Developing Agentic AI: Patterns and Architectures for Autonomous Systems: A Practical Guide for AI Engineers to Build Scalable and Reliable Agents: 2 ... with Transformers and Context-Aware AI) - Tapa blanda

Sinopsis

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.

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