THE MULTI-AGENT ENGINEER
Building Production-Ready AI SystemsBy Silver Nsaka
Length4h 3m
About this audiobook
The Multi-Agent Engineer is the definitive engineering handbook for building production-ready multi-agent AI systems. Written for AI engineers, ML engineers, and enterprise architects who already understand LLM basics, this book teaches the distributed systems discipline required to take agentic AI from demo to production
Following a complete Media Rights & Content Licensing Engine case study built chapter by chapter, you will learn to:
• Design multi-agent architectures with the Supervisor, Planner-Executor, and Critic-Actor patterns
• Build typed agent communication using Pydantic schemas
• Implement Human-in-the-Loop approval workflows with durable state persistence
• Engineer fault tolerance with retry logic, circuit breakers, and the Dead-Agent Queue
• Evaluate agents with LLM-as-Judge pipelines calibrated against expert judgment
• Secure agents with IAM, prompt injection defense, and sandboxed tool
• Deploy on Kubernetes with event-driven autoscaling
Audiobook details
GenreTechnology
Length4 hrs 3 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateJul 15, 2023
LanguageEnglish
Table of contents
1Introduction
2Preface
3From Prompts to Processes: Why Single LLM Calls Are Not Enough
41.1 The Anatomy of a Broken AI System
51.2 The Limits of Context Windows
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61.3 Compound AI Systems: A New Paradigm
71.4 The State Problem
81.5 The Case Study: Media Rights and Content Licensing Engine
91.6 Chapter Summary
10The Multi-Agent Paradigm: Principles of Production AI
112.1 The Seven Properties of Production Multi-Agent Systems
12Property 1: Composability
13Property 2: Observability
14Property 3: Reliability
15Property 4: Security
16Property 5: Evaluability
17Property 6: Cost Accountability
18Property 7: Governance
192.2 Agent Roles and Responsibilities
202.3 The Evolution from Chatbots to Autonomous Workflows
212.4 The Orchestrator Pattern
222.5 Agent Communication Protocols
232.6 Designing for Failure
242.7 Chapter Summary
25Layers of Intelligence: Anatomy of the Agentic Stack
263.1 The LLM Layer
273.2 The Memory Layer
283.3 The Tool Layer
293.4 The Orchestration Layer
303.5 The Evaluation Layer
313.6 The Observability Layer
323.7 The Security Layer
333.8 Chapter Summary
34Framework Wars: LangGraph, CrewAI, AutoGen, and Semantic Kernel
354.1 LangGraph: Graph-Based Workflow Orchestration
364.2 CrewAI: Role-Based Collaborative Agents
374.3 AutoGen: Conversational Multi-Agent Coordination
384.4 Semantic Kernel: Enterprise Integration Focus
394.5 The Case for Framework Independence
404.6 Chapter Summary
41Stack Selection and Integration Strategy
425.1 The Reference Stack
435.2 The Pydantic Contract
445.3 Integrating the Reference Stack
455.4 Chapter Summary
46Designing Agents: Roles, Capabilities, and Reasoning Strategies
476.1 The Single Responsibility Principle for Agents
486.2 Reasoning Strategies
49Direct Completion
50Chain-of-Thought (CoT)
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