From AI Prototype to Production with LangGraph SDK

🌱 ➡️ 🌲 Turning AI Prototypes into Production-Ready Agents A good AI application is nothing without knowing how to properly deploy. This week, I learned how to take an AI project from prototype to production using the LangGraph SDK. The LangGraph SDK gives structure — it’s the framework for building and serving agentic systems. LangSmith adds observability — tracking, logging, and debugging agents so you can see how and why they make decisions. 🎥 Loom Walkthrough: https://lnkd.in/gGtqWga5 💻 Repo: https://lnkd.in/gCH52CDZ What I did: I built two agents that uses tools like Tavily Search, Arxiv, and RAG. 1️⃣ A simple tool agent 2️⃣ An agent-with-helpfulness that evaluates its own responses for quality before finishing. Using LangGraph Studio, I visualized every node, tool call, and loop — seeing exactly how the agent reasoned step by step. 💡 Takeaway: Building is only half the story. Deployment, monitoring, and visibility are what make AI systems production-ready. LangGraph and LangSmith bring that structure and transparency together — turning “cool demos” into reliable, scalable applications that will help the bottom line and give insight. Thank you AI Makerspace! ⚡️ #AIEngineering #LangGraph #LangSmith #AIMakerspace #LLMOps

🌱AI Apps - from Prototype -> Production w/ Langraph SDK 🌲

🌱AI Apps - from Prototype -> Production w/ Langraph SDK 🌲

https://www.loom.com

Nice walkthrough of how to visualize and evaluate agents through langgraph studio Betsy! 🏗️ 🚢 🚀

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