Building Applications with AI Agents. Designing and Implementing Multiagent Systems Michael Albada

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Spis treści książki

• 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

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