AegisFlow: A Multi-Agent Agentic AI Framework for Autonomous Remediation and Self-Healing in Fragile Data Ecosystems

AegisFlow, a multi-agent framework described in arXiv paper 2610.06971v1, cut Mean Time to Repair for fragile data pipelines by 98.1 percent, from an average of 170 minutes per patch to 3.2 minutes, with a 92 percent patch success rate across five common failure scenarios. The framework pairs a Watchdog agent that collects runtime telemetry with a Repair agent that uses large language models to create, test and deploy code patches via Parallel Shadow Patching, a non-intrusive execution model based on the MAPE-K loop that verifies patches in digital twin environments. AegisFlow succeeded on 96 percent of JSON schema changes and 98 percent of punctuation drift cases but was least successful on Shadow DOM cases at 85 percent, and the authors say it frees about 98 percent of data engineering on-call time from firefighting.

arXiv:2610.06971v1 Announce Type: new Abstract: Traditional data pipelines are notoriously brittle, often failing due to upstream schema drift, API contract changes, or website DOM modifications. Present observability tools only raise alerts but for human engineers, resulting in a high Mean Time to Repair MTTR and operational fatigue. In this paper we propose AegisFlow Agentic Engine for Intelligent Self-healing and Graph-driven Operations for Workload remediation , a novel agentic framework that closes the loop between detection and resolution. AegisFlow uses a Watchdog agent to collect runtime telemetry and has a Repair agent to automatically create, test and deploy code patches based on Large Language Models LLMs . The framework presents the non-intrusive execution model called Parallel Shadow Patching, a non-intrusive execution model based on the Monitor, Analyze, Plan, Execute, Knowledge MAPE-K loop to generate and verify patches in digital twin environments. Through experimental testing, we have evaluated AegisFlow across five common failure scenarios, and see 98.1 percent improvement in MTTR from an average of 170 minutes per patch to 3.2 minutes and a patch success rate of 92 percent . In particular, the system is successful in dealing with changes in the JSON schema 96 percent and punctuation drift 98 percent , and is least successful in Shadow DOM cases 85 percent . AegisFlow frees up about 98 percent of data engineering on-call time from firefighting and reallocates it towards innovation. The framework is deployment agnostic consisting of a system that can be deployed in a plugin fashion into an existing pipeline orchestration system with minimal uplift to the existing system.