, tool call logs, and latency metrics — surfacing hallucinations, failed tool executions, or latency spikes before they impact users. This granular visibility has reduced mean time to resolution (MTTR) for agent failures by 70%.
Quantitative Evaluation: A/B Testing Prompts at Scale
Clay runs hundreds of A/B tests weekly across prompt variants, measuring outcomes like lead qualification accuracy, response relevance, and CRM conversion lift. LangSmith’s evaluation tools provide quantitative feedback, helping teams replace underperforming prompts with data-backed improvements. Since implementation, erroneous outreach has dropped by 42%, directly boosting pipeline velocity.
Seamless Deployment: From Prototype to Production in Hours
LangSmith’s centralized deployment system eliminates custom orchestration. With built-in version control, environment segregation, and auto-scaling, Clay cuts deployment cycles from days to hours. Enterprise clients in SaaS, fintech, and healthcare now receive new AI features 5x faster — accelerating time-to-value across 1,200+ organizations.
Democratizing AI: No-Code Agent Builder Drives 300% Growth
Clay’s no-code Agent Builder, powered by LangSmith’s API, empowers non-technical GTM teams to create and iterate on workflows without engineering support. In the past year, sales operations staff created 300% more agents — turning frontline users into AI co-developers and unlocking unprecedented agility.
Why Scale Demands End-to-End Observability
Clay’s stack combines LangChain for prototyping and LangGraph for stateful agents — but LangSmith is the operational backbone. With sub-200ms response times across 99.7% of runs and billions of monthly data points, Clay proves that AI scale isn’t about model size — it’s about control. LangSmith turns chaotic AI behavior into measurable, reliable revenue operations.
