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Are you ready to build the next generation of intelligent, autonomous systems? The world of AI is moving beyond simple chatbots and into powerful, multi-purpose agents that can automate complex workflows and solve real-world problems. The Google Agent Development Kit (ADK) is the cutting-edge framework that makes this a reality.

In this comprehensive, hands-on course, you will go from a beginner with basic Python knowledge to a proficient AI agent developer. We will demystify the ADK framework and its core components, including Agents and Tools with practical, project-based learning.

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Are you ready to build the next generation of intelligent, autonomous systems? The world of AI is moving beyond simple chatbots and into powerful, multi-purpose agents that can automate complex workflows and solve real-world problems. The Google Agent Development Kit (ADK) is the cutting-edge framework that makes this a reality.

In this comprehensive, hands-on course, you will go from a beginner with basic Python knowledge to a proficient AI agent developer. We will demystify the ADK framework and its core components, including Agents and Tools with practical, project-based learning.

We start with the fundamentals, guiding you through setting up your development environment and building your first intelligent agent. You will then learn how to extend your agent's capabilities by creating custom tools the key to connecting your agent to any external API or data source. Imagine an agent that can book flights, manage your calendar, or analyze financial data, all by using the tools you create.

As you progress, you will dive into more advanced topics. We will cover the design and implementation of sophisticated multi-agent systems, where multiple specialized agents collaborate to solve a single, complex task. You'll master orchestration patterns, from sequential to parallel and loop-based agent interactions, giving you the power to tackle any challenge. We will also explore advanced concepts like structured outputs, persistent memory, and the powerful "agent-as-a-tool" pattern, which allows agents to call other agents.

By the end of this course, you will not only have a deep theoretical understanding of the Google ADK but also a portfolio of projects demonstrating your practical skills. You will be equipped with the knowledge to build, deploy, and scale production-ready AI agents, positioning you at the forefront of the AI revolution.

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What's inside

Learning objectives

  • Gain a foundational understanding of the google agent development kit (adk) and its core components, including agents, tools, sessions, and runners.
  • Set up and configure a local development environment for adk.
  • Build a basic intelligent agent to perform a specific task.
  • Develop multi-agent systems and understand orchestration patterns like sequential, parallel, and loop agents.

Syllabus

Introduction to Agentic AI
What is Agentic AI?
Difference between AI vs GenAI vs Agentic AI

The Quiz has questions on Agentic AI and its differences from traditional AI and Deep Learning.

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The Quiz has questions from the lessons LlmAgents and WorkflowAgents.

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Activities

Coming soon We're preparing activities for AI Agents with Google ADK: The Practical Guide. These are activities you can do either before, during, or after a course.

Career center

Learners who complete AI Agents with Google ADK: The Practical Guide will develop knowledge and skills that may be useful to these careers:

Reading list

We haven't picked any books for this reading list yet.
Provides a gentle introduction to machine learning, focusing on the most important concepts and algorithms. It good choice for readers who are new to the field.
Comprehensive guide to deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It must-read for anyone who wants to learn about deep learning.
Classic introduction to reinforcement learning, covering topics such as Markov decision processes, value functions, and Q-learning. It valuable resource for anyone who wants to learn about reinforcement learning.
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as Bayesian inference, neural networks, and support vector machines.
Provides a gentle introduction to AI, focusing on the most important concepts and algorithms. It good choice for readers who are new to the field.
Dives deep into the complexities of systems with multiple interacting agents. It covers algorithmic, game-theoretic, and logical foundations, which are crucial for understanding how agents behave and coordinate in complex environments. It valuable reference for those looking to deepen their understanding beyond single-agent systems.
Reinforcement learning key paradigm for developing intelligent agents that can learn to make sequential decisions by interacting with their environment. is the classic text on the subject, providing a comprehensive introduction to the core concepts and algorithms used in training agents. It must-read for anyone focusing on learning agents.
Provides a solid introduction to the field of multiagent systems, covering key concepts, architectures, and applications. It's more accessible than some of the deeper theoretical texts and serves as an excellent starting point for understanding the principles behind multiple interacting intelligent agents.
Delves into the logical foundations for reasoning about the properties and behavior of rational agents, particularly focusing on the Belief-Desire-Intention (BDI) model. It is more theoretical and suited for those who want to understand the formal underpinnings of agent systems.
This textbook presents AI as the study of intelligent computational agents, providing a unified vision of the field's foundations. It covers a wide range of AI topics through the lens of agents, making it highly relevant for understanding the subject broadly. The latest edition includes updates on recent AI advances like deep learning.
Offers a practical approach to designing and implementing single and multi-agent systems, particularly in the context of generative AI. It helps bridge the gap between theoretical concepts and real-world deployment of AI agents. It is highly relevant for understanding contemporary applications.
Focusing on building LLM-powered autonomous agents, this book provides a practical framework for developing agents that can handle real-world tasks. It covers using tools like the OpenAI Assistants API and LangChain, making it very relevant for contemporary agent development.
Provides a comprehensive overview of AI, covering topics such as machine learning, natural language processing, and computer vision. It is also written in a clear and concise style, making it accessible to readers of all levels.

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