Open Access
Provenance For Autonomous Agents: What Regulated Enterprises Must Reconstruct After An AI Agent Acts
4
Independent Researchers USA
Abstract
Organizational decisions are linked to actions through autonomous AI agents which influence employees' work and operational delivery. The aim of this study is to propose an integrated framework for provenance in regulated enterprises across human resource management (HRM), labour management (LM), operations management (OM) and industrial relations (IR). The research is an integrative synthesis of 40 different research publications between 2001 and 2026 and descriptive secondary analysis of 20 publicly reported survey estimates. The literature is structured around four themes: Provenance and accountability, HR and organizational decisions, labor and industrial relations and human-AI operational performance. The synthesis sets out four interconnected requirements: the same context for both sources and execution, explicit delegation and human contribution, linkages between employment arrangements and operational delivery, and traceable participation and evaluation. These requirements are translated into eight proposed evidence dimensions that include mandate, rules, inputs, configuration, execution, participation, outcomes and stewardship. The differences in the shares reporting positive workforce and operational outcomes, based on public employer consultation data, range from 9.30 to 15.00 percentage points. Implementation context is provided by training, managerial decision and governance indicators. The main analytical result is the link between technical execution and institutional arrangements for people and work that is established through meaningful provenance. The study provides a 40-study evidence matrix, four research figures, a public data comparison that can be replicated, and seven proposed evaluation measures to help guide future workforce decisions and operational coordination and evaluate reconstruction quality in enterprises.
Keywords
autonomous agents,
provenance,
human resource management
References
Autio, C., Schwartz, R., Dunietz, J., Jain, S., Stanley, M., Tabassi, E., Hall, P., & Roberts, K. (2024). Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile. NIST AI 600-1. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.600-1
Bankins, S., & Formosa, P. (2023). The ethical implications of artificial intelligence (AI) for meaningful work. Journal of Business Ethics, 185, 725–740. https://doi.org/10.1007/s10551-023-05339-7
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. S. (2021). Does the whole exceed its parts? The effect of AI explanations on complementary team performance. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–16. Association for Computing Machinery. https://doi.org/10.1145/3411764.3445717
Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae044
Buneman, P., Khanna, S., & Tan, W.-C. (2001). Why and where: A characterization of data provenance. In J. Van den Bussche & V. Vianu (Eds.), Database Theory—ICDT 2001 (Lecture Notes in Computer Science, Vol. 1973, pp. 316–330). Springer. https://doi.org/10.1007/3-540-44503-X_20
Cheney, J., Chiticariu, L., & Tan, W.-C. (2009). Provenance in databases: Why, how, and where. Foundations and Trends in Databases, 1(4), 379–474. https://doi.org/10.1561/1900000006
Davidson, S. B., & Freire, J. (2008). Provenance and scientific workflows: Challenges and opportunities. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data (pp. 1345–1350). Association for Computing Machinery. https://doi.org/10.1145/1376616.1376772
Dell’Acqua, F., McFowland, E., III, Mollick, E., Lifshitz, H., Kellogg, K. C., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2026). Navigating the jagged technological frontier: Field experimental evidence of the effects of artificial intelligence on knowledge worker productivity and quality. Organization Science, 37(2), 403–423. https://doi.org/10.1287/orsc.2025.21838
Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid intelligence. Business & Information Systems Engineering, 61(5), 637–643. https://doi.org/10.1007/s12599-019-00595-2
Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020). Algorithmic management and app-work in the gig economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30(1), 114–132. https://doi.org/10.1111/1748-8583.12258
European Parliament and Council of the European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence. Adopted text of 13 June 2024, Articles 12, 14 and 26 and Annex III. Official Journal of the European Union. https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
Fügener, A., Grahl, J., Gupta, A., & Ketter, W. (2022). Cognitive challenges in human–artificial intelligence collaboration: Investigating the path toward productive delegation. Information Systems Research, 33(2), 678–696. https://doi.org/10.1287/isre.2021.1079
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92. https://doi.org/10.1145/3458723
Herschel, M., Diestelkämper, R., & Ben Lahmar, H. (2017). A survey on provenance: What for? What form? What from? The VLDB Journal, 26(6), 881–906. https://doi.org/10.1007/s00778-017-0486-1
