My Learning Journey from the 5-Day AI Agents Intensive Course by Google + Kaggle
Curious about what modern AI agents can do, I joined the AI Agents Intensive Course by Google and Kaggle. I had a very busy schedule and initially did not plan for deep hands-on work, but still went through the materials, examples, and discussions. Even such a light approach gave me a strong conceptual understanding of how AI agents work and why they are becoming such an important part of the future of autonomous systems.
Day-by-Day Experience
Day 1 — Foundations of AI Agents
The basic structure of an AI agent was introduced on the first day: how it observes, decides based on feedback, and acts. This is the first time I have really understood the internal loop of an agent; it made it very clear.
Day 2 — Tools and Architectures
I learned about various agent architectures, vector databases, and what role prompt templates play. This helped me understand how frameworks like LangChain or agentic workflows actually support agent behaviour in real-world systems.
Day 3 — Agent Workflows
This section focused on reasoning, planning, and multi-step processes. I followed how an agent approaches a task by breaking it into steps, evaluating progress, and refining actions until it reaches a goal.
Day 4 — Autonomy and Use Cases
It was on this day that real-world applications came into clearer view. Examples of browsing agents, research agents, and autonomous assistants showed me how those systems actually worked in a practical context, behind the scenes.
Day 5 — Best Practices
The last day covered safety, evaluation, prompt design, and reliability practices. This gave me a sense of how professionals build trustworthy and stable agent systems.
Key Learnings
Even without doing every hands-on activity, I learned several important concepts:
An AI agent is not just a chatbot; it is a system that can take actions.
Agents operate by means of a continuous cycle of observation, thinking, and action.
Multi-step reasoning and planning make agents powerful, as well as useful.
Tools such as vector databases, RAG, and structured prompts dramatically increase performance.
Any real-world deployment does require safety and evaluation.
What I Want to Build Next
Once the basics are clear, I intend to try:
A simple personal assistant agent A research agent that can summarize information Basic autonomous task workflows using notebooks
This course gave me enough confidence to start trying small projects on my own.
Conclusion Although I joined the course for mere curiosity, this course helped me to gain clarity about the very fundamentals of an AI agent through structured materials. Easy to follow at one's own pace, the concepts were simple and practical. It motivated me to actually explore the world of agent development and experiment with my own small systems. Thanks go out to Google, Kaggle, and DEV, who have made this learning experience accessible and engaging.
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