From User to Builder: My Honest Review of Google's 5-Day AI Agents Intensive Course with Kaggle
Source: Dev.to
Course Overview
Over the past five days, I immersed myself in Google and Kaggle’s AI Agents Intensive, a hands‑on learning sprint designed to help participants understand, build, and deploy AI agents using practical tools and real‑world challenges. What began as curiosity quickly evolved into a structured, insightful journey into the future of intelligent automation.
Day 1 — Foundations of AI Agents
- Core concepts: what AI agents are, how they perceive, reason, and act.
Day 2 — Tools, Frameworks & Notebook Walkthroughs
- Explored the practical ecosystem behind AI agents.
- Interacted with agent toolsets, modified simple agent behaviors, and experimented with prompt engineering for task optimization.
- First real taste of building—not just learning.
Day 3 — Building My First Agent
- Defined agent goals, provided tools and constraints, evaluated the agent’s reasoning trace, and refined its behavior through iterative feedback.
- Seeing the agent complete tasks end‑to‑end felt incredibly rewarding.
Day 4 — Advanced Agent Workflows & Optimization
- Chained tools, added memory and state, improved reasoning patterns, and used evaluation benchmarks from Kaggle.
- Shifted mindset from user to system designer.
Day 5 — Capstone Challenge & Reflection
- Completed a mini‑project: created an agent capable of solving a realistic problem with minimal intervention.
- Reflected on how autonomous, goal‑driven systems can collaborate with humans to streamline tasks, explore data, and solve meaningful problems.
- Key takeaways:
- Even beginners can build functional agents with the right tools.
- Experimentation is the fastest way to understand how these systems think.
- The future of work will be shaped by human–agent collaboration.
Multi‑Agent Customer Support Assistant — Capstone Project Overview
A simple but fully functional Multi‑Agent Customer Support Assistant built for the Enterprise Agents track.
What This System Does
When a user sends a message (e.g., “I need a refund” or “My invoice amount is wrong”), the system processes it using four specialized agents:
- Intent Agent – Analyzes the message, identifies intent (refund, cancellation, billing issue, etc.) and urgency level (low, medium, high).
- Reply Agent – Generates a short, clean, professional customer‑support reply based on the identified intent.
- Escalation Agent – Determines whether human support is needed.
- Coordinator Agent – Acts as the “brain” of the system: receives the message, calls the three specialized agents, collects their outputs, and returns a complete response package containing:
- Predicted intent
- Urgency level
- Auto‑generated reply
- Escalation decision
- Clean JSON output
This architecture demonstrates how multi‑agent systems rely on orchestration rather than isolated decision‑making.
Why I Built This Project
- Enterprise relevance: Aligns with the Enterprise Agents track, showcasing multi‑agent collaboration in a common business workflow.
- Clear responsibilities: Each agent has a well‑defined role, making the system easy to understand and predict.
- Ideal for multi‑agent architecture: Customer support naturally splits into understanding, replying, and escalation.
- Lightweight and practical: Uses simple rule‑based logic instead of heavy models, resulting in fast execution, easy comprehension, and safe operation without external API calls. The structure is extensible—LLMs can replace each agent for more advanced versions.
- Meets Kaggle competition requirements.
Resources
- Project Overview Video (2 minutes): https://youtu.be/ZfRAe9AJVfU?si
- Capstone Project Write‑up: https://kaggle.com/competitions/agents-intensive-capstone-project/writeups/new-writeup-1764584691566
A huge thank you to the Google and Kaggle teams for putting this together. I highly recommend these materials to any developer or architect serious about building the next generation of AI.