This is a submission for the Google AI Agents Writing Challenge: Learning Reflections
I found this writing challenge the day before the deadline.
To save time, I used NotebookLM for the first time and fed the course materials into it. I didn't grasp everything. Some ideas are still vague. Yet in just two days, my understanding of AI agents and the future role of engineers became clearer.
This post is a reflection on what I learned and what I'm still thinking about.
What Even Is an AI Agent?
Before this course, I couldn't clearly explain what an AI agent actually is. I used to think agents were basically smarter chatbots. My mental image was vague, like a powerful model, some clever prompts, a bunch of files, and glue code holding it all together. The AI field has been moving so fast that I've felt overwhelmed trying to keep up with what's new versus what's actually different.
What clicked for me was seeing the agent framed as a system, not just a model. You need a model for reasoning, tools for acting, and orchestration logic for deciding when and how to use them. The model is the brain, but without hands (tools) and a plan (orchestration), it's just thinking out loud. And worse, it can sound confident even when it's wrong.
Context and Contracts
One concept that stayed with me was “mise en place”, the cooking principle of preparing ingredients before you start. Instead of endlessly tweaking prompts, the course emphasized asking different questions. What information do we give the agent? When do we give it? What do we intentionally leave out? The quality of an agent's output relies more on the context humans provide than on clever wording.
The Model Context Protocol (MCP) section reinforced this. Standardizing how agents interact with tools sounds dry at first, but tools are how agents act. Someone still has to define the contracts, schemas, and boundaries.
Just like we needed REST to make APIs interoperable, we need protocols like MCP to make agent-tool interactions predictable and safe. Execution can be automated. The rules governing it cannot. We're responsible for both the input and the outcome.
Trust, Quality, and the Role of Engineers
I have worked in roles where quality and trust matter, across software, documentation, and education. Seeing the same concerns emerge in agent systems made the topic feel personal rather than abstract.
The most memorable part of the discussion was about agent quality and AgentOps. Unlike traditional software testing, AI agents don’t produce the same output every time. The idea that "the trajectory is the truth" means we should evaluate not only the final answer but also the steps an agent took, the tools it chose, and how it handled errors. That shift has changed how I think about trusting AI.
It also made me reflect on my own role. Technology keeps changing, but the fundamentals stay the same, understanding architecture, ensuring quality, and maintaining stability. The real challenge now is learning how to work with AI effectively.
Final Thought
For a course I joined with 48 hours left, I came away with new questions, several project ideas, and a clearer sense of where human judgment still matters. That feels like a meaningful outcome, even if it also made the gaps in my understanding more obvious.
As AI agents get better at execution, engineers are becoming more responsible for deciding what gets executed and how, instead of handing those decisions over entirely to the AI. I don't think this role is smaller. If anything, it feels heavier and more interesting.
I'm excited to start building my own ideas with AI agents.
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