The Comprehensive Guide to Agentic AI: An End-to-End Practitioner Resource

A new arXiv publication, 'The Hitchhiker's Guide to Agentic AI,' offers a comprehensive practitioner's reference for engineers building autonomous AI systems. Its central thesis is that building truly capable agentic systems requires understanding the entire pipeline, not just a single layer, so the book starts from the LLM substrate—transformer architecture, GPU systems, training and fine-tuning techniques (SFT, LoRA, MoE), model compression, and inference optimization.

The second section covers the alignment and reasoning layer, detailing RLHF, PPO, DPO and its variants, GRPO, reward modeling, and RL techniques for large reasoning models including chain-of-thought and test-time scaling. The bulk of the book focuses on agentic AI itself: trajectory-based RL, RAG and Agentic RAG, memory systems (in-context, external, episodic, semantic), agent harness design, context management, and a taxonomy of agent design patterns.

Multi-agent coordination receives deep treatment as well, covering the Model Context Protocol (MCP), agent skills and tool use, the Agent-to-Agent (A2A) protocol, and multi-agent architectures across centralized, decentralized, and hierarchical topologies. The book concludes with agent development frameworks, agentic UI design, evaluation methodology, and production deployment. Each chapter pairs theoretical foundations with code examples and references to primary literature, making it valuable for both research and engineering practice.