My Experience with Google-Kaggle AI agents Intensive Course

Source: Dev.to

My Learning Highlights

1. What an Agent Actually Is

Before the course I assumed agents were basically chatbots with extra steps.
In reality, an agent includes:

• a planning component
• tools it can use
• a bit of memory
• a loop that lets it check its own work

Seeing this in action during the labs made it click: it’s not magic, just a small system that thinks with a model.

2. Multi‑Agent Systems Are Logical

One session covered how multiple agents can collaborate:

• one plans
• one executes the steps
• one checks the results

The structure felt oddly similar to how real‑world team projects operate.

3. Tools Are the Real Power

This was my biggest “aha” moment.
Agents become truly useful once they can:

• run a bit of code
• call an API
• look something up
• read a file

At that point they stop feeling like a chatbot and start behaving like software that can help you build things or automate tasks.

My Capstone Project

I built a simple goal‑planning agent. You give it a task, and it breaks the task down into smaller steps and determines what resources are needed.

• Why I chose it: (details omitted)
• What went well: (details omitted)
• What didn’t: (details omitted)
• What I learned: (details omitted)

How My Understanding Changed

• Before: “Agents are just advanced chatbots.”
• After: “Agents are small systems powered by an AI model that uses tools, memory, and planning.”

This shift reshaped how I think about AI projects and why many consider agents the next big step.

Final Thoughts

Overall I’m very happy with what I learned. The course gave me a clearer picture of how agents work and how to build something practical with them. I plan to keep experimenting, especially with multi‑agent workflows, because that part was especially exciting.