Agentic AI-autonomous systems capable of perception, reasoning, and action-is redefining how we build intelligent applications. From AI customer service agents and healthcare assistants to real-time financial analysis tools, these systems integrate Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and goal-oriented control. Rust, with its unmatched performance, safety guarantees, and asynchronous power via Tokio, is the ideal language to build scalable, high-concurrency AI agents that are production-ready.
Written by a seasoned systems engineer and AI practitioner, Building Agentic AI with Rust is the first comprehensive guide focused on using Rust to build high-performance, autonomous AI agents. With deep real-world experience, clean architectural patterns, and a practical teaching style, this book bridges the gap between cutting-edge AI research and robust, deployable software engineering practices.
This hands-on guide shows developers how to architect, implement, and deploy agentic AI systems using Rust and modern AI tools like OpenAI, Hugging Face, and vector search engines. Each chapter provides a step-by-step approach, from designing the agent loop to implementing a scalable RAG system and deploying with Docker and cloud services. You'll learn best practices for async programming with Tokio, profiling for performance, and implementing real-world use cases across industries.
Implementing the Perceive-Reason-Act loop in Rust
Architecting modular AI agents with traits and async tasks
Integrating OpenAI and Hugging Face LLMs using structured prompts
Building Retrieval-Augmented Generation (RAG) pipelines
Scaling with Tokio, caching, and vector stores like Qdrant
Packaging, containerizing, and deploying agents to AWS and GCP
Monitoring, logging, and optimizing agents for production
Full case studies: customer support, healthcare, and financial AI
This book is written for Rust developers, AI engineers, system architects, and technical enthusiasts looking to build powerful autonomous agents with real-world capabilities. If you're comfortable with Rust and want to extend your skills into modern AI systems, this guide is for you.
No prior experience with LLMs or RAG is required-concepts are introduced clearly and practically.
Agentic AI is no longer experimental-it's production-ready, and it's here now. As the field of generative AI evolves rapidly, learning to build scalable, secure, and performant agents with Rust puts you ahead of the curve. Don't wait to catch up with the future-become one of the first engineers building it.
Master the intersection of systems programming and generative AI. Build fast, safe, and intelligent autonomous agents with Rust today. Get your copy of Building Agentic AI with Rust and start coding the future of AI, now.
代理式人工智慧（Agentic AI）自動系統具備感知、推理和行動能力，正在重新定義我們構建智能應用的方式。從人工智慧客服代理和醫療助理到即時金融分析工具，這些系統整合了大型語言模型（Large Language Models, LLMs）、檢索增強生成（Retrieval-Augmented Generation, RAG）和目標導向控制。Rust 以其無與倫比的性能、安全保證和透過 Tokio 提供的非同步能力，成為構建可擴展、高併發的生產就緒人工智慧代理的理想語言。
《使用 Rust 構建代理式人工智慧》是由一位經驗豐富的系統工程師和人工智慧實踐者撰寫的首部全面指南，專注於使用 Rust 構建高性能、自主的人工智慧代理。這本書結合了深厚的實務經驗、清晰的架構模式和實用的教學風格，彌合了尖端人工智慧研究與穩健、可部署的軟體工程實踐之間的鴻溝。
這本實用指南展示了開發者如何使用 Rust 和現代人工智慧工具（如 OpenAI、Hugging Face 和向量搜索引擎）來架構、實現和部署代理式人工智慧系統。每一章提供逐步的方法，從設計代理循環到實現可擴展的 RAG 系統，並使用 Docker 和雲服務進行部署。您將學習使用 Tokio 進行非同步編程的最佳實踐、性能分析以及在各行各業中實現實際用例。
- 在 Rust 中實現感知-推理-行動循環
- 使用 traits 和非同步任務架構模組化人工智慧代理
- 使用結構化提示整合 OpenAI 和 Hugging Face LLMs
- 構建檢索增強生成（RAG）管道
- 使用 Tokio、快取和向量存儲（如 Qdrant）進行擴展
- 將代理打包、容器化並部署到 AWS 和 GCP
- 監控、記錄和優化生產中的代理
- 完整案例研究：客戶支持、醫療和金融人工智慧
這本書是為 Rust 開發者、人工智慧工程師、系統架構師和希望構建具備實際能力的強大自主代理的技術愛好者而寫的。如果您對 Rust 感到熟悉並希望將技能擴展到現代人工智慧系統，這本指南就是為您準備的。
不需要具備 LLMs 或 RAG 的先前經驗——概念將以清晰和實用的方式介紹。代理式人工智慧不再是實驗性的——它已經準備好進入生產，並且現在就在這裡。隨著生成式人工智慧領域的快速發展，學習如何使用 Rust 構建可擴展、安全和高效的代理將使您走在前端。不要等著追趕未來——成為首批構建它的工程師之一。
掌握系統編程與生成式人工智慧的交集。今天就使用 Rust 構建快速、安全和智能的自主代理。獲取《使用 Rust 構建代理式人工智慧》的副本，開始編寫人工智慧的未來。
