Completing the 5-Day AI Agents Intensive with Google was a challenge in the best possible way and one of the most valuable hands-on learning experiences I’ve had with AI so far. I started the program with a general idea of how large language models work, but I finished it with a much deeper understanding of what actually makes an AI agent — things like tool use, orchestration, memory, observability, and system-level design.
One of the toughest parts for me was the technical setup and integration. Working inside Kaggle with the Agent Development Kit (ADK), configuring API keys, and making sure all dependencies were aligned was more complicated than I expected. When I started working with Model Context Protocol (MCP), I also had to really slow down to understand how tools are registered, discovered, and called by agents. Turning Python functions into usable tools and managing long-running operations forced me to think much more carefully about structure, permissions, and error handling.
Context engineering was another major learning curve. Implementing session-based memory and persistent long-term memory made me realize how important it is to control what an agent sees and remembers. Managing context windows, making memory retrieval efficient, and ensuring coherent multi-turn conversation required a different level of discipline compared to simple prompt engineering. That section truly shifted how I think about building stateful AI systems.
Day 4, which focused on agent quality, was where everything started to feel more “production-grade.” Adding observability through structured logs, traces, and custom metrics helped me actually see the agent’s internal reasoning and tool usage. Using evaluation techniques like LLM-as-a-judge and thinking about Human-in-the-Loop systems made me more aware of how much testing, monitoring, and refinement is required to make AI agents reliable and safe.
Finally, learning about Agent2Agent (A2A) communication and deploying agents through Vertex AI Agent Engine opened my eyes to how scalable and collaborative multi-agent architectures really work in real-world environments. Orchestration between independent agents, proper routing of tasks, and safe inter-agent communication felt like stepping into actual AI system design, not just experimental projects.
What helped me overcome the challenges was persistence, breaking problems down into smaller steps, carefully reading documentation, experimenting in small iterations, and learning from the community. Each roadblock ended up making me more confident, because I wasn’t just following instructions — I was actually understanding why things worked.
This intensive didn’t just teach me how to use specific tools; it changed how I think about building AI systems as a whole. I now see AI agents not just as chatbots, but as modular, observable, multi-component systems that can solve real problems when designed thoughtfully. I’m genuinely excited to take what I’ve learned here and apply it to more advanced, real-world use cases.
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