Provenance For Autonomous Agents: What Regulated Enterprises Must Reconstruct After An AI Agent Acts
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.
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