--- title: Monte Carlo: Building Data + AI Observability Agents... description: Monte Carlo, an enterprise data and AI observability platform, built an AI Troubleshooting Agent using LangGraph and LangSmith to automate root cause analysis... canonical: https://daily.dev/posts/monte-carlo-building-data-ai-observability-agents-with-langgraph-and-langsmith-g6hc5hf0m twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Monte Carlo: Building Data + AI Observability Agents with LangGraph and LangSmith | daily.dev og:description: Monte Carlo, an enterprise data and AI observability platform, built an AI Troubleshooting Agent using LangGraph and LangSmith to automate root cause analysis... og:url: https://daily.dev/posts/monte-carlo-building-data-ai-observability-agents-with-langgraph-and-langsmith-g6hc5hf0m og:image: https://api.daily.dev/og/posts/G6hC5HF0M.png og:image:alt: Monte Carlo: Building Data + AI Observability Agents 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. # Monte Carlo: Building Data + AI Observability Agents with LangGraph and LangSmith **[LangChain](https://daily.dev/sources/langchain)** · 5 min read · 0 upvotes · 0 comments ## Summary Monte Carlo, an enterprise data and AI observability platform, built an AI Troubleshooting Agent using LangGraph and LangSmith to automate root cause analysis for data pipeline issues. The agent spawns hundreds of sub-agents to investigate multiple hypotheses in parallel — mimicking how experienced data engineers troubleshoot but at scale. LangGraph's graph-based decision flow enabled rapid development (4 weeks to demo), while LangSmith provided immediate debugging and trace visualization. The architecture runs on AWS using Amazon Bedrock for LLM access, ECS Fargate for containerized microservices, and Amazon RDS for data persistence. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [How Clay uses LangSmith to debug, evaluate, and monitor 300 million agents runs per month](https://daily.dev/posts/how-clay-uses-langsmith-to-debug-evaluate-and-monitor-300-million-agents-runs-per-month-ouhxyynqq) · LangChain · 1 upvotes · 0 comments - [How ServiceNow uses LangSmith to get visibility into its customer success agents](https://daily.dev/posts/how-servicenow-uses-langsmith-to-get-visibility-into-its-customer-success-agents-yf11jgqdb) · LangChain · 0 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#amazon-bedrock](https://daily.dev/tags/amazon-bedrock), [#langgraph](https://daily.dev/tags/langgraph), [#data-observability](https://daily.dev/tags/data-observability), [#langsmith](https://daily.dev/tags/langsmith) [View this post on daily.dev](https://daily.dev/posts/monte-carlo-building-data-ai-observability-agents-with-langgraph-and-langsmith-g6hc5hf0m) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Monte Carlo: Building Data + AI Observability Agents with LangGraph and LangSmith","url":"https://daily.dev/posts/monte-carlo-building-data-ai-observability-agents-with-langgraph-and-langsmith-g6hc5hf0m","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/monte-carlo-building-data-ai-observability-agents-with-langgraph-and-langsmith-g6hc5hf0m"},"datePublished":"2026-07-08T19:23:02.411Z","dateModified":"2026-07-08T19:24:13.845Z","description":"Monte Carlo, an enterprise data and AI observability platform, built an AI Troubleshooting Agent using LangGraph and LangSmith to automate root cause analysis...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/053820de421d288cc4749776c307befc?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/053820de421d288cc4749776c307befc?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"LangChain","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"LangChain","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/0f4f2629e0724a849823f8cd0d913e13","url":"https://daily.dev/sources/langchain"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/monte-carlo-building-data-ai-observability-agents-with-langgraph-and-langsmith-g6hc5hf0m","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"ai-agents,amazon-bedrock,langgraph,data-observability,langsmith","timeRequired":"PT5M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"LangChain","item":"https://daily.dev/sources/langchain"},{"@type":"ListItem","position":3,"name":"Monte Carlo: Building Data + AI Observability Agents with LangGraph and LangSmith"}]} ```