AI Agents and Agentic AI Architecture in Python from Coursera

Forget everything you know about traditional AI. Agentic AI doesn’t just analyze data; it acts on it. From drafting sales emails to updating CRMs, it’s not just a tool—it’s a game-changer. Are you ready to harness its power?

Agentic AI combines Generative AI with tools and actions, enabling the creation of "agents" that revolutionize how work is done. Unlike tools that simply generate static outputs, these agents take action—drafting emails directly in your inbox, updating a CRM with client meeting notes, or analyzing sales data and producing a PowerPoint presentation.

In the fast-paced world of AI-enhanced work, understanding Agentic AI isn’t optional—it’s your competitive advantage. These intelligent agents are reshaping how tasks are executed, decisions are made, and value is created across industries. By automating repetitive workflows and enhancing high-value tasks, they empower teams to focus on innovation and strategy. Agentic AI is transforming industries, and this course will ensure you’re leading the charge, not playing catch-up. Understanding their capabilities and limitations is essential for staying competitive in this rapidly evolving landscape.

This course demystifies Agentic AI, teaching you how these agents work and how to design them to suit your needs. By the end, you’ll not only know how to differentiate genuine AI innovation from overhyped gimmicks but also be equipped to build tools that deliver tangible value. Whether you’re in business, technology, or leadership, this course will help you stay ahead of the curve.

What's inside

Syllabus

Extending AI Agents with Self-Prompting

AI Agent Design Principles & Safety

Multi-Agent Systems

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores agentic AI, which combines generative AI with tools and actions, enabling the creation of agents that revolutionize how work is done

Teaches how to differentiate genuine AI innovation from overhyped gimmicks, equipping learners to build tools that deliver tangible value in various industries

Covers multi-agent systems, which are becoming increasingly important in complex AI applications and require careful design and implementation

Uses Python, a versatile language commonly used in AI development, making it easier to integrate agentic AI into existing projects and workflows

Examines AI agent design principles and safety, which are crucial for responsible development and deployment of AI agents in real-world scenarios

Discusses dependency injection for tools, which is a software design pattern that promotes modularity and testability in AI agent architectures

Reviews summary

Practical introduction to ai agents

According to learners, this course provides a solid introduction to the concepts and architecture of AI agents in Python. Many appreciate the clear explanations and find the practical coding examples helpful for applying the theory. However, some reviewers note that the course lacks depth on more advanced or real-world deployment topics. A few mention encountering outdated or buggy code, highlighting the challenge of keeping content current in this fast-moving field. Learners suggest having prior experience with Python and basic AI concepts is beneficial, as the course may not be suitable for absolute beginners. Overall, it serves as a good starting point but requires further learning for deeper understanding and practical application.

Content may become outdated quickly.

"Subject moves so fast, hard for courses to keep up."

"Requires staying updated outside the course."

"Libraries change constantly."

Hands-on parts help solidify understanding.

"The coding exercises were particularly helpful."

"Applying concepts with code made a difference."

"Liked the practical demonstrations."

Provides a solid base for AI agents.

"Gives a great high-level understanding of agent concepts."

"Solid foundation to get started."

"Covers the core principles well."

Needs prior Python/AI knowledge.

"Not for absolute beginners in Python."

"Should state prerequisites more clearly."

"Found it challenging without prior ML background."

Good intro, but not for advanced learners.

"Wish it went deeper into complex architectures."

"More advanced techniques needed."

"Didn't cover deployment aspects well."

Some examples need updating, may cause issues.

"Ran into problems with the code examples."

"Need to update libraries frequently."

"Code doesn't always work out of the box."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AI Agents and Agentic AI Architecture in Python with these activities:

Review Python Fundamentals

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Strengthen your Python foundation to better understand the code examples and agent implementations used in the course.

Browse courses on Python Programming

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Review basic Python syntax and data structures.
Practice writing simple Python functions.
Familiarize yourself with object-oriented programming concepts in Python.

Review 'Artificial Intelligence: A Modern Approach'

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Expand your knowledge of AI fundamentals to better understand the context of agentic AI.

View Artificial Intelligence: A Modern Approach on Amazon

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Read the chapters on intelligent agents and problem-solving.
Focus on the sections related to planning and decision-making.

