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What are the four steps in the agent loop?

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This module introduces the fundamentals of AI agents and why they represent a paradigm shift away from simple LLMs. You’ll learn the difference between language models that can only discuss solutions to agents that can autonomously execute them.

Exercise 1: The problem: The evolution that led us here Exercise 2: The solution: Agent abstraction Exercise 3: Quiz Question 1 Exercise 4: Quiz Question 2

This module reveals the internal architecture that powers every AI agent. You’ll explore how three fundamental components—the model, tools, and orchestration—work together to transform static language models into autonomous systems capable of continuous, goal-directed action.

Exercise 1: The problem: Building intelligence without structure Exercise 2: The solution: Three components working together Exercise 3: Quiz Question 1 Exercise 4: Quiz Question 2

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This module equips you with a practical decision-making framework for determining when agents are the right solution. You'll learn to identify scenarios where agents provide exceptional value, recognize anti-patterns where simpler solutions might be better, and make informed architectural decisions for your specific use case.

Exercise 1: The problem: The right tool for the wrong job Exercise 2: The solution: Clear decision criteria Exercise 3: Quiz Question 1 Exercise 4: Quiz Question 2

You've completed the foundational course on AI agents in just 60 minutes and now possess a clear understanding of what agents are, how they work, and when to use them. This theoretical foundation will serve as your north star as you progress to building real agent systems in upcoming courses.

Exercise 1: Reading list