Quick Guide & Tips

💻   Accessing Utils File and Helper Functions

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Open"

You will be able to see all the notebook files for the lesson, including any helper functions used in the notebook on the left sidebar. See the following image for the steps above.

🔄   Reset User Workspace

If you need to reset your workspace to its original state, follow these quick steps:

1:   Access the Menu: Look for the three-dot menu (⋮) in the top-right corner of the notebook toolbar.

2:   Restore Original Version: Click on "Restore Original Version" from the dropdown menu.

For more detailed instructions, please visit our Reset Workspace Guide.

💻   Downloading Notebooks

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Download as"

3:   Then, click on "Notebook (.ipynb)"

💻   Uploading Your Files

After following the steps shown in the previous section ("File" => "Open"), then click on "Upload" button to upload your files.

📗   See Your Progress

Once you enroll in this course—or any other short course on the DeepLearning.AI platform—and open it, you can click on 'My Learning' at the top right corner of the desktop view. There, you will be able to see all the short courses you have enrolled in and your progress in each one.

Additionally, your progress in each short course is displayed at the bottom-left corner of the learning page for each course (desktop view).

📱   Features to Use

🎞   Adjust Video Speed: Click on the gear icon (⚙) on the video and then from the Speed option, choose your desired video speed.

🗣   Captions (English and Spanish): Click on the gear icon (⚙) on the video and then from the Captions option, choose to see the captions either in English or Spanish.

🔅   Video Quality: If you do not have access to high-speed internet, click on the gear icon (⚙) on the video and then from Quality, choose the quality that works the best for your Internet speed.

🖥   Picture in Picture (PiP): This feature allows you to continue watching the video when you switch to another browser tab or window. Click on the small rectangle shape on the video to go to PiP mode.

√   Hide and Unhide Lesson Navigation Menu: If you do not have a large screen, you may click on the small hamburger icon beside the title of the course to hide the left-side navigation menu. You can then unhide it by clicking on the same icon again.

🧑   Efficient Learning Tips

The following tips can help you have an efficient learning experience with this short course and other courses.

🧑   Create a Dedicated Study Space: Establish a quiet, organized workspace free from distractions. A dedicated learning environment can significantly improve concentration and overall learning efficiency.

📅   Develop a Consistent Learning Schedule: Consistency is key to learning. Set out specific times in your day for study and make it a routine. Consistent study times help build a habit and improve information retention.

Tip: Set a recurring event and reminder in your calendar, with clear action items, to get regular notifications about your study plans and goals.

☕   Take Regular Breaks: Include short breaks in your study sessions. The Pomodoro Technique, which involves studying for 25 minutes followed by a 5-minute break, can be particularly effective.

💬   Engage with the Community: Participate in forums, discussions, and group activities. Engaging with peers can provide additional insights, create a sense of community, and make learning more enjoyable.

✍   Practice Active Learning: Don't just read or run notebooks or watch the material. Engage actively by taking notes, summarizing what you learn, teaching the concept to someone else, or applying the knowledge in your practical projects.

📚   Enroll in Other Short Courses

Keep learning by enrolling in other short courses. We add new short courses regularly. Visit DeepLearning.AI Short Courses page to see our latest courses and begin learning new topics. 👇

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🙂   Let Us Know What You Think

Your feedback helps us know what you liked and didn't like about the course. We read all your feedback and use them to improve this course and future courses. Please submit your feedback by clicking on "Course Feedback" option at the bottom of the lessons list menu (desktop view).

Also, you are more than welcome to join our community 👉👉 🔗 DeepLearning.AI Forum

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DeepLearning.AI
Short CourseIntermediate

Nvidia’s NeMo Agent Toolkit: Making Agents Reliable

Instructor: Brian McBrayer

Nvidia logo
  • Intermediate
  • 9 Video Lessons
  • 6 Code Examples
  • Instructor: Brian McBrayer
    • Nvidia
    Nvidia

What you'll learn

  • Build production-ready agents using configuration-driven workflows with Nvidia’s open-source NeMo Agent Toolkit.

  • Built-in observability and evaluation tools help debug agent reasoning, measure performance, and systematically improve reliability.

  • Deploy multi-agent workflows with authentication, rate limiting, and professional interfaces that integrate agents from any framework.

About this course

Join this new short course on Nvidia’s NeMo Agent Toolkit, taught by Brian McBrayer, Solutions Architect in Generative AI at Nvidia.

Many teams struggle to turn agent demos into reliable systems that are ready for production. Nvidia’s open-source NeMo Agent Toolkit (NAT) provides the building blocks you need to harden your agents for production, whether built in raw Python, LangGraph, CrewAI, or any other framework.

NAT makes it easy to add observability, run systematic evaluations, and deploy with production features like authentication and rate limiting. In this course, you’ll build a climate data analysis agent using configuration-driven workflows, add OpenTelemetry tracing to debug agent reasoning, measure performance improvements, and deploy with a professional interface. You’ll also expand to multi-agent workflows where specialized agents built with different frameworks collaborate on complex tasks.

In detail, you’ll:

  • Build your first NAT workflow using configuration-driven development by creating a climate science chatbot, running it locally, and serving it as a REST API with minimal code.
  • Add intelligent features by registering Python functions as tools for analyzing NOAA climate data, defining input schemas with Pydantic, and transforming your chatbot into a ReAct agent.
  • Enable observability with Phoenix tracing to visualize agent reasoning, tool selection decisions, and identify performance bottlenecks for confident debugging.
  • Integrate multiple agents from different frameworks by combining NAT agents with LangGraph subagents using NAT’s framework-agnostic orchestration.
  • Evaluate and improve performance with NAT’s evaluation framework by creating gold-standard datasets, uncovering bugs, and making data-driven improvements through configuration experiments.
  • Deploy to production with NAT UI by configuring authentication and rate limiting, enabling caching, and launching your workflow with REST APIs and a professional web interface.

NAT works directly with frameworks you already use to make agents that are observable, measurable, and deployable. Start building agents that deliver reliable results.

Who should join?

AI builders who want to make their agents production-ready. Basic familiarity with Python and LLM application development is recommended to make the most of this course.

Course Outline

9 Lessons・6 Code Examples

  • Introduction

    Video・3 mins

  • Overview of NAT

    Video・10 mins

  • Your First NAT Workflow

    Video with Code Example・9 mins

  • Adding Intelligence with Tools

    Video with Code Example・16 mins

  • Observability with Phoenix Tracing

    Video with Code Example・7 mins

  • Multi-Agent Integration Adding Math

    Video with Code Example・10 mins

  • Evaluation Finding and Fixing Bugs with NAT Eval

    Video with Code Example・7 mins

  • Production Deployment with NAT UI

    Video with Code Example・4 mins

  • Conclusion

    Video・1 min

  • Quiz

    Graded・Quiz

    ・10 mins

Instructor

Brian McBrayer

Brian McBrayer

Solutions Architect in Generative AI at Nvidia

Additional learning features, such as quizzes and projects, are included with DeepLearning.AI Pro. Explore it today

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Keep learning with updates on curated AI news, courses, and events, as well as Andrew’s thoughts from DeepLearning.AI!