Automating the Enterprise with Foundation Models
Authors: 
Michael Wornow, Avanika Narayan, Krista Opsahl-Ong, Quinn McIntyre, Nigam Shah, Christopher R�Authors Info & Claims
Michael Wornow, Avanika Narayan, Krista Opsahl-Ong, Quinn McIntyre, Nigam Shah, Christopher R�Authors Info & ClaimsPages 2805 - 2812
Abstract
Automating enterprise workflows could unlock $4 trillion/year in productivity gains. Despite being of interest to the data management community for decades, the ultimate vision of end-to-end workflow automation has remained elusive. Current solutions rely on process mining and robotic process automation (RPA), in which a bot is hard-coded to follow a set of predefined rules for completing a workflow. Through case studies of a hospital and large B2B enterprise, we find that the adoption of RPA has been inhibited by high set-up costs (12--18 months), unreliable execution (60% initial accuracy), and burdensome maintenance (requiring multiple FTEs). Multimodal foundation models (FMs) such as GPT-4 offer a promising new approach for end-to-end workflow automation given their generalized reasoning and planning abilities. To study these capabilities we propose ECLAIR, a system to automate enterprise workflows with minimal human supervision. We conduct initial experiments showing that multimodal FMs can address the limitations of traditional RPA with (1) near-human-level understanding of workflows (93% accuracy on a workflow understanding task) and (2) instant set-up with minimal technical barrier (based solely on a natural language description of a workflow, ECLAIR achieves end-to-end completion rates of 40%). We identify human-AI collaboration, validation, and self-improvement as open challenges, and suggest ways they can be solved with data management techniques.
References
[1]
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al. 2022. Do as i can, not as i say: Grounding language in robotic affordances. arXiv preprint arXiv:2204.01691 (2022).
[2]
Automation Anywhere. 2020. https://www.automationanywhere.com/company/press-room/global-research-reveals-worlds-most-hated-office-tasks
[3]
Rim Assouel, Tom Marty, Massimo Caccia, Issam H Laradji, Alexandre Drouin, Sai Rajeswar, Hector Palacios, Quentin Cappart, David Vazquez, Nicolas Chapados, et al. 2023. The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study. In NeurIPS 2023 Foundation Models for Decision Making Workshop.
[4]
Adriano Augusto, Raffaele Conforti, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi, Andrea Marrella, Massimo Mecella, and Allar Soo. 2018. Automated discovery of process models from event logs: Review and benchmark. IEEE transactions on knowledge and data engineering 31, 4 (2018), 686--705.
[5]
David Autor. 2014. Polanyi's paradox and the shape of employment growth. Technical Report. National Bureau of Economic Research.
[6]
Ioannis G Baltopoulos, Johannes Borgstr�m, and Andrew D Gordon. 2011. Maintaining database integrity with refinement types. In European Conference on Object-Oriented Programming. Springer, 484--509.
[7]
Rohan Bavishi, Erich Elsen, Curtis Hawthorne, Maxwell Nye, Augustus Odena, Arushi Somani, and Sağnak Taşırlar. 2023. Introducing our Multimodal Models. https://www.adept.ai/blog/fuyu-8b
[8]
Matthew Bayley and Ed Levine. 2013. Hospital revenue cycle operations: opportunities created by the ACA. Management (2013).
[9]
Michael Benedikt, Julien Leblay, and Efthymia Tsamoura. 2015. Querying with access patterns and integrity constraints. Proceedings of the VLDB Endowment 8, 6 (2015), 690--701.
[10]
Amanda Bergson-Shilcock and Roderick Taylor. 2023. Closing the Digital" Skill" Divide: The Payoff for Workers, Business, and the Economy. National Skills Coalition (2023).
[11]
Alessandro Berti and Mahnaz Sadat Qafari. 2023. Leveraging Large Language Models (LLMs) for Process Mining (Technical Report). arXiv preprint arXiv:2307.12701 (2023).
[12]
Anant Bhardwaj, David Karger, Harihar Subramanyam, Amol Deshpande, Sam Madden, Eugene Wu, Aaron Elmore, Aditya Parameswaran, and Rebecca Zhang. 2015. Collaborative data analytics with DataHub. In Proceedings of the VLDB Endowment International Conference on Very Large Data Bases, Vol. 8. NTH Public Access, 1916.
[13]
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021).
[14]
Erik Brynjolfsson, Danielle Li, and Lindsey R Raymond. 2023. Generative AI at work. Technical Report. National Bureau of Economic Research.
[15]
Fabio Casati and Ming-Chien Shan. 2000. Process automation as the foundation for e-business. In VLDB. Citeseer, 688--691.
