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

תקציר מקורי באנגליתarXiv:2606.24937v3 Announce Type: replace-cross 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