Agentic AI Systems

Single agents can only take you so far. Learn to build the systems that power real production AI: teams of agents, grounded in real knowledge, that remember.

Most "multi-agent" tutorials are really just one model wearing different prompts. Most "RAG" tutorials stop at a basic vector search with no idea why retrieval quality actually matters. And almost none of them show an agent that genuinely improves over time, rather than starting from zero in every session.

AGENTIC AI SYSTEMS is a 17-chapter, hands-on guide to the three subjects real production agentic systems need together: multi-agent orchestration, retrieval-augmented generation (RAG), and long-term memory. You'll build Nexus, a complete system that coordinates specialized agents, grounds every answer in real, cited documents, and remembers what it learns across sessions.

What you'll actually build:

• Genuinely distinct agents with formal roles, scoped communication, and real disagreement-handling — debate, consensus, and arbitration


• Multi-agent orchestration in LangGraph: sequential, hierarchical (supervisor-worker), and peer-to-peer patterns, including nested subgraphs


• A real embeddings pipeline with Voyage AI, and the exact math behind semantic search


• Chunking strategies that actually preserve meaning, instead of fracturing documents mid-sentence


• A production vector database (Chroma) with metadata filtering and clean update handling


• A complete, grounded RAG pipeline with honest "I don't know" handling and verifiable citations


• Advanced retrieval: hybrid search, cross-encoder re-ranking, and query transformation (including HyDE)


• Long-term semantic memory — durable, agent-generated knowledge that consolidates and persists across sessions


• A fully unified architecture, with deliberately scoped tool access per agent, evaluated and deployed as a real multi-user service

Two complete capstone projects close out the book: a fully guided build (OnboardHub, a multi-agent onboarding assistant grounded in a company handbook) and a lighter-scaffolded challenge (StudyMind, a memory-augmented tutoring system) where you apply everything to a new domain on your own.

Who this book is for:
Developers who've completed Book Two in this series — or have equivalent hands-on experience with LangChain, LangGraph, and structured output — and are ready to build the kind of sophisticated, production-grade agentic systems that single-agent tutorials never touch.

Written by Dave Malhotra, with the same commitment throughout this series: real, working code over slideware, every concept explained before it's automated, every chapter building toward something you can actually run.

A free companion package — full source code for Nexus, OnboardHub, StudyMind, and every supporting module — is available for download, no registration required.

This is Book Three, the final volume in the Agentic AI Engineering series — completing the arc from hand-built fundamentals, through the professional toolchain, to fully unified multi-agent, retrieval-grounded, memory-capable systems.

Build the systems that actually power production AI.