When I first signed up for the Google AI Agents 5-Day Intensive Course, I expected code labs, theory, and maybe a few surprising insights.
What I didn’t expect was how quickly those five days would reshape the way I think about building intelligent systems — and how they pushed me to aim for the Kaggle prize with new confidence.
Here’s a quick walk-through of what the week looked like from my perspective.
🔧 Day 1 — Understanding How Agents Actually Think
Day 1 hit me with a simple but powerful realization:
Agents aren’t just models… they are decision-makers.
We explored planning, autonomy, and the flow of “observe → think → act”.
It felt like learning the blueprint of how modern AI systems behave in the real world.
By the end of the day, I wasn’t just writing code — I was designing behavior.
🛠️ Day 2 — Tools, Tools, and More Tools
This was the day that completely changed the way I structure my projects.
We learned to:
Wrap Python functions as agent tools
Use the Model Context Protocol (MCP)
Connect agents to external APIs
Make them operate like smart, capable workers
Honestly, it felt like giving my models superpowers.
From here onwards, every script I wrote started feeling like a small AI system.
🧠Day 3 — Memory & Context: The Secret Ingredient
Day 3 made something click:
Good memory = good intelligence.
We covered:
Short-term vs long-term context
Retrieval strategies
Structuring sessions for consistent behavior
I started rethinking my dataset workflows and even reorganised my project notes to mimic the memory structure we built for agents.
📊 Day 4 — Evaluations: Where Agents Grow Up
This day was all about building responsible AI systems.
We learned:
LLM-as-a-Judge evaluations
Logging and tracing
How to measure quality beyond accuracy
Why feedback loops matter
After this session, I stopped guessing whether my agents were improving — I started tracking it.
🔗 Day 5 — Agent-to-Agent Collaboration (A2A)
The last day tied everything together.
We built:
Multi-agent workflows
Message-passing systems
Structured communication between agents
It made me realize how powerful AI becomes when agents stop working alone and start cooperating.
This was the moment where everything started feeling real, like the foundation of production-grade systems.
🎯 The Capstone — Bringing It All Together
The final project pushed me to combine:
Orchestration
Tools
Memory
Evaluation
Multi-agent coordination
I created a small but complete agent workflow — something I’m now refining for the Kaggle competition.
This challenge isn’t just about writing code.
It’s about applying everything from the course to create something that actually works.
✨ Final Thoughts
This 5-day intensive wasn’t just a course — it was a shift in mindset.
I walked away with:
A clear understanding of agent architecture
Hands-on experience building real workflows
A roadmap for improving and scaling my models
The confidence to take on the Kaggle challenge with a real plan
If you’re diving into AI Agents, this course gives you the structure you didn’t know you were missing.
And now… time to turn all this into something prize-worthy 👀💪
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