Cloud-based data pipelines increasingly power machine learning, analytics, and decision automation, yet their reliability is constrained by two coupled failure modes: (i) software defects in orchestration and processing code, and (ii) silent data quality degradation that propagates to downstream features and predictions. This paper proposes an automated, AI-driven monitoring and observability framework that unifies telemetry (logs, metrics, traces), pipeline metadata, and defect prediction into a closed-loop operations capability. The framework integrates standardized instrumentation and collection, an analytics layer for anomaly detection and drift monitoring, a defect prediction subsystem that learns fault-proneness signals from historical builds and runtime indicators, and a root-cause analysis (RCA) component that leverages dependency graphs derived from traces and lineage. A governance layer encodes service-level objectives (SLOs), data quality expectations, and remediation policies to reduce mean time to detect (MTTD) and mean time to recover (MTTR) while improving trust in pipeline outputs. We present a reference architecture, feature schema, and evaluation protocol, and we demonstrate how the approach supports actionable observability across batch and streaming workloads, enabling proactive interventions before defects and data issues impact consumers.