AI Agents Intensive Course – Google x Kaggle
Introduction
I joined the 5-Day AI Agents Intensive Course with Google and Kaggle to understand how modern AI agents work and how they can be used to solve real-world problems.
Before this course, I only used simple chatbots. After this course, I learned how to build real multi-agent systems with tools, memory, and workflows.
This is my learning journey and project reflection.
🗓️ Day 1 – From Prompts to Real Agents
What I learned:
- Difference between a chatbot and an AI agent
- How agents can take actions instead of only replying
- Basic agent architectures
My reflection:
This was the biggest mindset shift for me. I learned that agents are not just “smart replies” — they are systems that can think, decide and act.
🛠️ Day 2 – Agent Tools & Interoperability
What I learned:
- How agents use tools (functions / APIs)
- How tools help agents interact with external systems
- Best practices for safe tool usage
My application:
I created appointment tools that allow my agent to:
- Create new hospital appointments
- Fetch existing appointment details from a CSV database
This made my agent feel like a real working system.
🧠 Day 3 – Sessions & Memory (Context Engineering)
What I learned:
- How agents remember conversations
- Session handling and memory storage
- Why memory makes agents feel more human
My implementation:
I connected my main agent with:
InMemorySessionServiceInMemoryMemoryService
Now my hospital agent can remember user context during a session.
✅ Day 4 – Agent Quality & Observability
What I learned:
- How to measure agent quality
- Observability: tracking what agents think and do
- Why evaluation matters in production agents
My reflection:
This day helped me understand that building an agent is not only about features — it's about trust, reliability, and safety.
🚀 Day 5 – Multi-Agent System (Agent2Agent)
What I learned:
- How agents communicate with other agents
- Agent2Agent (A2A) architecture
- How to deploy agents
My capstone implementation:
I built a multi-agent hospital system:
- A Remote Appointment Agent (handles CSV data)
- A Hospital Orchestrator Agent (talks to users and calls the remote agent)
This design makes the system scalable and modular.
🏥 My Capstone Project – Hospital Appointment AI Agent
Project Idea
Many hospitals still use manual appointment systems.
I built an AI agent system that can:
- Book appointments
- Assign rooms automatically by time
- Fetch appointment details instantly
Architecture
User → Orchestrator Agent → Remote Appointment Agent → CSV Database
This architecture helped me understand real-world agent system design.
🎓 Capstone Project Demo
🎥 Demo Video: https://www.youtube.com/watch?v=F0c6Xfe7FCE
🎯 Key Learnings
- Agents are action-driven, not only chat-driven
- Tools turn agents into real applications
- Memory and sessions create personalized experiences
- Multi-agent systems are powerful and scalable
🙏 Gratitude
I sincerely thank Google and Kaggle for organizing this amazing 5-Day AI Agents Intensive Course.
It gave me hands-on experience and confidence to build real AI agent systems.
This course changed how I see AI — from simple chatbots to intelligent, autonomous systems.
🔮 If I Had More Time
If I had more time, I would:
- Add voice support to the agent
- Build a web UI dashboard
- Connect it with real hospital APIs
- Add authentication and security layers
Final Thoughts
This course was not just learning — it was building, experimenting, and growing.
I am excited to continue my journey in Agentic AI.
Thank you! 🚀
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