Practical Multi Agent AI Systems: How to Architect, Build, and Scale Next Generation Systems That Work in the Real World

A practical guide to building multi-agent AI systems that earn trust in production In this hands-on guide to production agentic AI, Murali Kashaboina explains how to design, build, and operate hierarchical multi-agent systems that are reliable enough for regulated enterprise environments. He demonstrates the complete lifecycle from choosing the right architecture pattern through deployment to the operational concerns that surface on day two, using an example customer service platform that handles real queries against a real Knowledge Graph with real security controls. Practical Multi-Agent AI Systems walks you through building a system with five specialist agent teams, 20+ worker agents, and multiple agentic tools, orchestrated by LangGraph with MCP tool servers and A2A agent-to-agent delegation. But unlike resources that stop at implementation, this book devotes equal attention to what happens after deployment. You’ll learn how to trace a wrong answer across five agents using a Context Graph integrated with LangFuse observability, how to classify failures into actionable categories, and how to build feedback loops that improve agent behavior using few-shot examples from production without fine-tuning the LLM. You’ll also find targeted coverage of the operational concerns most books skip entirely: context compression across multi-turn conversations, provider failover with circuit breakers, token budget enforcement, prompt injection through tool outputs, and a systematic evaluation framework using DeepEval that makes non-deterministic systems testable. Perfect for AI engineers, solution architects, and engineering leaders building agentic systems where accuracy, auditability, and security are non-negotiable, this book is an indispensable resource for every practitioner who wants to learn how to: Select and implement multi-agent architecture patterns with clear trade-off analysis Engineer context across agent boundaries using a seven-category taxonomy Enforce production security with mTLS, encryption, RBAC, guardrails, and PII scrubbing Build unified observability that shows the actual LLM prompt and response at every decision point Diagnose and fix failures using a structured taxonomy and inline trace cards Test non-deterministic systems with statistical evaluation and measurable quality baselines Build production-grade multi-agent AI systems with LangChain, LangGraph, and MCP Practical Multi-Agent AI Systems: How to Architect, Build, and Scale Next-Generation AI Systems That Work in the Real World walks through a complete, production-grade multi-agent system as a continuous project example. Using LangChain, LangGraph, MCP, A2A, and language models from OpenAI, Anthropic, and Amazon Bedrock, the book covers knowledge retrieval, personalized response generation, escalation orchestration, error handling, controls to secure multi-agent AI systems, integration testing and model evaluations, and deployment considerations with real, runnable code designed for practitioners. Each chapter pairs architectural insights with hands-on implementation, covering patterns including ReAct, Supervisor-Driven Network, Hierarchical Network, Tree-Of-Thought, Chain-Of-Agents, Sequential Orchestration, Semantic Consensus, Hand-Off Orchestration, and Magentic Orchestration. All code examples are available through an online source code repository, allowing readers to clone, run, and experiment with the full solution as they progress. You'll also discover: AI-driven planning, reasoning, and orchestration strategies purpose-built to fine-tune multi-agent behavior, optimize system performance, and ensure reliable execution in production environments System prompt engineering, role definition, actions and tools selection, and memory management techniques specific to multi-agent architectures Context engineering approaches for precise and concise context tuning that directly affect agent output quality and reliability Architectural decision guidance for choosing the right mix of orchestration patterns to fit specific real-world use cases Comprehensive observability and real-time monitoring of end-to-end agentic AI interactions covering LLM invocations, tool executions, and action flows, enhanced with contextual knowledge graphs and full traceability for production-grade transparency and control Robust technologies and enterprise-ready frameworks purpose-built to design, deploy, and scale production-grade multi-agent AI systems with reliability, security, and performance at their core Fail-safe integration and root cause analysis techniques for diagnosing failures across systems with many moving parts Written for AI engineers, enterprise architects, software developers, and technical leaders tasked with deploying agent systems, Practical Multi-Agent AI Systems delivers the architectural rationale, pattern selection guidance, and runnable code needed to build multi-agent AI solutions that handle real-world complexity at scale.

Pagina's: 544, Editie: Eerste editie, Paperback, Wiley