Review 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'

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Gain a deeper understanding of the machine learning concepts that underpin many AI agent architectures.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the chapters on neural networks and deep learning.
Experiment with the code examples provided in the book.

Four other activities

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Show all seven activities

Build a Simple Task Automation Agent

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Apply the concepts learned in the course by building a practical AI agent that automates a simple task, such as sending emails or updating a spreadsheet.

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Define a specific task to automate.
Design the agent's architecture and choose appropriate tools.
Implement the agent in Python using relevant libraries.
Test and refine the agent's performance.

Create a Blog Post on AI Agent Safety

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Reinforce your understanding of AI agent safety principles by writing a blog post that explains the key considerations and best practices.

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Research the ethical considerations of AI agents.
Outline the key points to cover in your blog post.
Write a clear and concise blog post explaining AI agent safety.
Publish your blog post on a platform like Medium or your personal website.

Design a Multi-Agent System Diagram

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Visualize the architecture of a multi-agent system by creating a diagram that illustrates the interactions between different agents.

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Choose a specific multi-agent system scenario.
Identify the different agents involved and their roles.
Create a diagram that shows the interactions between the agents.
Add annotations to explain the diagram.

Contribute to an Open Source AI Agent Project

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Gain practical experience by contributing to an open-source project related to AI agents, such as fixing bugs, writing documentation, or adding new features.

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Find an open-source AI agent project on GitHub.
Review the project's documentation and code.
Identify a bug to fix or a feature to add.
Submit a pull request with your changes.

Career center

Learners who complete AI Agents and Agentic AI Architecture in Python will develop knowledge and skills that may be useful to these careers:

AI Automation Engineer

An AI Automation Engineer leverages AI to automate processes and workflows, improving efficiency and reducing costs. This course is extremely useful for enabling engineers to create intelligent automation solutions. The course demystifies Agentic AI, teaching you how these agents work and how to design them to suit specific needs. By taking this course, one interested in becoming an AI Automation Engineer can begin to master technologies with tangible value. You also learn how to build tools, combine Generative AI with tools and actions, and enable the creation of agents that revolutionize how work is done.

See salaries and explore the career path for AI Automation Engineer

AI Product Manager

As an AI Product Manager, you will define the vision, strategy, and roadmap for AI-powered products. This course may be useful because it demystifies Agentic AI and shows how these agents work and how to design them to suit your needs. In particular, knowing how to separate true AI innovation from hype helps one to navigate the product landscape. You can then build tools that deliver real value. This course, focused on Agentic AI, teaches you how to build AI agents, combining generative AI with tools and actions, which is a critical skill for any AI Product Manager today.

See salaries and explore the career path for AI Product Manager

AI Solutions Architect

An AI Solutions Architect designs and implements AI solutions for businesses, focusing on scalability, reliability, and performance. This course helps deepen your understanding of Agentic AI, which is essential for designing effective AI solutions. The course provides insights into how these agents work and how to design them, ensuring that you're equipped to build and deploy tailored solutions. This course's syllabus covers AI agent design principles and safety, which are important when architecting AI solutions. Understanding multi agent systems is also useful. You can use this course to build solutions for businesses that are efficient, scalable, and reliable.

See salaries and explore the career path for AI Solutions Architect

AI Strategist

An AI Strategist helps organizations determine how to best leverage AI to achieve their business goals. This course may be useful because it provides a deep understanding of Agentic AI, which is a critical component of many AI strategies. The course teaches you how the agents work and how to design them. The capabilities and limitations of AI agents covered in this course is essential knowledge for any AI strategist. Furthermore, this course equips you to distinguish genuine AI innovation from overhyped gimmicks, ensuring strategic decisions are grounded in reality.

See salaries and explore the career path for AI Strategist

AI Consultant

AI Consultants advise businesses on how to implement AI solutions to improve their operations and gain a competitive advantage. This course helps deepen your understanding of Agentic AI, enabling you to provide more informed and effective consulting services. You will be better placed to advise your clients. The course teaches you how these agents work and how to design them, and it equips you to differentiate genuine AI innovation from overhyped gimmicks. With the knowledge gained from this course, you can assist businesses in designing and implementing AI agents specifically tailored to their unique needs and challenges.

