Recently I joined in the 5-Day AI Agents Intensive mostly out of curiosity. Everywhere I looked, people were talking about “Agents” but I felt like I only had a surface-level understanding of what that actually know it. I figured this course would either clear things up — or confuse me more. Luckily, it did the first one.
My Learning skills
1.What an Agent Actually Is
Before the course, I assumed agents were basically chatbots with extra steps.
Turns out there’s a whole structure behind them.
They’ve got:
- something that plans,
- tools they can use,
- a bit of memory,
- and a loop that lets them check their own work.
Once I saw it in action during the labs, it finally clicked. It’s not magic. It’s more like building a small system that happens to think with a model.
2. Multi-Agent Systems Are Logical
One of the sessions went into how multiple agents can work together.
I didn’t expect this part to be interesting,but it kind of changed how I think about complex tasks.Instead of forcing one model to juggle everything,you split the work:
- one plans
- one does the steps
- one checks things
It felt oddly similar to how actual team projects work.
3. Tools Are the Real Power
This was my biggest “ohhh, I get it now” moment.
Agents get way more useful once they can:
- run a bit of code,
- call an API,
- look something up,
- read a file.
That’s when it stops feeling like a chatbot and starts feeling like software that can help you build things or automate tasks.
- My Capstone Project
For my project, I built a simple goal-planning agent. You gave a task, and it would break it down into smaller steps and figure out what was needed.
Why I choose it:
I’m one of those people who writes lists for everything, so this feels natural.
What went well:
The planning part actually worked better than I expected. Separating the planner and executor helped keep it organized.
What didn’t:
The agent struggled with vague goals and also I had to change the tool definitions a lot because the agent kept calling them wrong.
What I learned:
Building an agent is half creativity and half debugging. Sometimes 80% debugging.
How My Understanding Changed
Before:
“Agents are just advanced chatbots.”
After:
“Agents are basically small systems powered by an AI model that uses tools, memory, and planning.”
It really changed how I think about AI projects. now I understand why people say agents are the next big step.
- Final Thoughts
Overall, I’m really happy I learned the course.It gave me a clearer picture of how agents actually work and how to build something practical with them. I’m planning to keep experimenting, especially with multi-agent workflows, because that part was really excited.
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