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Ella Sahnan
Ella Sahnan

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Building an Enterprise AI Calendar Agent: Our Journey with Google's (ADK) & Gemini 2.5

#googleaichallenge #ai #agents #devchallenge

This is a submission for the Google AI Agents Writing Challenge: Learning Reflections

Introduction

We recently completed the 5-Day AI Agents Intensive Course with Google and Kaggle, and it has been a transformative experience. Before this course, I understood LLMs as powerful text generators. Now, I see them as the "brains" of autonomous systems capable of reasoning, planning, and executing complex tasks.

In this post, I'll share my journey, the key concepts that reshaped my understanding of AI and Agents in particular, and a deep dive into the capstone project: The AI Calendar Agent, an autonomous system designed to solve the nightmare of enterprise meeting scheduling.

🚀 Key Learnings: How My Understanding Evolved

From "Chatbot" to "Agent Systems"

One of the biggest shifts in my thinking was moving from the request-response model of chatbots to the loop-based architecture of agents. The concept of an agent that can observe, reason, act, and reflect in a continuous loop was a game-changer.

Concepts from the course that clicked for me:

  • The importance of **Tools* (giving the LLM hands).*
  • The power of **Reasoning Traces* (Chain-of-Thought).*
  • The **ReAct* pattern (Reasoning + Acting).*

The Power of "Agentic Design Patterns"

We were particularly struck by how we can compose agents like building blocks.

  • Routing: Directing tasks to specialized sub-agents.
  • Evaluator-Optimizer: having one agent critique another's work.
  • Orchestrator-Workers: A central brain managing parallel tasks.

📅 The Capstone: AI Calendar Agent

For the capstone, we decided to tackle a problem we all face: Scheduling Meetings.

It sounds simple: "Find a time for Alice, Bob, and Charlie." But in an enterprise, it involves:

  1. Checking multiple calendars (and timezones!).
  2. Finding a meeting room with the right capacity, for in-person meetings.
  3. Respecting holidays and working hours, for remote colleagues.
  4. Dealing with "soft" constraints (e.g., "I prefer mornings").

System Architecture

We built the AI Calendar Agent using the Google Agent Development Kit (ADK) and Gemini 2.5-Flash. We chose a Hierarchical Multi-Agent Architecture to handle this complexity.

The system features a Coordinator Agent that delegates tasks to four specialized sub-agents:

  1. Availability Checker: Runs ADK tools to fetch FreeBusy data from Google Calendar API in parallel (reducing check time from 10s to ~1.5s!).
  2. Facility Manager: Searches for rooms based on capacity and amenities.
  3. Event Validator: Enforces policies and checks against holidays.
  4. Event Creator: The "doer" that actually interfaces with the Google Calendar API.

💡 Technical Highlights & "Aha!" Moments

1. Context Compaction & Token Optimization

One challenge we faced was the context window filling up with long JSON responses from the Calendar API.

  • Solution: We implemented a Context Truncation Strategy that intelligently summarizes older conversation turns while keeping the most recent interactions fresh. This reduced token usage by ~70%.

2. Observable Reasoning (Chain-of-Thought)

Debugging agents is hard. You can't just check a variable; you need to know what the AI was thinking.

  • Solution: We built a ReasoningEngine that logs the agent's internal monologue (e.g., [PLANNING], [DECISION], [VALIDATION]). This made the "black box" transparent and helped us tune the prompts effectively.

3. Persistence with ADK

A truly useful agent needs memory. Using ADK's persistence layer with SQLite, the agent can:

  • Resume sessions: Stop a conversation and pick it up later.
  • Recall preferences: Remember that "Alice works from home on Fridays" (Long-term memory).

🛠️ Challenges & Reflections

Building this wasn't smooth sailing.

  • The "Infinite Loop" Trap: At first, the agent would sometimes get stuck checking availability forever. We learned to implement strict stopping conditions and "give up" logic.
  • Tool Definition is an Art: We realized that the name and description of a tool are just as important as its code. If the LLM doesn't understand when to use a tool, it's useless.
  • Debugging is Hard: We found that debugging agents is much harder than debugging regular code. You can't just check a variable; you need to know what the AI was thinking.
  • Formation of a compatible team: We realized that working remotely with perfect strangers is hard. Especially when you're all in different timezones. We had to be clear and concise in our communication and be prepared to be brutally honest about our progress (or lack thereof).

Conclusion & Future Roadmap

This course has given us the foundation to build real, improving systems. For the AI Calendar Agent, the next steps are:

  • Phase 2: Adding "Fuzzy Search" for team, room, and holiday names.
  • Phase 3: Granular authorizations and permissions.

You can check out the full code on GitHub: TheCapstoneTeam/calender_agent.

Thank you to Google and Kaggle for this incredible learning opportunity!

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