This is a submission for the Google AI Agents Writing Challenge: Learning Reflections
Agentic AI is the evolution of automation which is agile in adaptive decision making.
Fellas ! It was truly an amazing event packed with practical implementation
I started with Building your first Agent, it was quite fun and accomplishing to record the working of an agent. The learning is based on utilising ADK and Gemini for API keys and compatible model. As I geared up on day 1, I got to know the very basics of what is Agentic AI and the method it invokes to work with, then the types of agents and clear workflows of architecture in multi-agent systems.…
This is a submission for the Google AI Agents Writing Challenge: Learning Reflections
Agentic AI is the evolution of automation which is agile in adaptive decision making.
Fellas ! It was truly an amazing event packed with practical implementation
I started with Building your first Agent, it was quite fun and accomplishing to record the working of an agent. The learning is based on utilising ADK and Gemini for API keys and compatible model. As I geared up on day 1, I got to know the very basics of what is Agentic AI and the method it invokes to work with, then the types of agents and clear workflows of architecture in multi-agent systems.
I caught up with Interoperability with MCP and tools to get to know the deeper side of orchestration behind an agent’s success. The best part was that you can connect the documents to the NotebookLM and learn the core concepts a way much better. They even provided the summary podcast created by NotebookLM.
I was able to call get tiny image tool from MCP server to test on my local host and it worked. Furthermore I wanted the output of image of an anime girl when asked from the agent just to work with different MCP server, let alone Replicate MCP server. There was a glitch and I moved on to day 3, however on day 2, I worked with agent with approval and definitely wouldn’t have missed. Day 3 and the dawn of context engineering Sessions and Memory one of my favourite topics, I put pen to paper and dived into how to make the agent stateful and the labs were my only resources cut to the chase for any beginner and I am glad everything was so smooth while I learned.
Day 4 was the addition to Responsible AI and if the agent is capable to solve problem Should it actually do or not, they must be evaluated too. From Glassbox and Blackbox evaluations to pillars of observability. There was proper guidance depending on the roles of any professional.
While Day 5 was all about Prototype to Production , Deploying the agent. On a good note, I would say the event was more than worthwhile The curated playbooks and the steps to develop the agents on kaggle is the foundation to my next move which is developing Agentic AI project end to end.
Thank you for the day