Technical guides and step-by-step instructions on building cloud-native applications, engineering thought leadership, and scaling Agentic AI.
Move beyond single-agent limitations. Learn how to build collaborative multi-agent systems in LangGraph where a supervisor routes tasks to specialised sub-agents over shared state channels — with fully runnable code and design guidance.
Go beyond black-box agent loops. Learn how LangGraph models workflows as explicit graphs with typed state, conditional routing, multi-turn memory, and checkpointing — with step-by-step runnable code.
Learn how deep research agents decompose complex tasks into sub-problems, and how the Model Context Protocol (MCP) lets agents connect to decentralized tool microservices — with full setup and runnable examples.
Understand how RAG gives LLMs access to your private knowledge base. Compare the traditional vector pipeline against Vectorless RAG — with fully runnable examples, design trade-offs, and practical guidance on when to use each.
Understand the shift from static LLM calls to autonomous agents. Learn LangChain v1.x model patterns, message types, streaming, batching, and build your first runnable ReAct tool agent step by step.
Make your LangChain agents production-safe. Add input/output guardrails, LLM failover routing with LiteLLM, and measure quality with LangSmith evaluations — with complete runnable examples and design guidance.