A new paper on arXiv tries to be the single reference for how agentic AI actually works, top to bottom.
The paper, posted as arXiv:2606.24937 (a replace, now on v3) and titled "The Hitchhiker's Guide to Agentic AI," runs from transformer architecture and GPU training through to production deployment. The arXiv listing does not name any authors or an institutional affiliation, so it's unclear who assembled it. It opens with LLM fundamentals like fine-tuning, LoRA, and inference optimization, moves into alignment methods such as RLHF, DPO, and GRPO, then devotes its second half to agent-specific material: trajectory-based RL, memory systems, the Model Context Protocol, and multi-agent coordination. Each chapter reportedly pairs theory with executable notebooks and citations to primary literature.
Most agentic AI writing picks one layer - a framework, a protocol, a benchmark - and stops there. This guide's pitch is that the layers are inseparable: memory architecture choices ripple into RL setup, which shapes harness design, which determines whether your evaluation methodology means anything. That's a fair corrective in a field where "agent" now gets attached to almost any system with a tool-call loop.
Without named authors or a listed institution, this reads less like a single lab's doctrine and more like a crowdsourced field map - useful for orientation, but worth checking against the primary sources it cites before taking any claim as settled.