Generative AI has revolutionized how organizations tackle problems, accelerating the journey from concept to prototype to solution. As the models become increasingly capable, we have witnessed a new design pattern emerge: AI agents. By combining tools, knowledge, memory, and learning with advanced foundation models, we can now sequence multiple model inferences together to solve ambiguous and difficult problems. From coding agents to research agents to analyst agents and more, we've already seen agents accelerate teams and organizations. While these agents enhance efficiency, they often require extensive planning, drafting, and revising to complete complex tasks, and deploying them remains a challenge for many organizations, especially as technology and research rapidly develops.

This book is your indispensable guide through this intricate and fast-moving landscape. Author Michael Albada provides a practical and research-based approach to designing and implementing single- and multiagent systems. It simplifies the complexities and equips you with the tools to move from concept to solution efficiently.

• Understand the distinct features of foundation model-enabled AI agents
• Discover the core components and design principles of AI agents
• Explore design trade-offs and implement effective multiagent systems
• Design and deploy tailored AI solutions, enhancing efficiency and innovation in your field

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Spis treści

Building Applications with AI Agents. Designing and Implementing Multiagent Systems eBook -- spis treści

• Preface
  • What This Book Is About
  • What This Book Is Not
  • Who This Book Is For
  • Navigating This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Introduction to Agents
  • Defining AI Agents
  • The Pretraining Revolution
  • Types of Agents
  • Model Selection
  • From Synchronous to Asynchronous Operations
  • Practical Applications and Use Cases
  • Workflows and Agents
  • Principles for Building Effective Agentic Systems
  • Organizing for Success in Building Agentic Systems
  • Agentic Frameworks
    • LangGraph
    • AutoGen
    • CrewAI
    • OpenAI Agents Software Development Kit (SDK)
  • Conclusion
• 2. Designing Agent Systems
  • Our First Agent System
  • Core Components of Agent Systems
  • Model Selection
  • Tools
    • Designing Capabilities for Specific Tasks
    • Tool Integration and Modularity
  • Memory
    • Short-Term Memory
    • Long-Term Memory
    • Memory Management and Retrieval
  • Orchestration
  • Design Trade-Offs
    • Performance: Speed/Accuracy Trade-Offs
    • Scalability: Engineering Scalability for Agent Systems
    • Reliability: Ensuring Robust and Consistent Agent Behavior
      • Fault tolerance
      • Consistency and robustness
    • Costs: Balancing Performance and Expense
      • Development costs
      • Operational costs
      • Cost versus value
  • Architecture Design Patterns
    • Single-Agent Architectures
    • Multiagent Architectures: Collaboration, Parallelism, and Coordination
  • Best Practices
    • Iterative Design
    • Evaluation Strategy
    • Real-World Testing
  • Conclusion
• 3. User Experience Design for Agentic Systems
  • Interaction Modalities
    • Text-Based
    • Graphical Interfaces
    • Speech and Voice Interfaces
    • Video-Based Interfaces
    • Combining Modalities for Seamless Experiences
    • The Autonomy Slider
  • Synchronous Versus Asynchronous Agent Experiences
    • Design Principles for Synchronous Experiences
    • Design Principles for Asynchronous Experiences
    • Finding the Balance Between Proactive and Intrusive Agent Behavior
  • Context Retention and Continuity
    • Maintaining State Across Interactions
    • Personalization and Adaptability
  • Communicating Agent Capabilities
    • Communicating Confidence and Uncertainty
    • Asking for Guidance and Input from Users
    • Failing Gracefully
  • Trust in Interaction Design
  • Conclusion
• 4. Tool Use
  • LangChain Fundamentals
    • Local Tools
    • API-Based Tools
    • Plug-In Tools
    • Model Context Protocol
    • Stateful Tools
  • Automated Tool Development
    • Foundation Models as Tool Makers
    • Real-Time Code Generation
  • Tool Use Configuration
  • Conclusion
• 5. Orchestration
  • Agent Types
    • Reflex Agents
    • ReAct Agents
    • Planner-Executor Agents
    • Query-Decomposition Agents
