Developing Agentic AI: Patterns and Architectures for Autonomous Systems: A Practical Guide for AI Engineers to Build Scalable and Reliable Agents: 2 - Brossura
Libro 2 di 3: AI Agent Mastery Series: Building, Scaling, and Integrating Intelligent AI Systems with Transformers and Context-Aware AI
Sinossi
Master the art of building scalable and reliable autonomous systems with Developing Agentic AI: Patterns and Architectures for Autonomous Systems, a definitive guide tailored for AI engineers. Authored by Ethan Quan, this 14-chapter book explores cutting-edge workflow patterns and architectures, diving into goal decomposition, ReAct reasoning, and reflection loops to create intelligent agentic systems. Learn to implement tool chaining, orchestrate multi-agent collaborations, and manage short-term and long-term memory layers with practical code snippets in Python and TypeScript.
The book addresses scalability strategies like rate limiting and sandbox execution, self-healing deployments, and monitoring for drift with performance optimization techniques. Discover governance models for responsible AI, cost optimization in agentic workflows, and the power of low-code agent factories for rapid development. Enriched with real-world case studies—such as scaling DevOps and customer support agents—this guide bridges the gap from ad-hoc scripts to robust, production-ready solutions. Ideal for AI engineers seeking to design resilient, maintainable agentic systems, this book requires no advanced prerequisites—just a drive to innovate. With concise tutorials and actionable checklists, achieve impactful results quickly.
Key Topics: Agentic systems, workflow patterns, goal decomposition, ReAct reasoning, reflection loops, tool chaining, multi-agent collaboration, memory management, scalability, rate limiting, self-healing deployments, drift monitoring, governance, cost optimization, low-code development, real-world case studies.
Who This Book Is For:
- AI Engineers designing autonomous workflows.
- Developers building scalable agentic architectures.
- Data Scientists optimizing multi-agent systems.
- Product Leaders overseeing AI system reliability.
- No prior agentic system expertise needed—just a passion for AI innovation.
- Comprehensive Approach: From workflow patterns to production-ready systems.
- Practical Insights: Hands-on tutorials and code for immediate application.
- Future-Focused: Explores emerging trends in agentic system design.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
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Developing Agentic AI: Patterns and Architectures for Autonomous Systems: A Practical Guide for AI Engineers to Build Scalable and Reliable Agents
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Paperback. Condizione: new. Paperback. Master the art of building scalable and reliable autonomous systems with Developing Agentic AI: Patterns and Architectures for Autonomous Systems, a definitive guide tailored for AI engineers. Authored by Ethan Quan, this 14-chapter book explores cutting-edge workflow patterns and architectures, diving into goal decomposition, ReAct reasoning, and reflection loops to create intelligent agentic systems. Learn to implement tool chaining, orchestrate multi-agent collaborations, and manage short-term and long-term memory layers with practical code snippets in Python and TypeScript.The book addresses scalability strategies like rate limiting and sandbox execution, self-healing deployments, and monitoring for drift with performance optimization techniques. Discover governance models for responsible AI, cost optimization in agentic workflows, and the power of low-code agent factories for rapid development. Enriched with real-world case studies-such as scaling DevOps and customer support agents-this guide bridges the gap from ad-hoc scripts to robust, production-ready solutions. Ideal for AI engineers seeking to design resilient, maintainable agentic systems, this book requires no advanced prerequisites-just a drive to innovate. With concise tutorials and actionable checklists, achieve impactful results quickly.Key Topics: Agentic systems, workflow patterns, goal decomposition, ReAct reasoning, reflection loops, tool chaining, multi-agent collaboration, memory management, scalability, rate limiting, self-healing deployments, drift monitoring, governance, cost optimization, low-code development, real-world case studies.Who This Book Is For: AI Engineers designing autonomous workflows.Developers building scalable agentic architectures.Data Scientists optimizing multi-agent systems.Product Leaders overseeing AI system reliability.No prior agentic system expertise needed-just a passion for AI innovation.Why Choose This Book?Comprehensive Approach: From workflow patterns to production-ready systems.Practical Insights: Hands-on tutorials and code for immediate application.Future-Focused: Explores emerging trends in agentic system design.Ready to develop scalable, reliable agentic AI? Get Developing Agentic AI: Patterns and Architectures for Autonomous Systems today and revolutionize your AI engineering projects This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9798296018151
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Developing Agentic AI: Patterns and Architectures for Autonomous Systems: A Practical Guide for AI Engineers to Build Scalable and Reliable Agents
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Paperback. Condizione: new. Paperback. Master the art of building scalable and reliable autonomous systems with Developing Agentic AI: Patterns and Architectures for Autonomous Systems, a definitive guide tailored for AI engineers. Authored by Ethan Quan, this 14-chapter book explores cutting-edge workflow patterns and architectures, diving into goal decomposition, ReAct reasoning, and reflection loops to create intelligent agentic systems. Learn to implement tool chaining, orchestrate multi-agent collaborations, and manage short-term and long-term memory layers with practical code snippets in Python and TypeScript.The book addresses scalability strategies like rate limiting and sandbox execution, self-healing deployments, and monitoring for drift with performance optimization techniques. Discover governance models for responsible AI, cost optimization in agentic workflows, and the power of low-code agent factories for rapid development. Enriched with real-world case studies-such as scaling DevOps and customer support agents-this guide bridges the gap from ad-hoc scripts to robust, production-ready solutions. Ideal for AI engineers seeking to design resilient, maintainable agentic systems, this book requires no advanced prerequisites-just a drive to innovate. With concise tutorials and actionable checklists, achieve impactful results quickly.Key Topics: Agentic systems, workflow patterns, goal decomposition, ReAct reasoning, reflection loops, tool chaining, multi-agent collaboration, memory management, scalability, rate limiting, self-healing deployments, drift monitoring, governance, cost optimization, low-code development, real-world case studies.Who This Book Is For: AI Engineers designing autonomous workflows.Developers building scalable agentic architectures.Data Scientists optimizing multi-agent systems.Product Leaders overseeing AI system reliability.No prior agentic system expertise needed-just a passion for AI innovation.Why Choose This Book?Comprehensive Approach: From workflow patterns to production-ready systems.Practical Insights: Hands-on tutorials and code for immediate application.Future-Focused: Explores emerging trends in agentic system design.Ready to develop scalable, reliable agentic AI? Get Developing Agentic AI: Patterns and Architectures for Autonomous Systems today and revolutionize your AI engineering projects This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798296018151
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