Introduction
Hi everyone! I’m currently taking part in the Google × Kaggle AI Agents Intensive, and I’ve completed Day 1a (Agent Basics) and Day 1b (Agent Architectures). This post is a short reflection on what I learned, what I built, and how it went!
Day 1a – Agent Basics
Learned what an AI agent is and how it can automate reasoning and decision-making.
Tried my first simple agent using prompts and instructions.
Learned how to safely manage API keys and secrets in Kaggle.
Saw how agents can follow instructions to perform tasks step-by-step.
Day 1b – Agent Architectures
Explored how multiple agents can work together in a multi-agent system.
Learned about roles such as Coordinator, Planner, and Executor.
Built sequential and parallel workflows to coordinate…
Introduction
Hi everyone! I’m currently taking part in the Google × Kaggle AI Agents Intensive, and I’ve completed Day 1a (Agent Basics) and Day 1b (Agent Architectures). This post is a short reflection on what I learned, what I built, and how it went!
Day 1a – Agent Basics
Learned what an AI agent is and how it can automate reasoning and decision-making.
Tried my first simple agent using prompts and instructions.
Learned how to safely manage API keys and secrets in Kaggle.
Saw how agents can follow instructions to perform tasks step-by-step.
Day 1b – Agent Architectures
Explored how multiple agents can work together in a multi-agent system.
Learned about roles such as Coordinator, Planner, and Executor.
Built sequential and parallel workflows to coordinate agents.
Encountered a “Resource Exhausted” error — and learned that it happens when the API quota is used up.
Understood how rate limits and quotas affect API-based systems.
Key Takeaways
AI agents can make complex tasks easier by breaking them into smaller steps.
Good coordination is essential when multiple agents share information.
Resource and rate-limit management is an important part of building reliable systems.
The Kaggle notebooks made it easy to learn, run, and visualize results interactively.