Multi-Agent AI Engineering: Design, build, and operate AI systems that think and act as coordinated teams - Image 1

£41.99

Packt Publishing Multi-Agent AI Engineering: Design, build, and operate AI systems that think and act as coordinated teams

Name: Multi-Agent AI Engineering: Design, build, and operate AI systems that think and act as coordinated teams
Brand: Packt Publishing
Price: 41.99 GBP
Availability: InStock

AI & Machine Learning

It has never been this cheap. We have no record of a lower price.

£42 today · cheaper than every other day in the last 2 months

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 29 days · 29 data points (no recent data)

Historical

Generating forecast…

Price Distribution

Price distribution over 29 days • 1 price levels

Days at Price

Price Analysis

Most common price: £42 (29 days, 100.0%)

Price range: £42 - £42

Price levels: 1 different prices over 29 days

Description

Move agentic AI from clever demos to reliable production with the principles, patterns, and practices behind multi-agent systems at scale. Key Features Build production-ready agents and multi-agent systems with hands-on Python examples Apply foundational principles, proven design patterns, and orchestration strategies Evaluate, observe, scale, and evolve agent systems through real-world case studies Purchase of the print or Kindle book includes a free PDF eBook Book Description As AI systems take on more complex tasks, the limits of single-model applications become increasingly clear. Problems requiring long-horizon reasoning, specialized expertise, coordination, and parallel execution demand multiple agents working together reliably in production. But building multi-agent systems is fundamentally an engineering challenge. Agents must communicate, delegate tasks, manage context, recover from failures, and stay aligned on shared goals under real-world constraints. Multi-Agent AI Engineering is a practical guide to designing and operating production-grade multi-agent systems. Drawing on the authors’ research, open-source contributions, and experience building AI systems at scale, the book focuses on architectural principles that extend beyond any single framework or trend. You’ll explore agent foundations, communication protocols, memory and context management, orchestration, interoperability standards, and canonical multi-agent patterns through hands-on Python examples. The book also covers production realities including evaluation, observability, reliability, safe self-improvement, and scaling agentic systems in practice. By the end, you’ll be equipped to design, build, and scale reliable multi-agent systems for real-world deployment. What you will learn Apply foundational principles to design production-ready agents Design agent communication, routing, and collaboration flows Orchestrate teams with proven multi-agent design patterns Manage memory, retrieval, and context across agent teams Evaluate, red-team, and benchmark agent system behaviors Deploy and scale multi-agent systems in production Instrument agents with OpenTelemetry-based observability Evolve and improve agent systems safely in production Who this book is for If you are an AI engineer, ML practitioner, software architect, or technical leader who wants to move beyond agent demos and ship multi-agent AI systems that work in production, this book is for you. By the end, you will be able to design, deploy, evaluate, and continuously improve agentic systems with confidence. It is equally valuable for engineering and product managers making informed decisions about agentic AI architecture. Readers should be comfortable with Python and have basic familiarity with LLMs; deep ML expertise is not required. Table of Contents Introduction to Multi-Agent Systems Principles of Multi-Agent Systems Frameworks and Mental Models Constructing Your First Agents Agent Communication and Collaboration Context Management in Agents Orchestrating Agent Teams Unified Abstractions and Protocols for Agent Collaboration Design Patterns for Multi-Agent Collaboration Comparative Survey of Frameworks Evaluating the Performance and Behaviors of Multi-Agent Systems Deploying and Scaling Multi-Agent Systems Observability for Agentic AI Case Studies from Real-World Applications Conclusions and Future Outlook

Product Specifications

Brand: Packt Publishing
Format: paperback
ASIN: 180669087X
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 09 September 2026
Listed Since: 21 May 2026

Barcode

No barcode data available

Packt Publishing Multi-Agent AI Engineering: Design, build, and operate AI systems that think and act as coordinated teams

It has never been this cheap. We have no record of a lower price.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Systems Thinking for Agentic AI: A Software Architect’s Guide to Building Reliable LLM and Agent Systems

Agentic AI Unleashed: A guide to designing, building, and deploying autonomous AI systems (English Edition)

Agent Architectures: Design Patterns for Multi-Agent AI Systems - Patterns · Protocols · Coordination · Governance

Model Context Protocol for LLMs: Build secure, scalable, and context-aware AI agents using a standardized protocol

Agentic AI with Microsoft Foundry: Design and develop intelligent AI solutions and autonomous agents with Microsoft's Agent Framework

The Agentic Enterprise: A Leader's Guide to Orchestrating, Governing, and Scaling AI Agent Systems

Generative AI at AWS: Turn business strategy into production-ready AI applications and agents

Redefining Data Engineering with AI: Agentic Engineering Your Way to Data Democracy

Architectures for the Intelligent AI-Ready Enterprise: Building real-world solutions with MongoDB

Observability for Large Language Models: Site Reliability and Chaos Engineering for AI at Scale

Agentic Reliability Engineering: Building Agentic Systems That Think, Adapt, and Recover

Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples

Designing Agentic AI Experiences: UX Strategies for Collaborative, Human-Centered AI

Architecture-Based Design of Multi-Agent Systems

Agentic Spec-Driven Development: A Practical Method for Using AI to Build Complete Specifications for Software, Products, and Knowledge Work

Engineering Intelligent Hybrid Multi-Agent Systems

Intermediate Python and Large Language Models

Building AI Agents for Finance: Design & Deploy Financial AI Agents with Robust Architectures, Advanced Reasoning, and Python

The Most Complete AI Agentic Engineering System: Step-by-step guide to build, optimize, and scale LLM agents—with exclusive monthly and rigorous ... metrics, and built-in self-improvement

Large Language Models in Finance: A hands-on guide to LLM architectures, agents, RAG, governance, and evaluation in finance

Coding with AI: Examples in Python

Cracking AI Engineering Interviews: A Practical Field Guide to Coding, Systems, ML, and Engineering Judgment

Engineering AI Agents for Production: Infrastructure, Observability, Security, and Production Case Studies: 2 (From LLMs to Agent Ecosystems)

AI Engineering and AI Agentic Systems: Designing Intelligent Workflows, Prompting, Autonomous Agents, and Scalable Real-World AI Solutions