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AegisFlow: A Multi-Agent Agentic AI Framework for Autonomous Remediation and Self-Healing in Fragile Data Ecosystems
Publication: cs.AI updates on arXiv.org
Author: Muhammad Bilal Awan, Zubair Hussain, Abdul Shahid
Source published: 07/10/2026, 04:00:00 UTC
Briefing created: 04/11/2026, 02:30:27 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 08/10/2026, 02:18:24 UTC.
Briefings
AegisFlow is an agentic AI framework designed to automate data remediation by using a Watchdog agent for telemetry collection and a Repair agent powered by LLMs to generate code patches. Evaluated across five failure scenarios, the system achieved a 98.1% improvement in Mean Time to Repair (MTTR) and a 92% patch success rate, with specific success rates of 96% for JSON schema changes and 98% for punctuation drift. The framework operates via a non-intrusive Parallel Shadow Patching model using the MAPE-K loop in digital twin environments, freeing up 98% of data engineering on-call time for innovation. It is deployment agnostic, allowing integration into existing pipelines with minimal system uplift.
Key points
- AegisFlow employs a Watchdog agent to collect runtime telemetry and a Repair agent utilizing Large Language Models to automatically generate and deploy code patches.
- The framework achieved a 98.1% improvement in Mean Time to Repair (MTTR) and a 92% patch success rate across five failure scenarios.
Context
This research addresses the high Mean Time to Repair (MTTR) and operational fatigue caused by brittle data pipelines. AegisFlow closes the loop between detection and resolution through a non-intrusive execution model, offering a scalable solution for fragile data ecosystems.
Limitations
Experimental evaluation was conducted across five common failure scenarios. Specific success rates vary by scenario, with Shadow DOM cases showing lower performance (85%) compared to JSON schema (96%) and punctuation drift (98%) changes. The framework's effectiveness depends on the specific failure patterns encountered during testing.
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