This is a submission for the Google AI Agents Writing Challenge: Learning Reflections
The most important thing I noticed is how rapidly AI technology is evolving. Just a few months ago, during the “5-Day Gen AI Intensive Course with Google,” we were discussing prompt engineering, embeddings, vector databases, Gen AI agents, and domain-specific LLMs. Now, we have moved a step further to address more complex challenges in AI-based web application development.
Google ADK has come of age and is well equipped to tackle many of these challenges with state-of-the-art tools and an intuitive interface. The “5-Day AI Agents Intensive Course with Google” packs a semester’s worth of learning material, enabling participants to quickly become productive and develop a real-world project of their own.
I had a refreshing insight on the very first day of the course when we were introduced to the foundational concepts of AI agents and their defining characteristics. The ideas of sequential agents, parallel agents, and loop agents were eye-opening and sufficient to transform my perception of the orchestration possibilities of AI agents.
While built-in tools simplify many tasks, the real challenge arises when defining custom tools. As a developer, I was relieved to realize that my coding skills are still relevant in the age of vibe coding. The second day’s course material also provided hands-on experience in creating MCP tools and managing long-running operations using HITL (human in the loop).
Prompt engineering is out; context engineering is in. Moving beyond toy projects, we are now focused on building mission-critical, LLM-based solutions. The third day of the course focuses on implementing short-term and long-term memory to create more robust agents capable of handling complex, multi-turn tasks. Our agents can now truly understand context.
A production system must be reliable. We were taught how to make AI agents robust and transparent using advanced metrics and evaluation strategies. Honestly, this topic felt quite boring and cumbersome to grasp at first. However, mastering this aspect is essential to successfully making an impact in AI agent development for businesses.
The final day revolves around building a prototype to query a vendor’s product database and deploying the agent for real-world use.
My five-day journey was a revelation into the exciting world of AI agents and agentic architecture. I can now see the future with greater clarity and take advantage of this understanding to plan my career accordingly.
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