Paper Detail
Haggai Roitman
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.
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@misc{roitman2026hitchhiker,
title = {The Hitchhiker's Guide to Agentic AI: From Foundations to Systems},
author = {Haggai Roitman},
year = {2026},
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 -- t},
url = {https://huggingface.co/papers/2606.24937},
keywords = {transformer architecture, GPU systems, training, fine-tuning, SFT, LoRA, MoE, model compression, inference optimization, reinforcement learning from human feedback, RLHF, PPO, DPO, GRPO, reward modeling, RL for large reasoning models, chain-of-thought, test-time scaling, agentic training, trajectory-based RL, retrieval-augmented generation, RAG, Agentic RAG, memory systems, in-context memory, external memory, episodic memory, semantic memory, agent harness design, context management, agent design patterns, Model Context Protocol, MCP, agent skills, tool use, Agent-to-Agent communication protocol, multi-agent architectures, centralized topology, decentralized topology, hierarchical topology, agent development frameworks, agentic UI design, evaluation methodology, production deployment, huggingface daily},
eprint = {2606.24937},
archiveprefix = {arXiv},
}
{}