[16]
Tathagata Chakraborti, Vatche Isahagian, Rania Khalaf, Yasaman Khazaeni, Vinod Muthusamy, Yara Rizk, and Merve Unuvar. 2020. From Robotic Process Automation to Intelligent Process Automation: -Emerging Trends-. In Business Process Management: Blockchain and Robotic Process Automation Forum: BPM 2020 Blockchain and RPA Forum, Seville, Spain, September 13--18, 2020, Proceedings 18. Springer, 215--228.
[17]
M Chui, E Hazan, R Roberts, A Singla, K Smaje, A Sukharevsky, L Yee, and R Zemmel. 2023. The economic potential of generative AI The next productivity frontier The economic potential of generative AI: The next productivity frontier.
[18]
Cristiano Andr� da Costa, U�lison Jean Lopes dos Santos, Eduardo Souza dos Reis, Rodolfo Stoffel Antunes, Henrique Chaves Pacheco, Thayn� da Silva Fran�a, Rodrigo da Rosa Righi, Jorge Luis Vict�ria Barbosa, Franklin Jebadoss, Jorge Montalvao, et al. 2023. Intelligent methods for business rule processing: State-of-the-art. arXiv preprint arXiv:2311.11775 (2023).
[19]
Laila Dahabiyeh and Omar Mowafi. 2023. Challenges of using RPA in auditing: A socio-technical systems approach. Intelligent Systems in Accounting, Finance and Management (2023).
[20]
Xiang Deng, Yu Gu, Boyuan Zheng, Shijie Chen, Samuel Stevens, Boshi Wang, Huan Sun, and Yu Su. 2023. Mind2Web: Towards a Generalist Agent for the Web. arXiv:2306.06070 [cs.CL]
[21]
Norman Di Palo, Arunkumar Byravan, Leonard Hasenclever, Markus Wulfmeier, Nicolas Heess, and Martin Riedmiller. 2023. Towards a unified agent with foundation models. arXiv preprint arXiv:2307.09668 (2023).
[22]
Marlon Dumas, Fabiana Fournier, Lior Limonad, Andrea Marrella, Marco Montali, Jana-Rebecca Rehse, Rafael Accorsi, Diego Calvanese, Giuseppe De Giacomo, Dirk Fahland, et al. 2023. AI-augmented business process management systems: a research manifesto. ACM Transactions on Management Information Systems 14, 1 (2023), 1--19.
[23]
Dirk Fahland, Fabian Fournier, Lior Limonad, Inna Skarbovsky, and Ava JE Swevels. 2024. How well can large language models explain business processes? arXiv preprint arXiv:2401.12846 (2024).
[24]
Dahlia Fernandez and Aini Aman. 2021. The challenges of implementing robotic process automation in global business services. International Journal of Business and Society 22, 3 (2021), 1269--1282.
[25]
Daocheng Fu, Xin Li, Licheng Wen, Min Dou, Pinlong Cai, Botian Shi, and Yu Qiao. 2024. Drive like a human: Rethinking autonomous driving with large language models. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 910--919.
[26]
Hiroki Furuta, Ofir Nachum, Kuang-Huei Lee, Yutaka Matsuo, Shixiang Shane Gu, and Izzeddin Gur. 2023. Multimodal Web Navigation with Instruction-Finetuned Foundation Models. arXiv preprint arXiv:2305.11854 (2023).
[27]
Diimitrios Georgakopoulos, Mark Hornick, and Amit Sheth. 1995. An overview of workflow management: From process modeling to workflow automation infrastructure. Distributed and parallel Databases 3 (1995), 119--153.
[28]
Michael Grohs, Luka Abb, Nourhan Elsayed, and Jana-Rebecca Rehse. 2023. Large Language Models can accomplish Business Process Management Tasks. In International Conference on Business Process Management. Springer, 453--465.
[29]
Izzeddin Gur, Hiroki Furuta, Austin Huang, Mustafa Safdari, Yutaka Matsuo, Douglas Eck, and Aleksandra Faust. 2023. A real-world webagent with planning, long context understanding, and program synthesis. arXiv preprint arXiv:2307.12856 (2023).
[30]
Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Yong Dai, Hongming Zhang, Zhenzhong Lan, and Dong Yu. 2024. WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models. arXiv:2401.13919 [cs.CL]
[31]
Sarah Calkins Holloway, Michael Peterson, Andrew MacDonald, and Bridget Scherbring Pollak. 2018. From revenue cycle management to revenue excellence.
[32]
Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, et al. 2023. Metagpt: Meta programming for multi-agent collaborative framework. arXiv preprint arXiv:2308.00352 (2023).
