Abstract
Modern enterprise data platforms increasingly operate under conditions of extreme scale, heterogeneity, and uncertainty. Traditional data pipeline orchestration frameworks rely on static Directed Acyclic Graphs (DAGs) and deterministic retry semantics, which are fundamentally misaligned with environments characterized by schema volatility, infrastructure churn, and non-stationary workloads. This paper presents a comprehensive architectural model for Multi-Agent Orchestrated Data Pipelines (MODP), where autonomous agents replace task-centric orchestration with goal-driven reasoning.The architecture integrates four primary subsystems: an Agent Orchestrator, a Knowledge Plane grounded in Retrieval-Augmented Generation (RAG), a Unified Feature Store, and a Causal Tracing Engine. Together, these components enable self-healing execution, dynamic schema adaptation, and causal observability across the data lifecycle. Empirical evidence from large-scale distributed systems research demonstrates that agent-based orchestration improves fault tolerance, reduces mean time to recovery (MTTR), and significantly enhances developer productivity. This work formalizes agentic data engineering as a shift from procedural execution to intent-based systems, positioning autonomous multi-agent orchestration as a foundational design principle for next-generation data platforms.