Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop
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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.
Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Mar 2017.
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Citing papers per year: 2021: 6; 2022: 7; 2023: 3; 2024: 4; 2025: 5; 2026: 1.
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- 2021Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation13 Sep 2021
- 2021VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search5 Aug 2021
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- 2021AutoDS: Towards Human-Centered Automation of Data Science13 Jan 2021
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- 2021"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment4 Jan 2021
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12 most cited of 21 All 21 (JSON)
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- 2024How to Measure Human-AI Prediction Accuracy in Explainable AI Systems23 Aug 2024
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- 2023Leveraging Large Language Models for Collective Decision-Making3 Nov 2023
- 2023Tribe or Not? Critical Inspection of Group Differences Using TribalGram16 Mar 2023
- 2023fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks22 Feb 2023
- 2022Using Graph Neural Networks for Program Termination28 Jul 2022
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12 newest of 21 All 21 (JSON)