See salaries and explore the career path for AI Consultant

Generative AI Specialist

The Generative AI Specialist creates new content, such as text, images, and audio, using generative AI models. This course explores Agentic AI, which combines generative AI with tools and actions. This is the kind of advanced knowledge specialists need. The course arms learners with the ability to design agents that draft emails directly in your inbox, update a CRM with client meeting notes, or analyze sales data and produce a PowerPoint presentation. This knowledge can give the generative AI specialist a competitive edge.

See salaries and explore the career path for Generative AI Specialist

Machine Learning Engineer

The Machine Learning Engineer develops and implements machine learning models for various applications. This course provides a practical understanding of Agentic AI, which uses machine learning in a novel way. The Machine Learning Engineer will be well-served to understand how these agents work and how to design them for specific applications. This course, focused on the principles of Agentic AI, helps prepare one to design and build machine learning models that are more strategic than those that simply generate static outputs.

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Data Scientist

Data Scientists analyze large datasets to extract insights and develop data-driven solutions. This course is relevant to anyone who wants to expand their expertise into the cutting-edge area of Agentic AI. This course may be useful as it focuses on how AI agents act on data. You'll learn how to differentiate genuine AI innovation from overhyped gimmicks, which is critical for data-driven decision-making. This understanding can significantly enhance a data scientist's ability to develop more strategic and impactful solutions.

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Software Developer

The Software Developer designs, develops, and tests software applications. This course teaches how to build AI agents, which can be integrated into various software applications. This course may be useful, especially for those who want to leverage AI in their software development projects. The course focuses on Agentic AI. In particular, learning the principles of AI agent design and safety, alongside tools for dependency injection, are skills that software developers can use to gain a competitive edge.

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Digital Transformation Manager

A Digital Transformation Manager leads initiatives to integrate digital technology into all areas of a business. This course may be useful for understanding and implementing AI solutions as part of digital transformation efforts. The course demystifies Agentic AI and teaches how the agents work. This background may provide managers with the knowledge needed to make informed decisions about AI investments. Staying ahead of the curve is vital for anyone driving digital transformation.

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Business Intelligence Analyst

The Business Intelligence Analyst analyzes business data to identify trends and provide actionable insights. This course may be useful by providing insight into how AI agents analyze information to help improve decision making. The course goes beyond just analyzing data; it teaches how to create agents that can take actions such as drafting emails or updating CRMs, something that can add tremendous value to business intelligence operations. By learning the capabilities and limitations of Agentic AI, you can enhance your analytical skills.

See salaries and explore the career path for Business Intelligence Analyst

Data Analyst

Data Analysts collect, clean, and analyze data to provide insights and support decision-making. This course may be useful by broadening one's toolset to include AI agents for data analysis. By teaching how these agents work and how to design them, the course may help improve analytical insights. Additionally, this course equips you with the ability to differentiate genuine AI innovation from overhyped gimmicks, which can improve decision-making. The course focuses on Agentic AI.

See salaries and explore the career path for Data Analyst

Research Scientist

Research Scientists conduct research to advance scientific knowledge in various fields. This course may be useful for researching Agentic AI and its potential applications. By demystifying Agentic AI and teaching how these agents work, the course may help contribute to the advancement of AI technology. The syllabus includes multi agent systems, which is important for researching the interactions between multiple AI entities. Also, the course covers approaches to improving AI agent reasoning, which is relevant for advancing the state of AI.

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Chatbot Developer

Chatbot Developers create and maintain conversational AI systems that interact with users. This course is relevant to chatbot development because it teaches how to create AI agents that can automate tasks and take actions. The course may be useful by providing a unique perspective on how to build more sophisticated chatbots. This includes chatbots that can draft emails, update CRMs, and analyze data. You can differentiate yourself from other chatbot developers by understanding how to build AI agents that are more than just conversational interfaces.

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Robotics Engineer

Robotics Engineers design, develop, and test robots and robotic systems for various applications. This course is relevant to robotics because it focuses on AI agents, which can control robots. The course provides a deep understanding of how AI agents work, how to design them, and how to integrate them with tools and actions. With the knowledge gained from this course, you can build robots that are more autonomous and adaptive. Furthermore, the syllabus covers approaches to improving AI agent reasoning, which is important for engineering more effective robotic systems.

See salaries and explore the career path for Robotics Engineer

AI Agents and Agentic AI Architecture in Python

What's inside

Syllabus

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Reviews summary

Practical introduction to ai agents

Activities

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