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

Source: Dev.to

Introduction

We recently completed the 5‑Day AI Agents Intensive Course with Google and Kaggle. Before the course, I viewed LLMs mainly as powerful text generators. Now I see them as the “brains” of autonomous systems capable of reasoning, planning, and executing complex tasks. This post shares my journey, the concepts that reshaped my understanding of AI agents, and a deep dive into our capstone project: an autonomous AI Calendar Agent for enterprise meeting scheduling.

Key Concepts

• Tools – Giving the LLM “hands” to interact with external systems.
• Reasoning Traces (Chain‑of‑Thought) – Making the model’s thought process explicit.
• ReAct pattern – Combining reasoning and acting in a single loop.

We also explored composable agent patterns:

• Routing – Directing tasks to specialized sub‑agents.
• Evaluator‑Optimizer – One agent critiques another’s work.
• Orchestrator‑Workers – A central brain manages parallel tasks.

Capstone Project: AI Calendar Agent

Problem Statement

Scheduling meetings in an enterprise involves more than picking a time:

• Checking multiple calendars across time zones.
• Finding a meeting room with appropriate capacity and amenities.
• Respecting holidays and working hours for remote colleagues.
• Handling soft constraints (e.g., “prefer mornings”).

Architecture

We built the agent with the Google Agent Development Kit (ADK) and Gemini 2.5‑Flash, using a hierarchical multi‑agent design:

Challenges & Solutions

Context Window Management

Problem: Long JSON responses from the Calendar API filled the model’s context window.
Solution: Implemented a Context Truncation Strategy that intelligently summarizes older turns while keeping recent interactions fresh, cutting token usage by ~70 %.

Debugging Agent Behavior

Problem: Traditional debugging (checking variables) isn’t sufficient for agents.
Solution: Developed a ReasoningEngine that logs the agent’s internal monologue with tags like [PLANNING], [DECISION], [VALIDATION]. This made the “black box” transparent and helped fine‑tune prompts.

Memory & Persistence

Using ADK’s SQLite persistence layer, the agent can:

• Resume sessions – Stop a conversation and pick it up later.
• Recall preferences – Remember long‑term facts such as “Alice works from home on Fridays”.

Infinite Loop Trap

Problem: The agent sometimes got stuck checking availability indefinitely.
Solution: Added strict stopping conditions and a “give up” fallback logic.

Tool Definition

Crafting clear names and descriptions for tools proved essential; without them the LLM fails to invoke the tools correctly.

Team Collaboration

Working remotely across time zones required concise communication and honest progress reporting.

Lessons Learned

• Loop‑based agent architectures enable continuous observation, reasoning, acting, and reflection.
• Well‑defined tools and explicit reasoning traces dramatically improve reliability.
• Debugging agents demands visibility into their thought processes, not just code execution.
• Memory and persistence are critical for real‑world usefulness.

Future Work

1. Phase 2: Add fuzzy search for team members, rooms, and holiday names.
2. Phase 3: Implement granular authorizations and permissions.

Code Repository

You can explore the full source code on GitHub:
TheCapstoneTeam/calendar_agent

Acknowledgements

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