# Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop

arXiv: https://arxiv.org/abs/1703.08428 · HTML: https://mlsanity.com/paper/1703.08428/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2017.

## Built on by (most cited first, 26 of 26)

- [Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation](https://mlsanity.com/paper/2204.11788/) (2022)
- [TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems](https://mlsanity.com/paper/2506.04133/) (2025)
- [AutoDS: Towards Human-Centered Automation of Data Science](https://mlsanity.com/paper/2101.05273/) (2021)
- ["Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment](https://mlsanity.com/paper/2101.01524/) (2021)
- [TeachTune: Reviewing Pedagogical Agents Against Diverse Student Profiles with Simulated Students](https://mlsanity.com/paper/2410.04078/) (2024)
- [Multi-User Chat Assistant (MUCA): a Framework Using LLMs to Facilitate Group Conversations](https://mlsanity.com/paper/2401.04883/) (2024)
- [fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks](https://mlsanity.com/paper/2302.11703/) (2023)
- [CASS: Towards Building a Social-Support Chatbot for Online Health Community](https://mlsanity.com/paper/2101.01583/) (2021)
- [Conversational Agents: Theory and Applications](https://mlsanity.com/paper/2202.03164/) (2022)
- [Leveraging Large Language Models for Collective Decision-Making](https://mlsanity.com/paper/2311.04928/) (2023)
- [Do It For Me vs. Do It With Me: Investigating User Perceptions of Different Paradigms of Automation in Copilots for Feature-Rich Software](https://mlsanity.com/paper/2504.15549/) (2025)
- [PEARL: Self-Evolving Assistant for Time Management with Reinforcement Learning](https://mlsanity.com/paper/2601.11957/) (2026)
- [A Bottom-Up End-User Intelligent Assistant Approach to Empower Gig Workers against AI Inequality](https://mlsanity.com/paper/2204.13842/) (2022)
- [From User Surveys to Telemetry-Driven AI Agents: Exploring the Potential of Personalized Productivity Solutions](https://mlsanity.com/paper/2401.08960/) (2024)
- [AI That Helps Us Help Each Other: A Proactive System for Scaffolding Mentor-Novice Collaboration in Entrepreneurship Coaching](https://mlsanity.com/paper/2508.11052/) (2025)
- [CHOIR: A Chatbot-mediated Organizational Memory Leveraging Communication in University Research Labs](https://mlsanity.com/paper/2509.20512/) (2025)
- [ScheduleMe: Multi-Agent Calendar Assistant](https://mlsanity.com/paper/2509.25693/) (2025)
- [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021)
- [VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search](https://mlsanity.com/paper/2108.02725/) (2021)
- [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021)
- [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022)
- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)
- [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022)
- [Tribe or Not? Critical Inspection of Group Differences Using TribalGram](https://mlsanity.com/paper/2303.09664/) (2023)
- [How to Measure Human-AI Prediction Accuracy in Explainable AI Systems](https://mlsanity.com/paper/2409.00069/) (2024)

## Abstract (arXiv)

Although information workers may complain about meetings, they are an essential part of their work life. Consequently, busy people spend a significant amount of time scheduling meetings. We present Calendar.help, a system that provides fast, efficient scheduling through structured workflows. Users interact with the system via email, delegating their scheduling needs to the system as if it were a human personal assistant. Common scheduling scenarios are broken down using well-defined workflows and completed as a series of microtasks that are automated when possible and executed by a human otherwise. Unusual scenarios fall back to a trained human assistant who executes them as unstructured macrotasks. We describe the iterative approach we used to develop Calendar.help, and share the lessons learned from scheduling thousands of meetings during a year of real-world deployments. Our findings provide insight into how complex information tasks can be broken down into repeatable components that can be executed efficiently to improve productivity.
