Learning to Think in Agents: My Takeaways from Google’s 5-Day Intensive
Source: Dev.to
Why this intensive mattered to me
What I liked most was the structure: short explanations, hands‑on labs, and then a capstone project that forced me to connect everything. It never felt purely theoretical. Every concept was quickly grounded with “okay, now let’s build with this.”
How my understanding of agents evolved
- What is the agent’s goal?
- What context and memory does it need?
- Which tools or APIs should it be allowed to use?
- How does it decide the next step in a loop?
These questions made agents feel less like “magic” and more like engineering. I also understood the value of having multiple agents collaborating, each specializing in a different role (e.g., planner, researcher, executor), and how that can make complex tasks more reliable.
Learning to build with Google AI
The integration with other Google tools made the experience feel complete: experiment in notebooks, use datasets, and then think about how this could be deployed or scaled with Google Cloud later. It shifted my perspective from “AI demos” to “AI products.”
Hands‑on labs and my project
For the capstone, I built a project that brought these ideas together and made me think carefully about the agent’s role, the tools it should use, and how to keep it grounded and reliable. That process taught me a lot about trade‑offs:
- How much autonomy to give the agent
- How to structure prompts
- How to log or debug its behavior when something goes wrong
What I’m taking forward
- Designing agents with clear goals and roles
- Using Gemini and Google AI tools to quickly prototype
- Thinking about how to connect these agents to real data, APIs, and users
Most importantly, I feel much more confident about building agentic AI into real projects. Instead of asking “Can I do this?”, I now ask “How should I design the agent and tools so this works well?” That shift in thinking is the biggest thing I gained from the 5‑Day AI Agents Intensive Course.