This is a submission for the Google AI Agents Writing Challenge: Learning Reflections
From Curiosity to Confidence
I joined the 5-Day AI Agents Intensive Course mainly out of curiosity. As a beginner, I hesitated at first, unsure if I could keep up with the concepts from Google and Kaggle. But the deeper I went, the more confident I became.
The moment that truly amazed me was seeing modular sequential agents working together: each agent refining a part of the task and producing a final output that felt surprisingly “Intelligent/Smart” That experience made me want to share my excitement with others and that’s what inspired this post.
How My Understanding of Agents Evolved
Before the course, I used to think:
- I thought agents as chatbots
- I believed prompts were the main factor
- I imagined agent behavior as “one-shot responses”
After this course:
- I now understand agents as decision-making systems
- They use tools, memory, structured state, and feedback loops
- Agents collaborate like team members
- I can now build simple multi-agent workflows on my own
- Agentic AI feels accessible instead of intimidating
I feel comfortable building simple multi-agent systems, something I never imagined before the course.
My Capstone Demo: HealthSense — AI Health Context Assistant
For my capstone, I built HealthSense, a simple educational prototype using ADK + Gemini. It analyzes text/image input and provides general health context (causes, warnings, suggestions). It’s not production-ready just a concept to practice agent workflows.
Its goal was simple: explore how an agent can interpret multimodal input (text + image) and provide general health context like:
- possible causes
- safety warnings
- helpful suggestions
- “red flag” alerts (using curated rules)
The project is intentionally a prototype, but it helped me apply everything I learned in a hands-on way. This course boosted my confidence and inspired me to continue exploring agentic AI.
Key Takeaways From the Course
- Agents need structure, tools, and memory
- Multi-agent workflows are surprisingly powerful
- State transitions improve debugging
- Human-in-the-loop is essential
- ADK makes agent building approachable
You don’t need to be an expert to start building agentic AI.
Conclusion
This course turned my curiosity into confidence. It reshaped how I think about AI and showed me that powerful agentic systems are not limited to experts, beginners can build them too.
I’m excited to continue exploring agent workflows, refining HealthSense, and experimenting with more ideas in the agentic AI world.
If you're new to AI agents, this course is the perfect place to start. It certainly transformed my learning journey.
Thank you to Google & Kaggle for creating such an impactful learning experience.
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