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Most common price: £58 (60 days, 100.0%)

Price range: £58 - £58

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Description

Single-agent AI systems and chatbots perform well in narrow, controlled interactions—but they break down when faced with real-world complexity. Long-running tasks, coordination across domains, evolving context, and the need for accountability quickly expose the limits of isolated models. As organizations push AI beyond demos and experiments, the challenge is no longer how to prompt a model, but how to engineer systems of agents that can work together reliably over time. Engineering Multi-Agent AI Systems addresses this shift head-on, treating agentic AI not as a novelty, but as a serious software engineering problem. Real-world AI systems must operate under uncertainty, interact with tools and data sources, recover from failure, and remain observable and governable. These requirements cannot be met by single agents acting in isolation. They demand architectures that support coordination, memory, orchestration, and control. This book provides a structured, engineering-driven approach to designing multi-agent AI systems. It explains how agents can be decomposed into roles, orchestrated using proven patterns, and supported by infrastructure that enables asynchronous execution, evaluation, and resilience. Rather than focusing on model internals or prompt tricks, the book emphasizes system-level design: how agents communicate, how decisions are traced, how failures are handled, and how humans remain in the loop. Each chapter builds on the last, guiding readers from foundational concepts to production-oriented architectures and real-world applications. By reading this book, you will learn how to: Design AI agents as predictable, testable software components Choose and implement orchestration patterns for coordinating multiple agents Build memory, tooling, and infrastructure that support long-running, real-world tasks Evaluate and monitor non-deterministic agent behavior Apply governance, constraints, and human oversight without crippling autonomy Move from experimental prototypes to reliable, scalable agentic systems Whether you are building research pipelines, software development agents, operational automation, or decision-support systems, this book equips you with the frameworks and mental models needed to engineer AI systems that are robust, maintainable, and accountable. If you are a software engineer, AI practitioner, system architect, or technical leader ready to move beyond single-agent tools and design AI systems that work in the real world, Engineering Multi-Agent AI Systems provides the guidance to do it responsibly and effectively. Start building AI systems as engineered systems—not experiments—and design agentic architectures that can scale, adapt, and endure.

Product Specifications

Format: hardcover
ASIN: B0GGHH18K5
Category: Books > Subjects > Computing & Internet > Computer Science > Information Systems
Domain: Amazon UK
Release Date: 13 January 2026
Listed Since: 13 January 2026

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Engineering Multi-Agent AI Systems: Designing, Orchestrating, and Governing Reliable AI Agents for Real-World Applications

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Engineering Multi-Agent AI Systems: Designing, Orchestrating, and Governing Reliable AI Agents for Real-World Applications

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