The Hitchhiker's Guide to Agentic AI: From Foundations to Systems

Abstract

The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems, covering the full stack from first principles to production deployment. The central thesis: building great agentic systems requires understanding every layer of the pipeline, not just one. The book opens with the LLM substrate, covering transformer architecture, GPU systems, training and fine-tuning (SFT, LoRA, MoE), model compression, and inference optimization, as essential foundations. It then develops the alignment and reasoning layer: RLHF, PPO, DPO and its variants, GRPO, reward modeling, and RL for large reasoning models including chain-of-thought and test-time scaling. The second half is devoted to agentic AI proper: agentic training and trajectory-based RL, RAG and Agentic RAG, memory systems (in-context, external, episodic, and semantic), agent harness design, loop engineering, graph-based orchestration, and a taxonomy of agent design patterns covering security, red teaming, and gateway infrastructure. Inter-agent coordination is covered in depth: the Model Context Protocol (MCP), agent skills and tool use, the Agent-to-Agent (A2A) protocol, and multi-agent architectures spanning centralized, decentralized, and hierarchical topologies. The book concludes with agent development frameworks, agentic UI design, evaluation methodology (non-deterministic evaluation, reasoning collapse, LLM-as-Judge), production deployment, and the regulatory environment (EU AI Act, California SB 942) as an engineering requirement. Each chapter pairs theory with implementation guidance, executable notebooks, and references to the primary literature.