Guiding Questions
What is agentic AI?
How is it different from the AI we already use?
Why does no-code automation matter?
What will readers learn by the end of this book?
From AI That Responds to AI That Acts
You’ve probably interacted with AI before—asking a chatbot a question, getting a recommendation, or using voice assistants like Siri or Alexa. While these tools feel intelligent, most of them are reactive. They wait for you to tell them what to do. Agentic AI is different. Agentic AI systems are designed to take goals, make plans, use tools, and learn from feedback with minimal human input. In other words, they don’t just respond—they act. Agentic AI in Action explores this shift from simple AI responses to autonomous systems that can reason, decide, and execute tasks on their own.
What Makes an AI “Agentic”?
An AI becomes agentic when it has a few core abilities: understanding goals, remembering past actions, using tools, and evaluating its own performance. Instead of completing one task and stopping, an agent can break a large goal into smaller steps, decide which action to take next, and adjust if something goes wrong. This book walks readers through these ideas step by step, explaining how planning, memory, decision-making, and reflection work together to form intelligent agents that operate in the real world.
Why n8n Changes Everything
One of the biggest barriers to building advanced AI systems has always been complexity. Traditionally, creating autonomous agents required deep programming knowledge, custom infrastructure, and constant maintenance. This is where no-code and low-code tools like n8n come in. n8n allows users to visually design workflows that connect AI models, APIs, databases, and real-world tools. Throughout the book, readers learn how n8n acts as the “control center” for agentic systems, making powerful AI automation accessible to students, creators, and professionals without requiring complex code.
What You’ll Build (Not Just Read About)
This isn’t just a theory book. As the chapters progress, readers move from understanding concepts to building real systems. By the final chapter, the book walks through a full capstone project: AutoPilot AI, an autonomous task execution agent. This agent can accept a high-level goal, break it into tasks, use tools like calendars and web search, store memory, and report results automatically. Along the way, readers learn how to design agents responsibly, monitor their behavior, and decide when human oversight is still necessary.
Why Agentic AI Matters Now
Agentic AI is already shaping how work gets done—from automating business operations to managing personal productivity and research. As these systems become more common, understanding how they work is no longer optional. Agentic AI in Action is written to bridge the gap between curiosity and capability, showing how autonomous AI systems are built, how they behave, and how anyone can start creating them today using tools like n8n.
Review Questions
What is the difference between reactive AI and agentic AI?
What core abilities make an AI system autonomous?
Why does n8n make agentic AI more accessible?
What kind of project will readers build by the end of the book?
How might agentic AI change the way people work and learn?
Recent Comments