Production‑Grade Agents with LangGraph

Answer the question: “What is an agent?”

Understand how to build production‑grade agent applications using LangGraph, including multi‑step workflows, monitoring, and evaluation.

🛠️ Key Topics Covered:

• What defines an “agent” in AI—beyond prompts
• Constructing agent workflows with state, cycles, and human‑in‑the‑loop controls
• Building agentic RAG pipelines and tool‑augmented flows
• Monitoring and evaluating agents using LangSmith for observability and metrics

🧪 Learn to:

1. Set up a LangGraph agent architecture
2. Integrate LangSmith tracing for runtime introspection
3. Deploy and monitor complex multi‑step agents in production

🚀 Ideal for engineers building robust, traceable AI agents in real‑world systems.

Why this guide matters

– Builds on LangGraph’s strengths: its graph‑based orchestration model for reliable agent https://medium.com/cyberark-engineering/building-production-ready-ai-agents-with-langgraph-a-real-life-use-case-7bda34c7f4e4

• Covers use of LangSmith for production‑grade observability and evaluation https://docs.smith.langchain.com/?utm_source=chatgpt.com
• Uses high‑quality references including official docs, tutorials, and videos https://www.reddit.com/r/LangChain/comments/1kpv07s/how_langgraph_langsmith_saved_our_ai_agent_heres/?utm_source=chatgpt.com https://www.youtube.com/watch?v=GMPFt-LrOWc&utm_source=chatgpt.com https://www.youtube.com/watch?v=1w5cCXlh7JQ&utm_source=chatgpt.com https://www.youtube.com/watch?pp=0gcJCdgAo7VqN5tD&v=aHCDrAbH_go&utm_source=chatgpt.com https://djangodevops.pro/blog/langgraph-studio-langsmith-the-complete-python-agent-development-guide/?utm_source=chatgpt.com