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
Lane, M., Williams, M., & Broecke, S. (2023). The impact of AI on the workplace: Main findings from the OECD AI surveys of employers and workers. OECD Social, Employment and Migration Working Papers, No. 288. OECD Publishing. https://doi.org/10.1787/ea0a0fe1-en
Langer, M., König, C. J., & Papathanasiou, M. (2019). Highly automated job interviews: Acceptance under the influence of stakes. International Journal of Selection and Assessment, 27(3), 217–234. https://doi.org/10.1111/ijsa.12246
Lee, M. K., Kusbit, D., Metsky, E., & Dabbish, L. (2015). Working with machines: The impact of algorithmic and data-driven management on human workers. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1603–1612). ACM. https://doi.org/10.1145/2702123.2702548
Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics, 160(2), 377–392. https://doi.org/10.1007/s10551-019-04204-w
Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3–26. https://doi.org/10.1080/09585192.2016.1244699
Meijerink, J., & Bondarouk, T. (2023). The duality of algorithmic management: Toward a research agenda on HRM algorithms, autonomy and value creation. Human Resource Management Review, 33(1), Article 100876. https://doi.org/10.1016/j.hrmr.2021.100876
Milanez, A., Lemmens, A., & Ruggiu, C. (2025). Algorithmic management in the workplace: New evidence from an OECD employer survey. OECD Artificial Intelligence Papers, No. 31. OECD Publishing. https://doi.org/10.1787/287c13c4-en
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220–229). Association for Computing Machinery. https://doi.org/10.1145/3287560.3287596
Moreau, L., & Missier, P. (Eds.). (2013). PROV-DM: The PROV Data Model. W3C Recommendation, 30 April 2013. https://www.w3.org/TR/prov-dm/
Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586
OECD. (2023). OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market. OECD Publishing. Figure 7.3, Panel B. https://doi.org/10.1787/08785bba-en. Associated data: https://stat.link/34bepy.
OECD. (2026). Agentic AI in organisations: Early insights from practitioner interviews. OECD Artificial Intelligence Papers, No. 65. OECD Publishing. https://doi.org/10.1787/1257a26f-en
Parent-Rocheleau, X., & Parker, S. K. (2022). Algorithms as work designers: How algorithmic management influences the design of jobs. Human Resource Management Review, 32(3), Article 100838. https://doi.org/10.1016/j.hrmr.2021.100838
Parker, S. K., & Grote, G. (2022). Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied Psychology, 71(4), 1171–1204. https://doi.org/10.1111/apps.12241
Pasquier, T., Han, X., Goldstein, M., Moyer, T., Eyers, D., Seltzer, M., & Bacon, J. (2017). Practical whole-system provenance capture. In Proceedings of the 2017 Symposium on Cloud Computing (pp. 405–418). Association for Computing Machinery. https://doi.org/10.1145/3127479.3129249
Raisch, S., & Fomina, K. (2025). Combining human and artificial intelligence: Hybrid problem-solving in organizations. Academy of Management Review, 50(2), 441–464. https://doi.org/10.5465/amr.2021.0421
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33–44). Association for Computing Machinery. https://doi.org/10.1145/3351095.3372873
Rani, U., Pesole, A., & González Vázquez, I. (2024). Algorithmic management practices in regular workplaces: Case studies in logistics and healthcare (EUR 31846 EN; JRC136063). Publications Office of the European Union. https://doi.org/10.2760/712475
Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber’s drivers. International Journal of Communication, 10, 3758–3784. https://ijoc.org/index.php/ijoc/article/view/4892
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257
Simmhan, Y. L., Plale, B., & Gannon, D. (2005). A survey of data provenance in e-science. ACM SIGMOD Record, 34(3), 31–36. https://doi.org/10.1145/1084805.1084812
Souza, R., Gueroudji, A., DeWitt, S., Rosendo, D., Ghosal, T., Ross, R., Balaprakash, P., & Ferreira da Silva, R. (2025). PROV-AGENT: Unified provenance for tracking AI agent interactions in agentic workflows. In Proceedings of the 2025 IEEE International Conference on e-Science (pp. 467–473). IEEE. https://doi.org/10.1109/eScience65000.2025.00093
Tabassi, E. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910
Vaccaro, M., Almaatouq, A., & Malone, T. (2024). When combinations of humans and AI are useful: A systematic review and meta-analysis. Nature Human Behaviour, 8(12), 2293–2303. https://doi.org/10.1038/s41562-024-02024-1
Vallas, S., & Schor, J. B. (2020). What do platforms do? Understanding the gig economy. Annual Review of Sociology, 46, 273–294. https://doi.org/10.1146/annurev-soc-121919-054857
Wood, A. J., & Lehdonvirta, V. (2021). Antagonism beyond employment: How the ‘subordinated agency’ of labour platforms generates conflict in the remote gig economy. Socio-Economic Review, 19(4), 1369–1396. https://doi.org/10.1093/ser/mwab016
Wood, A. J., Graham, M., Lehdonvirta, V., & Hjorth, I. (2019). Good gig, bad gig: Autonomy and algorithmic control in the global gig economy. Work, Employment and Society, 33(1), 56–75. https://doi.org/10.1177/0950017018785616
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