Introduction
When I joined the Google & Kaggle AI Agents Intensive program, I had only a basic idea of what AI agents really were. I assumed they were similar to chatbots with additional logic. But the course helped me realize that agents are much more powerful.
Through structured lessons, hands-on labs, and community discussions, I learned how agents use tools, memory, planning, and reasoning to operate like autonomous problem-solving systems.
This article summarizes the concepts that resonated most with me, how my understanding evolved, and a small capstone-style project I built inspired by the course.
Key Concepts That Reshaped My Thinking
- Tool Calling: Turning LLMs Into Action-Takers
One of the most impactful ideas was tool calling—the ability for agents to use external tools such as search, APIs, or calculators.
This shifted my perspective from “LLMs generate text” to “agents can act in the real world.”
I realized agents can:
fetch live information
automate tasks
run computations
connect to external applications
Tool calling made AI feel practical and powerful.
- Planning & Reasoning: Breaking Down Complex Tasks
The idea that an agent can plan its steps, reason through tasks, and iteratively refine its approach was eye-opening.
Learning about:
task decomposition
chain-of-thought reasoning
iterative refinement
helped me appreciate how agents move beyond simple Q&A and into real decision-making.
- Multi-Agent Collaboration: AI Teams Working Together
The multi-agent examples demonstrated how different agents can collaborate with unique roles—planner, researcher, evaluator, executor.
This highlighted how agent systems can scale and solve more complex problems through distributed intelligence.
How My Understanding of AI Agents Evolved
Before the course:
I believed agents were just upgraded chatbots.
After the course:
I now understand agents as autonomous systems with:
goals
memory
reasoning loops
tool access
structured decision-making
I realized that agents can act, not just respond — and that’s a huge shift.
My Mini Capstone Project: A Personal Study Assistant Agent
Even though it was a simple concept, building a small study assistant agent helped me apply everything I learned.
What the agent does:
Takes a topic (e.g., “Explain Cloud Computing”)
Uses a search tool to gather information
Summarizes the data
Generates examples and quick notes
Creates MCQs for revision
Saves the notes via a small memory system
Why this project mattered:
This project demonstrated how planning, retrieval, summarization, and memory work together in an agent pipeline.
It also showed me how even simple agents can provide real value when designed well.
Key Learnings from Building the Project
A clear agent persona is essential
Memory significantly improves usefulness
Tool calling turns an agent into a functional assistant
Evaluation and iteration matter for quality
This experience gave me confidence to build more advanced agents in the future.
Final Reflection
The AI Agents Intensive strengthened my foundational understanding of agentic AI.
It transformed my mindset from “AI answers questions” to “AI can autonomously solve problems.”
What I’m excited about next:
Building agents that integrate real APIs
Creating productivity tools
Designing multi-agent systems
Applying agentic AI to real-world workflows
This challenge inspired me to continue exploring and innovating with agent systems.
Thank You
A huge thanks to Google, Kaggle, and DEV for creating this program and providing the opportunity to learn and reflect.
This journey has motivated me to keep improving and contributing to the world of agentic AI.
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