A hands-on series on building agentic AI systems, from first principles to evaluation.
Agents, explained without the hype: what they are, when one is enough, when you need several, and how to build a working multi-agent system with LangChain.
Traditional RAG retrieves once and hopes for the best. Agentic RAG lets the model decide whether, what, and how to retrieve, then check its own work. Here is the difference, with code.
An agent with no tools is just a chatbot. Here is how tool calling really works, what makes a tool the model can use reliably, and how MCP lets you plug in tools you did not write.
A hands-on guide to building MCP servers with FastMCP, drawn from two I actually shipped: the anatomy, the rules, stdio vs remote HTTP, the pitfalls, and how to test and deploy.
You cannot improve an agent you cannot measure. A deep, practical guide to evaluating agents with LangSmith: tracing, datasets, evaluators, experiments, and what makes agent evaluation different.
Deep Agents bakes planning, context management, subagents, and human approval into the agent loop. Here is why it exists, every main feature, and a production-grade customer support agent built with it.
A clear split between an agent's working memory and its cross-session memory, with LangGraph code for checkpointers, stores, and the semantic/episodic/procedural breakdown.