Self-Healing Enterprise Data Pipelines An Agentic DataOps Architecture for Autonomous Detection, Diagnosis and Recovery

Abstract

Enterprise data platforms have become operationally complex systems composed of batch and streaming pipelines, CDC flows, SaaS connectors, APIs, file-based ingestion, transformation frameworks, cloud warehouses, lakehouses and downstream analytical products. Traditional DataOps practices can detect many failures, but recovery remains heavily dependent on human operators who interpret alerts, reconstruct execution context, identify likely root causes and decide whether a retry, rollback, replay, scale-up, quarantine or code change is safe. As pipeline estates grow and service-level expectations tighten, this human-centric operating model becomes increasingly difficult to scale.

This article proposes an Agentic DataOps architecture for self-healing enterprise data pipelines. The model treats self-healing not as automatic retry logic, but as a governed closed loop comprising detection, context retrieval, diagnosis, recovery planning, policy evaluation, execution, validation and learning. The paper introduces three original components: the Agentic DataOps Control Loop (ADCL), a five-level Pipeline Autonomy Model, and an Autonomous Recovery Confidence Index (ARCI) that combines diagnostic confidence, action reversibility, business criticality, blast radius, data-integrity risk and prior recovery evidence.

The central argument is that autonomous recovery should be bounded by evidence and policy. Low-risk, reversible failures can be handled automatically; high-impact failures should remain human-approved even when the agent has a strong diagnosis. The framework is intentionally platform-neutral and is applicable across SAP-centric estates, hyperscaler data platforms, lakehouses, orchestration frameworks and streaming architectures. Current 2026 developments in BigQuery, Databricks, observability tooling and agentic data management indicate that the industry is moving from AI-assisted development toward machine-operated data platforms. The missing layer is a disciplined architecture for safe runtime autonomy.