Authors : Shatrunjay Kumar Singh

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/yr4f4ubp

Scribd : https://tinyurl.com/yzy2366r

DOI : https://doi.org/10.38124/ijisrt/26apr2004

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Regulated organizations routinely absorb policy changes through a fragmented manual workflow in which analysts detect updates, interpret downstream consequences, assign owners, and write change notes under time pressure. This paper proposes a Regulatory Change Propagation Agent, a multi-agent architecture that detects regulatory differences and orchestrates structured follow-up work while preserving human approval gates. The design combines a diff engine, a dependency-graph reasoning layer, and notifier-authoring agents that transform upstream legal or policy changes into targeted work packets. To evaluate the approach, the paper defines four operational metrics: detection latency, coverage of affected sections, precision of impact mapping, and reviewer acceptance rate. A prototype evaluation on a synthetic but policy-shaped corpus suggests that agentic orchestration can materially reduce time-to-detection and improve consistency of impact analysis relative to manual baselines. The contribution of the paper is not only an automation pipeline, but also a governance pattern: automation performs first-pass propagation, while human reviewers remain accountable for approval, override, and publication.

Keywords : Regulatory Intelligence, Change Propagation, Agent Systems, Policy Operations, Workflow Orchestration, Human-In-TheLoop Governance.

References :

1. National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1.
2. National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework: Generative AI Profile, NIST AI 600-1, 2024.
3. World Wide Web Consortium, PROV-Overview: An Overview of the PROV Family of Documents.
4. World Wide Web Consortium, PROV-DM: The PROV Data Model.
5. World Wide Web Consortium, PROV-O: The PROV Ontology.
6. World Wide Web Consortium, PROV Primer.
7. Human-in-the-Loop: The Loop Isn't a Step, It's a Full Circle, ACM Interactions.
8. Trust, Regulation, and Human-in-the-Loop AI, Communications of the ACM.
9. HINT: Human-AI Integration Testing, ACM CHI Extended Abstracts.
10. Knowledge Graphs, Communications of the ACM.
11. Defining a Knowledge Graph Development Process Through a Systematic Review, ACM Computing Surveys / related review literature.
12. On the Quest for Effectiveness in Human Oversight, ACM FAccT / interdisciplinary oversight literature.
13. A. V. Aho and J. D. Ullman, Principles of text differencing and structured comparison.
14. X. Chen et al., Human-in-the-loop workflow design patterns for trustworthy enterprise AI systems.
15. S. K. Singh, Original conceptual framework for agentic legal and policy operations, including controlled drafting and doctrinal dependency mapping.
16. Policy operations practice notes on change management, traceability, and controlled publication workflows.