Название: Building Applications with AI Agents: Designing and Implementing Multiagent Systems (Final Release)
Автор: Michael Albada
Издательство: O’Reilly Media, Inc.
Год: 2025
Страниц: 355
Язык: английский
Формат: True/Retail PDF, EPUB
Размер: 17.2 MB

Generative AI has revolutionized how organizations tackle problems, accelerating the journey from concept to prototype to solution. As the models become increasingly capable, we have witnessed a new design pattern emerge: AI agents. By combining tools, knowledge, memory, and learning with advanced foundation models, we can now sequence multiple model inferences together to solve ambiguous and difficult problems. From coding agents to research agents to analyst agents and more, we've already seen agents accelerate teams and organizations. While these agents enhance efficiency, they often require extensive planning, drafting, and revising to complete complex tasks, and deploying them remains a challenge for many organizations, especially as technology and research rapidly develops.

This book is your indispensable guide through this intricate and fast-moving landscape. Author Michael Albada provides a practical and research-based approach to designing and implementing single- and multiagent systems. It simplifies the complexities and equips you with the tools to move from concept to solution efficiently.

Understand the distinct features of foundation model-enabled AI agents
Discover the core components and design principles of AI agents
Explore design trade-offs and implement effective multiagent systems
Design and deploy tailored AI solutions, enhancing efficiency and innovation in your field

AI agents, at their core, are sophisticated systems designed to interact with their environment, process information, and execute tasks autonomously. To do this efficiently, they rely on a structured set of tools. These tools are modular components that can be developed, tested, and optimized independently, then integrated to form a cohesive system capable of complex behavior. In practical terms, a tool could be as simple as recognizing an object in an image or as complex as managing a customer support ticket from initial contact to resolution. The design and implementation of these tools are critical to the overall functionality and effectiveness of the AI agent. We’ll start with some fundamentals of LangChain, and then cover the different types of tools that can be provided to an autonomous agent, which we will cover in sequence: local tools, API-based tools, and MCP tools.

Who This Book Is For:
This book is for engineers, developers, and technical leaders aiming to build AI agent-based applications. It’s geared toward roles like AI engineers, software developers, ML engineers, data scientists, and product managers with a technical bent. You might relate to scenarios like the following:

• You’re tasked with building an autonomous system for decision support, or interactive services.
• You have a working agent prototype and you want to harden it and get it ready for production.
• Your team struggles with agent reliability—handling failures, adapting to dynamic environments, or orchestrating complex tasks—and you want systematic approaches including orchestration, memory, and learning from experience.
• You’re integrating agents into existing workflows and seek best practices for scalability, multiagent coordination, UX design, measurement, validation, monitoring, and security.

You can also benefit if you’re a tool builder identifying gaps in the agent ecosystem, a researcher exploring applications, or a job seeker preparing for AI agent roles.

Contents: