Book outlines full-stack methodology for building agentic AI systems

A new arXiv preprint proposes a comprehensive framework for building agentic AI systems, structured as a practitioner’s reference spanning from foundational concepts to production deployment. The work, titled *The Hitchhiker’s Guide to Agentic AI: From Foundations to Systems*, argues that effective agentic systems depend on understanding every layer of the pipeline—not just the underlying large language model.

The guide begins with the LLM substrate, detailing transformer architecture, GPU systems, training and fine-tuning methods (including SFT, LoRA, and MoE), model compression, and inference optimization. It positions these as essential foundations rather than the primary focus of agentic design.

The alignment and reasoning layer is addressed next, covering techniques such as reinforcement learning from human feedback (RLHF), PPO, DPO and its variants, GRPO, reward modeling, and reinforcement learning for large reasoning models, including chain-of-thought and test-time scaling.

The second half focuses on agentic AI proper, with topics including agentic training and trajectory-based reinforcement learning, retrieval-augmented generation (RAG and Agentic RAG), memory systems (in-context, external, episodic, and semantic), agent harness design, and a taxonomy of agent design patterns.

Inter-agent coordination is covered in depth, including the Model Context Protocol (MCP), agent skills and tool use, the Agent-to-Agent (A2A) communication protocol, and multi-agent architectures spanning centralized, decentralized, and hierarchical topologies.

The book concludes with sections on agent development frameworks, agentic UI design, evaluation methodology for agentic tasks, and production deployment. Each chapter pairs theoretical foundations with implementation guidance, code examples, and references to primary literature.