--- title: How Lyft Built a Self-Serve AI Agent Platform with... description: Lyft's SCX Data Science and MLE team built a self-serve AI agent platform for customer support using LangGraph and LangSmith, cutting agent development time... canonical: https://daily.dev/posts/how-lyft-built-a-self-serve-ai-agent-platform-with-langgraph-and-langsmith-lapodvwtk twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: How Lyft Built a Self-Serve AI Agent Platform with LangGraph and LangSmith | daily.dev og:description: Lyft's SCX Data Science and MLE team built a self-serve AI agent platform for customer support using LangGraph and LangSmith, cutting agent development time... og:url: https://daily.dev/posts/how-lyft-built-a-self-serve-ai-agent-platform-with-langgraph-and-langsmith-lapodvwtk og:image: https://api.daily.dev/og/posts/LApoDVWtk.png og:image:alt: How Lyft Built a Self-Serve AI Agent Platform with LangGraph and LangSmith og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # How Lyft Built a Self-Serve AI Agent Platform with LangGraph and LangSmith **[LangChain](https://daily.dev/sources/langchain)** · 11 min read · 0 upvotes · 0 comments ## Summary Lyft's SCX Data Science and MLE team built a self-serve AI agent platform for customer support using LangGraph and LangSmith, cutting agent development time from ~6 months to ~2 weeks. The system uses a router-based multi-agent architecture where a meta agent dispatches rider and driver requests to specialized subagents, with safety checks running in parallel at every turn. Non-technical domain experts (ops teams, VoC leads, PMs) can now build configurable agents by writing prompts and JSON configs, without MLE involvement. LangSmith handles tracing, production monitoring dashboards, and automated LLM-as-a-judge evaluation pipelines. The team's biggest lesson: prompt quality—not infrastructure—was the real bottleneck, leading them to create a structured prompt writing framework and a Git-backed CI linting pipeline. Results include a 16% increase in AI resolution rate and a 20% reduction in hallucination rates. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Toyota Scales Enterprise AI with Deep Agents and LangSmith](https://daily.dev/posts/toyota-scales-enterprise-ai-with-deep-agents-and-langsmith-gsq24bwe8) · LangChain · 2 upvotes · 1 comments - [Agents Work. 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