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Agent Fundamentals

Google Cloud via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

2 hours
Sessions

Self-Paced
Level

Beginner
Subtitles

English

Found in

Part of

Google Cloud AI Agents: From Foundations to Enterprise Scale

Overview

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This course introduces the fundamentals of AI agents and explores where agents add value in the real world. It provides a foundation for developers, architects, and technical decision-makers who want to understand AI systems through the lens of autonomous, goal-directed behavior.

Syllabus

Welcome
- This module welcomes learners to the course.
Introduction to AI agents
- This module gives a conceptual overview of AI Agents. Discover how AI Agents use autonomous action and reasoning to solve complex problems. You’ll explore the technical architecture—models, tools, and orchestration—that enables agents to learn, plan, and achieve goals on your behalf.
Agent abstraction
- 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.
How agents work
- 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. This powerful combination transforms static language models into autonomous systems capable of continuous, goal-directed action.
Agents in action
- 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.
Course wrap up
- You've completed the foundational course on AI agents 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.