Technical guides and step-by-step instructions on building cloud-native applications, engineering thought leadership, and scaling Agentic AI.
LangGraph tutorial 2025: design stateful AI agent workflows as explicit directed graphs. Learn typed state, conditional routing, cyclic execution, and MemorySaver checkpointers with full Python code examples.
Build a deep research agent in LangChain that autonomously queries multiple sources, then connect it to any tool microservice using Model Context Protocol (MCP). Includes full FastMCP server setup and LangChain adapter integration.
Compare traditional vector RAG with vectorless RAG in LangChain. Learn ChromaDB embeddings, text chunking strategies, and structural index navigation — with full Python code for both approaches and when to choose each.
Learn how to transition from simple LLM wrappers to autonomous AI agents in LangChain v1.x. Build a fully runnable ReAct tool-calling agent with Gemini, understand the reasoning loop, and see why agents outperform raw LLM calls for real-world tasks.
Ship LangChain agents to production with confidence. Learn to add safety guardrails, configure LLM failover routing with LiteLLM, and run automated quality evaluations with LangSmith — with full Python code and real evaluation datasets.
In the previous parts, we built the brain, the hands, and optimized the engine for cost. But in an enterprise setting, “it usually works” is...