[33]
Wenyi Hong, Weihan Wang, Qingsong Lv, Jiazheng Xu, Wenmeng Yu, Junhui Ji, Yan Wang, Zihan Wang, Yuxiao Dong, Ming Ding, et al. 2023. CogAgent: A Visual Language Model for GUI Agents. arXiv preprint arXiv:2312.08914 (2023).
[34]
Richard Hull, Jianwen Su, and Roman Vaculin. 2013. Data management perspectives on business process management: tutorial overview. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. 943--948.
[35]
Peter C Humphreys, David Raposo, Tobias Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Adam Santoro, and Timothy Lillicrap. 2022. A data-driven approach for learning to control computers. In International Conference on Machine Learning. PMLR, 9466--9482.
[36]
Lucija Ivančić, Dalia Suša Vugec, and Vesna Bosilj Vukšić. 2019. Robotic process automation: systematic literature review. In Business Process Management: Blockchain and Central and Eastern Europe Forum: BPM 2019 Blockchain and CEE Forum, Vienna, Austria, September 1--6, 2019, Proceedings 17. Springer, 280--295.
[37]
Nicholas R. Jennings, Timothy J. Norman, and Peyman Faratin. 1998. ADEPT: An agent-based approach to business process management. ACM Sigmod Record 27, 4 (1998), 32--39.
[38]
Zhengbao Jiang, Frank F Xu, Jun Araki, and Graham Neubig. 2020. How can we know what language models know? Transactions of the Association for Computational Linguistics 8 (2020), 423--438.
[39]
Moe Kayali, Anton Lykov, Ilias Fountalis, Nikolaos Vasiloglou, Dan Olteanu, and Dan Suciu. 2023. CHORUS: Foundation Models for Unified Data Discovery and Exploration. arXiv preprint arXiv:2306.09610 (2023).
[40]
Victor Kilanko. 2023. Leveraging Artificial Intelligence for Enhanced Revenue Cycle Management in the United States. International Journal of Scientific Advances 4, 4 (2023), 505--14.
[41]
Volodymyr Leno, Artem Polyvyanyy, Marlon Dumas, Marcello La Rosa, and Fabrizio Maria Maggi. 2021. Robotic process mining: vision and challenges. Business & Information Systems Engineering 63 (2021), 301--314.
[42]
Xavier Lhuer. 2016. The next acronym you need to know about: RPA (robotic process automation). (2016).
[43]
Junyou Li, Qin Zhang, Yangbin Yu, Qiang Fu, and Deheng Ye. 2024. More agents is all you need. arXiv preprint arXiv:2402.05120 (2024).
[44]
Toby Jia-Jun Li, Marissa Radensky, Justin Jia, Kirielle Singarajah, Tom M Mitchell, and Brad A Myers. 2019. Interactive task and concept learning from natural language instructions and gui demonstrations. arXiv preprint arXiv:1909.00031 (2019).
[45]
Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Zhaopeng Tu, and Shuming Shi. 2023. Encouraging divergent thinking in large language models through multi-agent debate. arXiv preprint arXiv:2305.19118 (2023).
[46]
Xiaozhen Liu, Zuozhi Wang, Shengquan Ni, Sadeem Alsudais, Yicong Huang, Avinash Kumar, and Chen Li. 2022. Demonstration of collaborative and interactive workflow-based data analytics in texera. Proceedings of the VLDB Endowment 15, 12 (2022), 3738--3741.
[47]
Zhiwei Liu, Weiran Yao, Jianguo Zhang, Le Xue, Shelby Heinecke, Rithesh Murthy, Yihao Feng, Zeyuan Chen, Juan Carlos Niebles, Devansh Arpit, et al. 2023. Bolaa: Benchmarking and orchestrating llm-augmented autonomous agents. arXiv preprint arXiv:2308.05960 (2023).
[48]
Lin Ma, Dana Van Aken, Ahmed Hefny, Gustavo Mezerhane, Andrew Pavlo, and Geoffrey J Gordon. 2018. Query-based workload forecasting for self-driving database management systems. In Proceedings of the 2018 International Conference on Management of Data. 631--645.
[49]
Pedro Mejia-Alvarez, Luis Eduardo Leyva-del Foyo, and Arnaldo Diaz-Ramirez. 2018. Interrupt Handling Schemes in Operating Systems. Springer.
[50]
S�lvia Moreira, Henrique S Mamede, and Arnaldo Santos. 2023. Process automation using RPA-a literature review. Procedia Computer Science 219 (2023), 244--254.
