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Multi-Agent Systems
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Outline
1. Understanding the Evolution of AI
1.1 The Rise of AI Tools and Their Limitations in Solving Complex Problems
1.2 Why Autonomy Matters
1.3 Moving Beyond Traditional AI
2. Unlocking the Potential of Multi-Agent AI in Your Organization
2.1 How Multi-Agent AI Transforms Workflows and Drives Efficiency
2.2 Mapping Enterprise Needs to Multi-Agent Capabilities
2.3 Overcoming Resistance
3. Deciding When Multi-Agent AI Is the Right Solution
3.1 Identifying High-Value Problems Suited to Multi-Agent Systems
3.2 Understanding the Trade-offs of Operational Overhead vs. Scalability
3.3 Learning from Failure
4. Designing Modular Systems for Adaptability and Scale
4.1 Breaking Down Complex Problems into Agentic Workflows
4.2 Designing Agents to Specialize Without Rigid Dependencies
4.3 Achieving Scalability Through Distributed and Asynchronous Architectures
5. Building Agentic Applications That Align with Business Goals
5.1 Crafting Agent Networks That Integrate with Enterprise Systems
5.2 Harnessing Modularity to Customize Solutions
5.3 Measuring Success with KPIs and Metrics
AI Platforms With Multi-Agent Systems
Welcome! We’re incrementally publishing sections of the book here as they’re written.