The Hitchhiker's Guide to Agentic AI: From Foundations to Systems
A comprehensive practitioner's reference for building autonomous AI systems, covering the full stack from LLM foundations to agent coordination and production deployment. Written by Haggai Roitman and submitted to arXiv on June 22, 2026.
[2606.24937] The Hitchhiker's Guide to Agentic AI: From Foundations to Systems
[Submitted on 22 Jun 2026]
Title:The Hitchhiker's Guide to Agentic AI: From Foundations to Systems
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Abstract:The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems. The book covers the full stack from first principles to production deployment, organized around a central thesis: building great agentic systems requires understanding every layer of the pipeline, not just one. The book opens with the LLM substrate -- transformer architecture, GPU systems, training and fine-tuning (SFT,LoRA, MoE), model compression, and inference optimization -- treated as essential foundations rather than the primary focus. It then develops the alignment and reasoning layer: reinforcement learning from human feedback (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. Topics include agentic training and trajectory-based RL, retrieval-augmented generation (RAG and Agentic RAG), memory systems (in-context, external, episodic, and semantic), agent harness design and context management, and a taxonomy of agent design patterns. Inter-agent coordination is covered in depth: 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 agent development frameworks, agentic UI design, evaluation methodology for agentic tasks, and production deployment. Each chapter pairs rigorous theoretical foundations with implementation guidance, code examples, and references to the primary literature.
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
Cite as: arXiv:2606.24937 [cs.AI]
(or arXiv:2606.24937v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.24937
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Haggai Roitman [view email] [v1] Mon, 22 Jun 2026 17:48:54 UTC (5,525 KB)
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