[51]
Vinod Muthusamy, Yara Rizk, Kiran Kate, Praveen Venkateswaran, Vatche Isahagian, Ashu Gulati, and Parijat Dube. 2023. Towards large language model-based personal agents in the enterprise: Current trends and open problems. In Findings of the Association for Computational Linguistics: EMNLP 2023. 6909--6921.
[52]
Avanika Narayan, Ines Chami, Laurel Orr, and Christopher R�. 2022. Can Foundation Models Wrangle Your Data? Proceedings of the VLDB Endowment 16, 4 (2022), 738--746.
[53]
R OpenAI. 2023. GPT-4 technical report. arXiv (2023), 2303--08774.
[54]
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems 35 (2022), 27730--27744.
[55]
Joon Sung Park, Joseph O'Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. 2023. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. 1--22.
[56]
Andrew Pavlo, Gustavo Angulo, Joy Arulraj, Haibin Lin, Jiexi Lin, Lin Ma, Prashanth Menon, Todd C Mowry, Matthew Perron, Ian Quah, et al. 2017. Self-Driving Database Management Systems. In CIDR, Vol. 4. 1.
[57]
Andrew Pavlo, Matthew Butrovich, Lin Ma, Prashanth Menon, Wan Shen Lim, Dana Van Aken, and William Zhang. 2021. Make your database system dream of electric sheep: towards self-driving operation. Proceedings of the VLDB Endowment 14, 12 (2021), 3211--3221.
[58]
Arif Perdana, W Eric Lee, and Chu Mui Kim. 2023. Prototyping and implementing Robotic Process Automation in accounting firms: Benefits, challenges and opportunities to audit automation. International Journal of Accounting Information Systems 51 (2023), 100641.
[59]
R1. 2022. Healthcare Financial Trends Report. https://www.r1rcm.com/news/healthcare-trends-and-data-show-clinical-shortage-tip-of-the-iceberg
[60]
Habibur Rahman, Saravanan Thirumuruganathan, Senjuti Basu Roy, Sihem Amer-Yahia, and Gautam Das. 2015. Worker skill estimation in team-based tasks. Proceedings of the VLDB Endowment 8, 11 (2015), 1142--1153.
[61]
Lars Reinkemeyer. 2020. Process mining in action. Process Mining in Action Principles, Use Cases and Outloook (2020).
[62]
Yara Rizk, Praveen Venkateswaran, Vatche Isahagian, Austin Narcomey, and Vinod Muthusamy. 2023. A Case for Business Process-Specific Foundation Models. In International Conference on Business Process Management. Springer, 44--56.
[63]
Tara Safavi and Danai Koutra. 2021. Relational world knowledge representation in contextual language models: A review. arXiv preprint arXiv:2104.05837 (2021).
[64]
Sagar Sahu, Sania Salwekar, Atharva Pandit, and Manoj Patil. 2020. Invoice processing using robotic process automation. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol 6, 2 (2020), 216--223.
[65]
Henriika Sarilo-Kankaanranta and Lauri Frank. 2021. The Slow Adoption Rate of Software Robotics in Accounting and Payroll Services and the Role of Resistance to Change in Innovation-Decision Process. In Conference of the Italian Chapter of AIS. Springer, 201--216.
[66]
Mehmet Sayal, Fabio Casati, Umeshwar Dayal, and Ming-Chien Shan. 2002. Business process cockpit. In VLDB'02: Proceedings of the 28th International Conference on Very Large Databases. Elsevier, 880--883.
[67]
Fred Schulte and Erika Fry. 2019. Death by 1,000 clicks: Where electronic health records went wrong. Kaiser Health News 18 (2019).
[68]
Peter Shaw, Mandar Joshi, James Cohan, Jonathan Berant, Panupong Pasupat, Hexiang Hu, Urvashi Khandelwal, Kenton Lee, and Kristina Toutanova. 2023. From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces. arXiv preprint arXiv:2306.00245 (2023).
[69]
Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. 2023. HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face. arXiv:2303.17580 [cs.CL]
[70]
Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023. Reflexion: Language Agents with Verbal Reinforcement Learning.(2023). arXiv preprint cs.AI/2303.11366 (2023).
[71]
Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher D Manning. 2023. Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models finetuned with human feedback. arXiv preprint arXiv:2305.14975 (2023).
[72]
UIPath. 2022. UiPath Certified RPA Associate v1.0 - EXAM Description.pdf. https://start.uipath.com/rs/995-XLT-886/images/UiPath%20Certified%20RPA%20Associate%20v1.0%20-%20EXAM%20Description.pdf
[73]
Wil MP Van der Aalst. 2014. Process mining in the large: a tutorial. Business Intelligence: Third European Summer School, eBISS 2013, Dagstuhl Castle, Germany, July 7--12, 2013, Tutorial Lectures 3 (2014), 33--76.
