This is a submission for the Google AI Agents Writing Challenge.
I recently completed the 5-Day AI Agents Intensive with Google and Kaggle. The program gave me a clear and practical understanding of how agentic systems work and how they can be applied to real problems. Instead of thinking in terms of single model calls, I learned how structured agent workflows create systems that are modular, reliable, and easier to extend.
Key Learnings
1. Agent Orchestration
One of the most valuable concepts was how multiple agents can collaborate through well-defined roles. By separating tasks into independent agents, the overall workflow becomes more interpretable and flexible.
2. Sequential, Parallel, and Loop Agents
I gained a deeper understanding of when to use each pattern. Sequential execution is ideal for dependent tasks, parallel execution improves performance for independent tasks, and loop agents enable iterative refinement.
3. Memory and Context
The course showed how memory and context engineering can significantly improve agent behavior. Session memory, context compaction, and retrieval allow agents to behave more consistently and respond with greater awareness.
4. Tools and Long-Running Operations
Integrating tools such as search and code execution expands the capabilities of agents. Learning about pausing, resuming, and handling long-running tasks helped me understand how more advanced agent systems operate.
My Capstone Project: Micro Health Symptom Triage Agent
For the capstone, I built a multi-agent system that helps users interpret symptoms and receive structured triage insights. It includes four agents:
- SymptomAgent for extracting symptoms
- TriageAgent for severity assessment
- SuggestionAgent for precautions and guidance
- TrendAgent for identifying recurring patterns
This project helped me apply everything taught in the course, including multi-agent orchestration, deterministic fallback logic, parallel execution, and memory handling.
Final Reflections
The intensive significantly strengthened my understanding of agentic AI. I learned how to design agents with clear responsibilities, how to build reliable workflows, and how to evaluate agent performance in a structured way. Most importantly, it gave me the confidence to design more sophisticated agent systems in the future.
Thank you to Google and Kaggle for creating an engaging and practical learning experience.
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