AgenticAI-Calendar-Assistant

Inspiration

Students often face the overwhelming challenge of juggling academics, extracurriculars, and personal responsibilities without a structured way to prioritize tasks. Traditional to-do lists and calendars help, but they still require manual input, time estimation, and conflict resolution — which itself adds cognitive load. We noticed that even the most tech-savvy students often skip planning simply because it feels like another task. Inspired by this gap, we envisioned an agentic AI-powered calendar assistant that doesn’t just store events — it thinks, prioritizes, and plans like a human assistant would.

What it does

AgenticAI-Calendar-Assistant takes natural language input from the user and translates it into actionable calendar events. Whether it's scheduling a meeting, modifying an existing one, canceling an appointment, summarizing the day, or even planning the entire day based on a list of tasks, the assistant handles it all. It uses GPT-4o to classify the user’s intent, extract relevant details like time, duration, and participants, and then intelligently interfaces with Google Calendar. It also handles real-time conflict detection and provides flexible options — such as rescheduling existing events, finding the next available slot, or skipping conflicting events altogether. The result is a seamless and intelligent scheduling experience.

How we built it

We built the assistant using a modular Python backend. At the core, we use OpenAI’s GPT-4o model to parse natural language and classify the type of calendar request — such as new event creation, modification, cancellation, prioritization, or summarization. For calendar operations, we integrated the Google Calendar API with OAuth 2.0 for secure user authentication. The entire flow is built around a routing layer that maps classified intent to specialized handlers. We used Pydantic for structured data parsing and validation, Pandas for time manipulation, and custom logic for intelligent conflict resolution. Our system even supports back-and-forth decisions in real-time, asking the user how to handle scheduling conflicts. Everything runs in-memory with the ability to expand into a full web app.

Challenges we ran into

One of the biggest challenges was interpreting vague or incomplete user prompts. Natural language often lacks structured data like exact times or durations, which required us to build smart inference mechanisms. Another major hurdle was handling overlapping events in a human-like manner — simply rejecting events was not user-friendly, so we had to offer options like rescheduling or conflict deletion. Parsing multi-task prompts, like planning an entire day from a list of activities, demanded careful use of prompting and time-slot management. Additionally, integrating with the Google Calendar API came with edge cases such as timezone handling, recurring events, and attendee validations that needed to be accounted for.

Accomplishments that we're proud of

We’re proud of building a fully working assistant that can understand and process diverse types of calendar-related requests entirely through natural language. The ability to classify user intent, extract structured data, handle scheduling logic, and operate seamlessly with a real Google Calendar account — all in one cohesive pipeline — is something we’re excited about. We also developed robust logic for conflict resolution that goes beyond “event overlaps,” allowing the system to act like a true assistant that makes decisions. Most importantly, we succeeded in reducing cognitive friction, which was the core mission of our project.

What we learned

Throughout this project, we gained a deeper understanding of how to effectively use large language models for task routing and intent classification. We learned how to engineer prompts that reliably extract structured information from unstructured text, which was critical in scenarios where users did not follow a fixed template. Working with the Google Calendar API taught us a lot about real-world event scheduling complexities and the need for robust validation. We also learned the importance of anticipating user needs — such as offering choices during conflicts — to make the assistant feel truly intelligent and helpful.

What's next for AgenticAI-Calendar-Assistant

Looking forward, our next step is to integrate user-specific preferences like work hours, class schedules, and preferred break times to enable even more context-aware scheduling. We'll also track historical task durations to allow the assistant to better predict how long similar tasks will take for the same user. Another priority is to add support for recurring tasks, smart reminders (e.g., buffer time before events), and better handling of incomplete inputs. We plan to deploy a web-based interface with visual calendar integration and drag-and-drop rescheduling capabilities. Finally, we’ll conduct pilot testing with university students to gather real-world feedback and fine-tune the system’s ability to reduce daily planning stress.