@article{Chekuri_2026, title={Models and Concepts of AI Agents in Financial Operations for Autonomous Payroll Processing}, volume={11}, url={http://dx.doi.org/10.22161/ijaems.122.5}, DOI={10.22161/ijaems.122.5}, abstractNote={<jats:p>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.</jats:p>}, number={2}, journal={International Journal of Advanced Engineering, Management and Science}, publisher={AI Publications}, author={Chekuri, Shanmuka Siva Varma}, year={2026}, pages={33–39} }