AI Agents Intensive Course Writing Challenge https://dev.to/challenges/google-kaggle-ai-agents-2025-11-10
OptimaWave Agent: How the 5 Day AI Agents Intensive Course Helped Me Build a Smarter Travel Planner
Your Budget. Your Trip. Designed by AI.
Introduction
The Google x Kaggle 5 Day AI Agents Intensive Course was a turning point in my AI journey.
Before this course, I understood LLMs but did not fully grasp agents systems that observe, reason, plan, act, and work autonomously.
Over five days, through whitepapers, codelabs, and hands on experimentation, I learned to design real agentic systems using:
ADK (Agent Development Kit)
MLL (Machine Learning Library)
Model Context Protocol (MCP)
Context Engineering
Observability Tools
Agent2Agent (A2A) Protocol
Vertex AI Agent Engine
To apply this knowledge, I built OptimaWave Agent, an AI powered travel planner that recommends destinations based on budget, travel days, and theme.
This article is the story of how each day of the course shaped my understanding and inspired my final capstone project.
Day Wise Learnings (Full Reflection)
Day 1: Introduction to Agents
Understanding what makes agents different from LLMs.
I learned:
Agents are autonomous beings with goals
Tools enable actions beyond text
Multi agent collaboration solves complex tasks
Interoperability and identity control are core to security
Agent Ops is essential for reliability and governance
I built my first agent using ADK + Gemini, and even created a small multi agent system.
This inspired OptimaWave Agent’s modular architecture.
Day 2: Agent Tools & Model Context Protocol (MCP)
Day 2 showed me that tools turn LLMs into agents.
Key learnings:
Convert Python functions into agent tools
Extend agent capabilities beyond training data
Pause resume agent operations
Simplify tool discovery with MCP
For OptimaWave Agent, this inspired:
Filtering tools
Ranking tools
Dataset exploration tools
Visualization tools
Day 3: Context Engineering: Sessions & Memory
Agents must remember and maintain context.
I learned:
Use session history to maintain coherent conversations
Build long term memory across sessions
Dynamically assemble context for reasoning
In OptimaWave Agent, this allows the planner to:
Track user preferences
Refine recommendations over time
Maintain state across multiple turns
Day 4: Agent Quality (Logs, Traces, Metrics)
Making agents reliable, observable, and trustworthy.
Key learnings:
Logs: What happened
Traces: Why it happened
Metrics: How well it happened
In my planner, observability helps answer:
Why a destination was filtered out
Why a theme was recommended
How cities were ranked
Day 5: Prototype to Production
Deploying and scaling real agentic systems.
I learned:
Production lifecycle of agents
Deployment on Vertex AI Agent Engine
Scaling strategies
Multi agent collaboration using A2A Protocol
This inspired a multi agent travel system:
One agent for destinations
One for hotels
One for flights
One for weather
One for budgeting
Capstone Project: OptimaWave AgentAI Travel Planner
Problem:
Travelers struggle to balance:
Budget
Number of days
Destinations
Themes (romantic, adventure, heritage, etc.)
Planning is stressful, slow, and manual.
Solution:
An AI agent that instantly recommends the best destinations fitting user constraints.
Core Features:
Budget based filtering
Theme matching
Travel day optimization
Dynamic destination ranking
Visualization of available options
Interactive Kaggle Notebook demonstration
Example Input Output (Corrected)
Input:
Budget: 1300 USD
Days: 5
Theme: Romantic
Output:
Destination Travel Days Theme
Paris 5 romantic
Dubai 4 luxury
London 5 heritage
Visualization:
Bar chart showing budget comparisons across all destinations:
Dubai: 1000
Paris: 1200
London: 1300
Tokyo: 1500
Sydney: 1800
Architecture (Inspired by Day 1 & 5 Learnings)
User Input
Agent Processor (ADK + MLL)
Filtering Tools (Day 2)
Context Engine (Day 3)
Observability Pipeline (Day 4)
Recommendation Engine
Output + Visualization
This is a course aligned, production ready architecture.
Tools Used
ADK (Agent Development Kit)
MLL (Machine Learning Library)
Google AI Studio
MCP
Python
Kaggle Notebook
CSV Dataset
Future Improvements (Inspired by Day 5)
Multi agent travel ecosystem (A2A)
Live APIs: flights, hotels, weather
Personal itinerary generator
User preference memory
Only the Planner Module has been developed (Future version: Web + App support)
Full deployment on Vertex AI Agent Engine
Final Reflection
This intensive course didn’t just teach me how to build agents it taught me how to think in agents.
Every day built a new layer of understanding:
Day 1: What agents truly are
Day 2: How they act
Day 3: How they remember
Day 4: How we evaluate them
Day 5: How we deploy them
OptimaWave Agent is the result of this 5 day journey a fully working structured intelligent AI travel planner.
This challenge helped me grow from an intermediate ML developer to an advanced agentic system builder.
OptimaWave AgentAI End to End Code Implementation & Output Demonstration:
Top comments (1)
This project demonstrates the Planner Module of OptimaWave Agent.
A full Web and Mobile App experience is planned as a future enhancement...