**
Over the last five days, I took the Google x Kaggle AI Agents Intensive Course — and what started as “learning how to prompt better” quickly expanded into a complete understanding of how real AI agents think, act, store memory, collaborate, and evaluate themselves.
The "Aha!" Moment: Tools & Reasoning
The most resonant concept from the course was the transition from Zero-Shot Prompting to ReAct (Reasoning + Acting) loops.
Before: I would ask an LLM, "What is the sentiment of the market?" It would hallucinate or give a generic answer based on old training data.
After: I learned to build an agent that thinks:
Thought: "I need to check the latest news for specific tickers."
Action: Calls a Search Tool or News API.
Observation: Reads the headlines.
Reasoning: "These headlines look bearish. I should calculate a sentiment score."
Final Response: Sends an alert.
Giving the AI "hands" (Tools) and a "brain" (Gemini 2.0 Flash) allowed me to build something dynamic.
On the first day, we started with building simple agents and multi-agent architectures. I learned about different Google ADK modules and their functions: agent (model, instruction, output key), runner, parallel agent, sequential agent, and loop agent. Each pattern revealed a different way to structure agent behavior, from linear execution to parallel processing.
What made this course truly transformative was listening to various experts in the field of AI. Their thoughts and analysis across different topics provided real-world perspective that lectures alone cannot offer. The summarization sessions and quizzes at the end of each live session made remembering the concepts feel natural and engaging rather than forced.
Day 2's tool-calling lab was where things got interesting for me. I'd read about function calling before but never really understood it. Watching an agent decide which tool to use, figure out what parameters to pass, handle the response, and then keep going based on that... it felt almost magical. Until I looked under the hood and realized it was just really good engineering.
The multi-agent lab on Day 4 kind of blew my mind though. Seeing multiple agents work together, each handling their specialty, reminded me more of how actual teams work than how software usually works. One agent would do its thing, pass info to another agent, that one would do its thing. No single agent had to be perfect at everything
Day 3: Advanced Capabilities
Implemented multi-agent collaboration for complex workflows.
Learned about memory management and persistent state for long-term tasks.
Day 4 — Evaluation, Observability & Guardrails
No agent can be trusted without visibility.
We explored:
White-box evaluation
Black-box evaluation
Tracing tool calls
Logging behavior
Guardrail design
Hallucination prevention
Observability suddenly felt like giving the agent a heartbeat monitor — a way to truly understand whether it was functioning correctly or drifting off-course.
Day 5: From prototype to production, and why it matters
The final day’s focus on moving from prototype notebooks to production systems felt like a bridge from curiosity to responsibility. Concepts like the Agent2Agent (A2A) protocol and deployment options via Vertex AI’s Agent Engine illustrated how multi‑agent systems can be composed, deployed, and governed in larger environments.
The AI Agents Intensive was more than a course — it was a journey of effort, learning, frustration, improvement, and confidence. I’m grateful to Kaggle for creating an opportunity that pushed me out of my comfort zone and helped me achieve something I once thought I couldn’t.
And now, I’m excited for what comes next.
Thank you :)
**This is a submission for the Google AI Agents Writing Challenge: Learning Reflections
Top comments (0)