How to practice the Ungraded Lab modules of Agentic AI course - Gemini Search Response

Introductions
1

As I was having difficulty to understand how to access and practice the Ungraded modules.
I did my Gemini (Google AI) search and the response helped me practice.

This may help other members in this community too:

  1. The In-Browser Experience (Easiest)

Most DeepLearning.AI courses use an integrated Jupyter Notebook environment. You don’t need to install anything on your computer to start.

  • Access: Navigate to the specific “Ungraded Lab” item in the course sidebar.

  • The “Run” Mentality: Don’t just read the code. Click into each cell and press Shift + Enter to execute it.

  • Experimentation: Since these labs are ungraded, you can’t “break” anything permanently. Change the prompts, swap out the tools the agent uses, and see how the reasoning chain reacts.

2. Setting Up Locally (Best for Long-term Learning)

If you want to keep the code or use it in your own projects, you’ll want to run these modules on your own machine.

Key Steps:

  • Download the Files: In the Jupyter interface, go to File -> Open.... Select the notebook files (.ipynb) and any utility scripts (utils.py) or data folders, then click Download.

  • Environment Setup: Most of these labs use frameworks like LangGraph, CrewAI, or AutoGPT. You’ll need to create a virtual environment:

    Bash

    python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

  • API Keys: You will need your own API keys (OpenAI, Anthropic, or LangChain) to run the agents locally. Create a .env file in your project folder to store them securely.

3. Core Concepts to Focus On

While practicing, keep an eye on the Agentic Workflow loop. Unlike standard LLM calls, agents follow a specific cycle that you should try to visualize while the code runs:

Feature What to Practice
Tool Use Modify the tools provided (e.g., search, calculator) to see how the agent selects them.
Memory Check how the agent stores past interactions to inform future steps.
Reflection Look for “Self-Correction” steps where the agent checks its own work.

4. Troubleshooting Common Issues

  • Kernel Crashing: If the notebook stops responding, go to Kernel -> Restart & Clear Output.

  • Missing Dependencies: If running locally and you see ModuleNotFoundError, check the first cell of the notebook—it usually contains the pip install commands you need.

  • API Limits: If you get “Rate Limit” errors, it’s usually because the agent is looping too fast. Add a small time.sleep() in the loop if you’re using a personal tier API key.

Pro Tip: Open the “utils.py” file often found in the lab file directory. Andrew Ng’s team often hides the “heavy lifting” helper functions there; reading them is the best way to understand how the agent is actually being constructed.