Agent-Based Orchestration of Dynamic Data Ingestion for Resilient Enterprise Schema Governance

Abstract

Enterprise data landscapes are increasingly characterized by heterogeneous sources, continuous data streams, and rapid schema evolution. Traditional schema governance approaches, designed for relatively static and centrally curated data models, encounter limitations when confronted with dynamic ingestion patterns and frequent structural changes across systems. At the same time, organizations require stable views of critical entities to support analytics, compliance reporting, and operational decision making, even as upstream schemas change without coordinated planning. This paper examines an agent-based orchestration perspective on dynamic data ingestion, in which semi-autonomous software agents negotiate, coordinate, and adapt ingestion behavior under explicit schema governance policies. The proposed view treats ingestion pipelines, schema registries, and governance policies as components of a distributed control problem, where agents manage local decisions about mapping, transformation, and validation while aligning with enterprise-wide constraints. The discussion develops an architectural decomposition of ingestion and governance responsibilities into specialized agents, a linear modeling framework for capturing state, decision, and policy interactions, and resilience mechanisms that handle schema drift, partial failures, and inconsistent updates. The paper also outlines evaluation scenarios and qualitative observations regarding observability, controllability, and trade-offs between local autonomy and global policy adherence. The overall intent is to describe how agent-based orchestration can structure reasoning about dynamic data ingestion in complex enterprises and to demonstrate how linear models can be used to reason about the stability, safety, and robustness of schema governance processes under continuous change.