Wrapped up Day 1A of Kaggle's 5-Day AI Agents Intensive Course with Google – the "From Prompt to Action" codelab that had me build my first functional AI agent using Gemini and the Agent Development Kit (ADK). In this hands-on Kaggle notebook, basic prompts and Python functions evolved into a tool-calling agent handling end-to-end tasks autonomously, marking a clear jump from static LLM prompts.
Core Takeaways from the Build
- Tool Setup & Invocation: Defined simple Python tools (like search or math utilities), registered them via ADK, and watched Gemini route user queries to the right ones dynamically.
- ReAct Loop in Practice: The agent cycled through observe-reason-act-observe, tackling multi-step flows like "fetch data and summarize" without hardcoded logic.
- Debugging Wins: ADK's built-in tracing showed reasoning paths and tool calls, making it easy to refine tool descriptions for sharper performance.
Hands-On Experience & Project Tie-In
Forked the public Day 1A notebook, set up Gemini API keys, and extended the agent to pull weather data for a travel planner – worked flawlessly after one tool tweak.
Why It Stuck & Moving Forward
Day 1A demystified agent basics in ~2 hours of code, prepping for Day 1B's multi-agent setups – already completed that too. For AI builders juggling startups like mine (EdTech, HR analytics), this course skips theory traps and delivers production patterns fast. Dive in via the Kaggle guide:
"https://www.kaggle.com/learn-guide/5-day-agents"
This is the Kaggle Notebook:
"https://www.kaggle.com/code/mysticdatum/5d-ai-agents-d1a"
This is the YouTube Link related to the Assignment:
"https://youtu.be/Lah6M2Vdqgc?si=71JK4_sNFQCW2ViW"
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