Traditional data pipelines are notoriously brittle: upstream schema drift, API contract changes, or website DOM modifications frequently cause failures. Existing observability tools only raise alerts, leaving engineers to manually intervene, which results in a high mean time to repair (MTTR) and operational fatigue. To address this, we introduce AegisFlow (Agentic Engine for Intelligent Self‑healing and Graph‑driven Operations for Workload remediation), a closed‑loop agentic framework that automatically creates, tests, and deploys code patches once an anomaly is detected.
AegisFlow consists of two core agents:
- Watchdog agent continuously gathers runtime telemetry, detecting schema drift, API changes, and other anomalies;
- Repair agent leverages large language models (LLMs) to generate patch code and validates it in a digital twin environment using Parallel Shadow Patching.
The framework follows the MAPE‑K (Monitor‑Analyze‑Plan‑Execute‑Knowledge) loop: monitoring is performed by Watchdog, analysis and planning are driven by LLM‑inferred repair strategies, execution runs the patch safely in the shadow environment, and successful patches are promoted to the production system while updating the knowledge base. This non‑intrusive execution model can be plugged into existing pipeline orchestration systems without affecting live workloads.
Experimental evaluation across five common failure scenarios shows that AegisFlow reduces MTTR from an average of 170 minutes to 3.2 minutes—a 98.1% improvement—and achieves a patch success rate of 92%. Success rates are 96% for JSON schema changes, 98% for punctuation drift, and 85% for Shadow DOM cases, which remain the most challenging.
By automating remediation, AegisFlow frees roughly 98% of on‑call data engineering time, allowing engineers to shift from firefighting to innovation. Its deployment‑agnostic, plugin‑style architecture further minimizes integration overhead.
Review