Building Agentic Ai With Rust

Building Agentic AI with Rust

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.

Generative AI with LangChain

Go beyond foundational LangChain documentation with detailed coverage of LangGraph interfaces, design patterns for building AI agents, and scalable architectures used in production—ideal for Python developers building GenAI applications Key Features Bridge the gap between prototype and production with robust LangGraph agent architectures Apply enterprise-grade practices for testing, observability, and monitoring Build specialized agents for software development and data analysis Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis second edition tackles the biggest challenge facing companies in AI today: moving from prototypes to production. Fully updated to reflect the latest developments in the LangChain ecosystem, it captures how modern AI systems are developed, deployed, and scaled in enterprise environments. This edition places a strong focus on multi-agent architectures, robust LangGraph workflows, and advanced retrieval-augmented generation (RAG) pipelines. You'll explore design patterns for building agentic systems, with practical implementations of multi-agent setups for complex tasks. The book guides you through reasoning techniques such as Tree-of -Thoughts, structured generation, and agent handoffs—complete with error handling examples. Expanded chapters on testing, evaluation, and deployment address the demands of modern LLM applications, showing you how to design secure, compliant AI systems with built-in safeguards and responsible development principles. This edition also expands RAG coverage with guidance on hybrid search, re-ranking, and fact-checking pipelines to enhance output accuracy. Whether you're extending existing workflows or architecting multi-agent systems from scratch, this book provides the technical depth and practical instruction needed to design LLM applications ready for success in production environments.What you will learn Design and implement multi-agent systems using LangGraph Implement testing strategies that identify issues before deployment Deploy observability and monitoring solutions for production environments Build agentic RAG systems with re-ranking capabilities Architect scalable, production-ready AI agents using LangGraph and MCP Work with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI's o3-mini Design secure, compliant AI systems aligned with modern ethical practices Who this book is for This book is for developers, researchers, and anyone looking to learn more about LangChain and LangGraph. With a strong emphasis on enterprise deployment patterns, it’s especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this updated edition expands its reach to support engineering teams and decision-makers working on enterprise-scale LLM strategies. A basic understanding of Python is required, and familiarity with machine learning will help you get the most out of this book.

Multidisciplinary Approach in Research Area (Volume-13)