    • Reflection Agents
    • Deep Research Agents
      • Strengths
      • Weaknesses
  • Tool Selection
    • Standard Tool Selection
    • Semantic Tool Selection
    • Hierarchical Tool Selection
  • Tool Execution
  • Tool Topologies
    • Single Tool Execution
    • Parallel Tool Execution
    • Chains
    • Graphs
  • Context Engineering
  • Conclusion
• 6. Knowledge and Memory
  • Foundational Approaches to Memory
    • Managing Context Windows
    • Traditional Full-Text Search
  • Semantic Memory and Vector Stores
    • Introduction to Semantic Search
    • Implementing Semantic Memory with Vector Stores
    • Retrieval-Augmented Generation
    • Semantic Experience Memory
  • GraphRAG
    • Using Knowledge Graphs
    • Building Knowledge Graphs
    • Promise and Peril of Dynamic Knowledge Graphs
    • Note-Taking
  • Conclusion
• 7. Learning in Agentic Systems
  • Nonparametric Learning
    • Nonparametric Exemplar Learning
    • Reflexion
    • Experiential Learning
  • Parametric Learning: Fine-Tuning
    • Fine-Tuning Large Foundation Models
    • The Promise of Small Models
    • Supervised Fine-Tuning
    • Direct Preference Optimization
    • Reinforcement Learning with Verifiable Rewards
  • Conclusion
• 8. From One Agent to Many
  • How Many Agents Do I Need?
    • Single-Agent Scenarios
    • Multiagent Scenarios
    • Swarms
  • Principles for Adding Agents
  • Multiagent Coordination
    • Democratic Coordination
    • Manager Coordination
    • Hierarchical Coordination
    • Actor-Critic Approaches
  • Automated Design of Agent Systems
  • Communication Techniques
    • Local Versus Distributed Communication
    • Agent-to-Agent Protocol
  • Message Brokers and Event Buses
  • Actor Frameworks: Ray, Orleans, and Akka
  • Orchestration and Workflow Engines
  • Managing State and Persistence
  • Conclusion
• 9. Validation and Measurement
  • Measuring Agentic Systems
    • Measurement Is the Keystone
    • Integrating Evaluation into the Development Lifecycle
    • Creating and Scaling Evaluation Sets
  • Component Evaluation
    • Evaluating Tools
    • Evaluating Planning
    • Evaluating Memory
    • Evaluating Learning
  • Holistic Evaluation
    • Performance in End-to-End Scenarios
    • Consistency
    • Coherence
    • Hallucination
    • Handling Unexpected Inputs
  • Preparing for Deployment
  • Conclusion
• 10. Monitoring in Production
  • Monitoring Is How You Learn
  • Monitoring Stacks
    • Grafana with OpenTelemetry, Loki, and Tempo
    • ELK Stack (Elasticsearch, Logstash/Fluentd, Kibana)
    • Arize Phoenix
    • SigNoz
    • Langfuse
  • Choosing the Right Stack
  • OTel Instrumentation
  • Visualization and Alerting
  • Monitoring Patterns
    • Shadow Mode
    • Canary Deployments
    • Regression Trace Collection
    • Self-Healing Agents
  • User Feedback as an Observability Signal
  • Distribution Shifts
  • Metric Ownership and Cross-Functional Governance
  • Conclusion
• 11. Improvement Loops
  • Feedback Pipelines
    • Automated Issue Detection and Root Cause Analysis
    • Human-in-the-Loop Review
    • Prompt and Tool Refinement
      • Prompt refinement
      • Tool Refinement
    • Aggregating and Prioritizing Improvements
  • Experimentation
    • Shadow Deployments
    • A/B Testing
    • Bayesian Bandits
  • Continuous Learning
    • In-Context Learning
    • Offline Retraining
  • Conclusion
• 12. Protecting Agentic Systems
  • The Unique Risks of Agentic Systems
  • Emerging Threat Vectors
  • Securing Foundation Models
    • Defensive Techniques
    • Red Teaming
    • Threat Modeling with MAESTRO
  • Protecting Data in Agentic Systems
    • Data Privacy and Encryption
    • Data Provenance and Integrity
    • Handling Sensitive Data
  • Securing Agents
    • Safeguards
    • Protections from External Threats
    • Protections from Internal Failures
  • Conclusion
• 13. Human-Agent Collaboration
  • Roles and Autonomy
    • The Changing Role of Humans in Agent Systems
    • Aligning Stakeholders and Driving Adoption
  • Scaling Collaboration
    • Agent Scope and Organizational Roles
    • Shared Memory and Context Boundaries
  • Trust, Governance, and Compliance
    • The Lifecycle of Trust
    • Accountability Frameworks
    • Escalation Design and Oversight
    • Privacy and Regulatory Compliance
  • Conclusion: The Future of Human-Agent Teams
• Glossary
• Index