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AI agents are moving beyond simple chatbots. They can plan tasks, use tools, interact with APIs, work with data, coordinate with other agents, and automate real-world business workflows-but building reliable agents requires much more than connecting an LLM to a prompt.AI Agents at Work is a practical engineering guide to designing, building, testing, securing, and deploying modern AI agents for real-world applications.
Rather than focusing on hype, this book approaches agentic AI as a software engineering discipline. You will learn how agents reason through tasks, maintain state and memory, call external tools, coordinate specialized agents, interact with humans, and operate safely within production environments.
Inside the book, you will build 30 practical projects covering the complete journey from foundational agent concepts to production-ready systems.
You will learn how to:
• Understand the architecture and execution loop of AI agents
• Design goal-oriented, planning, routing, and supervisor-based agents
• Build agents that use APIs, databases, files, email, and external systems
• Implement memory, state management, retries, validation, and error handling
• Build multi-agent systems with specialized roles and coordinated workflows
• Design human-in-the-loop approval and escalation mechanisms
• Evaluate agent performance using measurable testing methodologies
• Implement observability with logs, metrics, and traces
• Protect agents against prompt injection, tool abuse, data leakage, and excessive agency
• Manage latency, cost, reliability, and production failure scenarios
• Deploy and scale production-oriented AI agent systems
The book progresses from AI agent fundamentals → architecture → tool integration → multi-agent systems → production engineering, allowing you to build your knowledge progressively while working through practical implementations.
Whether you are an AI engineer, software developer, automation specialist, cloud engineer, DevOps practitioner, or technical entrepreneur, this book provides a practical foundation for turning LLM-powered agents into dependable software systems.
Build agents. Connect tools. Coordinate intelligence. Engineer for reliability.
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