[74]
Maxim Vidgof, Stefan Bachhofner, and Jan Mendling. 2023. Large Language Models for Business Process Management: Opportunities and Challenges. arXiv preprint arXiv:2304.04309 (2023).
[75]
Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. 2023. Voyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291 (2023).
[76]
Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Xixuan Song, et al. 2023. Cogvlm: Visual expert for pretrained language models. arXiv preprint arXiv:2311.03079 (2023).
[77]
Zihao Wang, Shaofei Cai, Anji Liu, Yonggang Jin, Jinbing Hou, Bowei Zhang, Haowei Lin, Zhaofeng He, Zilong Zheng, Yaodong Yang, et al. 2023. Jarvis-1: Open-world multi-task agents with memory-augmented multimodal language models. arXiv preprint arXiv:2311.05997 (2023).
[78]
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35 (2022), 24824--24837.
[79]
Judith Wewerka and Manfred Reichert. 2020. Robotic Process Automation-A Systematic Literature Review and Assessment Framework. arXiv preprint arXiv:2012.11951 (2020).
[80]
Jason Wu, Siyan Wang, Siman Shen, Yi-Hao Peng, Jeffrey Nichols, and Jeffrey P Bigham. 2023. WebUI: A Dataset for Enhancing Visual UI Understanding with Web Semantics. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1--14.
[81]
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023. Autogen: Enabling next-gen llm applications via multi-agent conversation framework. arXiv preprint arXiv:2308.08155 (2023).
[82]
Zhiyong Wu, Chengcheng Han, Zichen Ding, Zhenmin Weng, Zhoumianze Liu, Shunyu Yao, Tao Yu, and Lingpeng Kong. 2024. OS-Copilot: Towards Generalist Computer Agents with Self-Improvement. arXiv preprint arXiv:2402.07456 (2024).
[83]
An Yan, Zhengyuan Yang, Wanrong Zhu, Kevin Lin, Linjie Li, Jianfeng Wang, Jianwei Yang, Yiwu Zhong, Julian McAuley, Jianfeng Gao, et al. 2023. Gpt-4v in wonderland: Large multimodal models for zero-shot smartphone gui navigation. arXiv preprint arXiv:2311.07562 (2023).
[84]
Jianwei Yang, Hao Zhang, Feng Li, Xueyan Zou, Chunyuan Li, and Jianfeng Gao. 2023. Set-of-mark prompting unleashes extraordinary visual grounding in gpt-4v. arXiv preprint arXiv:2310.11441 (2023).
[85]
Zhao Yang, Jiaxuan Liu, Yucheng Han, Xin Chen, Zebiao Huang, Bin Fu, and Gang Yu. 2023. AppAgent: Multimodal Agents as Smartphone Users. arXiv preprint arXiv:2312.13771 (2023).
[86]
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629 (2022).
[87]
Yining Ye, Xin Cong, Shizuo Tian, Jiannan Cao, Hao Wang, Yujia Qin, Yaxi Lu, Heyang Yu, Huadong Wang, Yankai Lin, et al. 2023. ProAgent: From Robotic Process Automation to Agentic Process Automation. arXiv preprint arXiv:2311.10751 (2023).
[88]
Liangzhao Zeng, Boualem Benatallah, Phuong Nguyen, and Anne HH Ngu. 2001. Agflow: Agent-based cross-enterprise workflow management system. In VLDB. 697--698.
[89]
Chaoyun Zhang, Liqun Li, Shilin He, Xu Zhang, Bo Qiao, Si Qin, Minghua Ma, Yu Kang, Qingwei Lin, Saravan Rajmohan, et al. 2024. UFO: A UI-Focused Agent for Windows OS Interaction. arXiv preprint arXiv:2402.07939 (2024).
[90]
Jingyi Zhang, Jiaxing Huang, Sheng Jin, and Shijian Lu. 2023. Vision-Language Models for Vision Tasks: A Survey. arXiv:2304.00685 [cs.CV]
[91]
Boyuan Zheng, Boyu Gou, Jihyung Kil, Huan Sun, and Yu Su. 2024. GPT-4V(ision) is a Generalist Web Agent, if Grounded. arXiv:2401.01614 [cs.IR]
[92]
Shuyan Zhou, Frank F Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Yonatan Bisk, Daniel Fried, Uri Alon, et al. 2023. Webarena: A realistic web environment for building autonomous agents. arXiv preprint arXiv:2307.13854 (2023).
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