AegisFlow: A Multi-Agent Agentic AI Framework for Autonomous Remediation and Self-Healing in Fragile Data Ecosystems
arXiv cs.AIen
arXiv cs.AI
AI Global WirearXiv: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 o
This is a short summary published by AI Global Wire. The full article is owned and hosted by arXiv cs.AI — open it there to read it in full.
Read the full story at arXiv cs.AI- Verktyg
- Forskning
- Agenter
Related AI news
- AI demand drives Episil SiC V-shaped recovery as GaN and TVS capacity reaches full utilizationDIGITIMES · October 8, 2026
- Singtel signs MOU with SIT on sovereign AITech in Asia · October 8, 2026
- Google debuts SynthID Detector tool for flagging AI-generated content, but it’s far from perfectSiliconANGLE · October 8, 2026
- Chang Wah Technology posts record revenue as demand broadens across electronics and AIDIGITIMES · October 8, 2026
- Pricey local AI machines arrive as memory costs threaten PC shipmentsDIGITIMES · October 8, 2026
- At WebexOne, Cisco’s Jeetu Patel argues the agent era will be won on context, cost and controlSiliconANGLE · October 8, 2026