This is a submission for the Google AI Agents Writing Challenge: Learning Reflections
Hi Dev Community!
I’m Abdulla Al Noman, a 4th-year CSE student at BRAC University. I recently completed the 5-Day AI Agents Intensive with Google, and it was an incredible deep dive into the world of AI agents - autonomous systems that can think, plan, and collaborate.
Here’s my reflection on the journey: what I learned, what resonated most, and how I applied it in my capstone project, ScholarMindAI.
Key Learnings & Concepts That Resonated
Day 1 – Foundational LLMs & Prompt Engineering
I realized that even the smartest AI needs clear guidance. Crafting structured prompts, tuning parameters, and evaluating responses are critical to making AI agents reliable.
Day 2 – Embeddings & Vector Databases
This taught me that semantic understanding beats keyword search. Embeddings and vector stores let AI agents retrieve and compare information intelligently, enabling smarter research workflows.
Day 3 – Generative AI Agents
Here, I learned how to orchestrate multiple agents together. Function calling, multi-agent design, and connecting agents to real-world tools highlighted how autonomous systems can tackle complex tasks collaboratively.
Day 4 – Domain-Specific LLMs
Fine-tuning agents for specialized domains showed me that context matters. Domain-adapted agents perform better and can handle complex knowledge like research papers or medical data.
Day 5 – MLOps for Generative AI
Making AI agents production-ready taught me the importance of resilience, observability, and memory management. It’s not just about building capabilities - it’s about making them reliable in the real world.
Capstone Project: ScholarMindAI
To put these concepts into practice, my teammate Suprava Saha Dibya and I built ScholarMindAI, a multi-agent academic research assistant.
It can:
- Search and summarize papers automatically
- Compare methodologies and highlight insights
- Generate structured literature reviews
- Manage citations in APA, MLA, Chicago, or Harvard
- Build dynamic knowledge graphs connecting concepts, authors, and methods
- Predict research impact and track project progress
- Support collaboration with shared dashboards and handoff documents
This project gave me hands-on experience orchestrating agents, handling memory and context, and designing workflows that actually help humans work smarter.
Reflections & Growth
- AI agents are team players, not just tools - they amplify human curiosity.
- Memory and context are essential for agents to handle complex, multi-step tasks.
- Hands-on labs and the capstone made abstract concepts tangible.
- Production readiness - logging, error handling, resilience - is just as important as intelligence.
The course changed my perspective: I now see AI agents not just as code, but as autonomous collaborators capable of meaningful work.
Thoughts
The AI Agents Intensive taught me that AI doesn’t replace curiosity - it enhances it. ScholarMindAI is my first step toward building agents that free researchers to think, explore, and innovate.
I’m excited to continue exploring this space and connecting with others passionate about AI agents.
Thanks for reading!
— Abdulla Al Noman
Top comments (0)