Models and Concepts of AI Agents in Financial Operations for Autonomous Payroll Processing

( Vol-12,Issue-2,March - April 2026 )

Author(s): Shanmuka Siva Varma Chekuri

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Page No: 033-039
DOI: 10.22161/ijaems.122.5

Keywords:

autonomous payroll, AI agents, financial data engineering, lakehouse architecture, feature store, metadata-driven orchestration, idempotent pipelines, compliance automation, multi-agent systems, real-time reconciliation.

Abstract:

The study examines models and architectural concepts of AI agents embedded in financial operations to support autonomous payroll processing in multi-region, compliance-sensitive environments. The research focus lies on agentic patterns that orchestrate payroll data flows end-to-end over ACID-compliant lakehouse platforms, feature stores, and metadata-driven orchestration layers. The work synthesizes current literature on AI agents in finance, autonomous decision systems, data lakehouse architectures, and feature-store-centric machine learning pipelines, and combines it with design patterns emerging in production-grade payroll and HR systems. Particular attention is given to deterministic computation graphs, idempotent pipelines, concurrency-safe merge patterns, and real-time observability for reconciliation and audit. The article aims to formulate a conceptual model of an autonomous payroll stack, outline classes of domain-specific agents, and identify their limitations and governance needs. The material is intended for researchers and practitioners in AI, data engineering, and financial systems design.

Received: 29 Jan 2026; Received in revised form: 25 Feb 2026; Accepted:02 Mar 2026; Available online: 06 Mar 2026

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