A Full AI Agent Engineering Book, with 28 Runnable Projects, Is Now Open-Source
Most agent resources stop at API tutorials. This book supplies the missing engineering layer — context design, tool sandboxing, small-model validation, and full evaluation pipelines — that turns a demo into a production system.
The book, now fully open-sourced with its PDF and code, builds everything around the equation Agent = LLM + Context + Tools. It spans from context engineering and KV Cache-friendly design to production-grade Coding Agents, multi-agent collaboration, and self-evolving systems. Each of the 10 chapters includes runnable projects, such as a 17-tool coding assistant, a hybrid retrieval pipeline for Agentic RAG, and a dual-agent phone-and-browser parallel system.
A deliberate design choice runs throughout: small models are used to prove that system architecture matters more than raw scale. Chapter 2, for instance, demonstrates complete tool calling on a 0.6B local model. The projects are ablation-driven, with controlled experiments that quantify the impact of removing individual context components, and the Coding Agent is built in pure Python with no command-line dependencies.
Li Bojie, a former Huawei Fellow and current agent infrastructure founder, targets engineers who need to build real systems, not just call APIs. The book also covers evaluation benchmarks like SWE-bench and GAIA, post-training comparisons between SFT and RL, and practical API key guidance for Chinese and international model providers.
The book's central formula — Agent = LLM + Context + Tools — is a deliberate pushback against the idea that better models alone solve agent problems; it treats the LLM as just one component in a larger system.
Validating tool calling on a 0.6B model is a practical counter to the assumption that agent capabilities require frontier models, which has direct cost implications for anyone running agents at scale.
Open-sourcing a full textbook with 28 runnable projects, rather than just a code repo, signals a bet that agent engineering is now a distinct discipline with its own canon, not a subfield of prompt engineering.