Introduction
Participating in the 5-Day AI Agents Intensive Course by Google and Kaggle was one of the most transformative learning experiences I’ve had in the field of AI. Although the program was short, it was dense, hands-on, and focused on the future of intelligent systems: AI agents.
Coming into the course, I had experience with machine learning and deep learning, but the concept of agentic systems—systems that sense, plan, act, and interact autonomously—was still relatively new. By the end of the program, my perspective completely shifted. I realized that agents are not just “smarter chatbots”—they represent a new generation of autonomous software.
In this post, I’m sharing my key takeaways, insights, and reflections from this intensive journey.
- The Shift From Models to Agents
One of the biggest things I learned was the difference between:
LLMs → tools that generate predictions
AI Agents → systems that think, plan, and execute actions
This shift was reinforced through the course modules, especially in the lessons about agent architectures, tool integration, and multi-step reasoning. I learned how agents can combine perception, memory, tools, and logic to operate more like digital workers than passive models.
This concept really stuck with me and changed how I think about the future of AI applications.
- Architectures That Make Agents Work
The course explained the three major approaches to building agents:
▪ Reactive Agents
Respond immediately to input.
Simple, fast, but limited.
▪ Deliberate Agents
Use planning and multi-step thinking.
Much more capable and reliable.
▪ Hybrid Agents
Mix both approaches for real-world use cases.
Seeing how these architectures map onto practical applications was one of the most helpful parts of the course. It made it clear why agent design matters more than just model choice.
- Tools, Actions, and the ADK
One of my biggest takeaways was how powerful the Agent Development Kit (ADK) is.
Through hands-on labs, I learned how to:
define tools (functions the agent can call)
attach them to an agent
configure instructions and role behavior
deploy the entire system using Vertex AI Agent Engines
This was the moment I felt I was not just using AI, but actually building intelligent systems.
- Deployment: Seeing Agents Come to Life
Day 5 of the course was the highlight for me.
I deployed a fully functioning agent using:
Vertex AI
Agent Engines
ADK configuration files
and a simple production-ready directory structure
This was where everything clicked.
Watching my agent stream responses in real time made the learning feel real and practical.
- My Capstone Project
For my capstone, I built a custom AI agent that combined the key concepts from the course:
Tool calling
Reasoning loops
Structured responses
ADK integration
Real deployment
Working on the project helped me understand the full lifecycle of agent development: from design → to implementation → to deployment.
It also showed me how much potential these systems have in real-world workflows.
- How My Understanding of AI Agents Evolved
Before the course:
I thought agents were “enhanced chatbots.”
After the course:
I understand that agents are autonomous, multi-step digital workers that can operate across tools, APIs, and real environments.
I now see Agentic AI as:
the next major evolution of AI
a bridge between software automation and intelligent reasoning
a foundation for future enterprise and personal applications
- Final Reflections
This intensive course was short but incredibly powerful.
It gave me:
✔ a deep understanding of agentic architectures
✔ hands-on experience with ADK and agent engines
✔ the ability to build functional, deployable agents
✔ confidence to continue exploring advanced agent systems
I’m grateful for the experience and excited to keep building real-world agentic applications.
If you’re interested in AI agents, I highly recommend exploring the course materials and experimenting with the ADK. This field is growing fast—and it’s incredibly exciting to be part of it.
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