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

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

AegisFlow automates repair tasks, freeing human engineers from operational fatigue.

This is a narrow technical automation of IT operations tasks, not a societal claim. Potential implications include reduced engineering toil (mild positive) but also reduced oversight of autonomous code deployment (mild risk). Evidence is a single unreviewed preprint with self-reported metrics on five scenarios.

The paper introduces AegisFlow, an AI framework designed for autonomous data pipeline remediation. It claims substantial improvements in Mean Time to Repair and a high patch success rate across various failure scenarios, indicating a significant positive impact on operational efficiency and human workload.

AegisFlow significantly improves data pipeline reliability and efficiency. It reduces repair time and frees up engineering resources for innovation. The results are well-supported by experimental testing.

SOURCE     arXiv cs.AI (tier 1)
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DOCUMENT   e6da1806-fe21-4ac2-bf99-37d850566c14
           https://arxiv.org/abs/2610.06971
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EVIDENCE   1 extracted excerpt
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      +9  (Mixed or uncertain)
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CONFIDENCE 74%