AegisFlow：用于脆弱数据生态系统中自主修复与自愈的多智能体代理式AI框架
AegisFlow: A Multi-Agent Agentic AI Framework for Autonomous Remediation and Self-Healing in Fragile Data Ecosystems
浏览论文内容
中文总结 AI 辅助
AegisFlow提出多智能体代理框架，利用LLM自动生成和验证补丁，通过并行影子修补在数字孪生环境中修复脆弱数据管道，将MTTR从170分钟降至3.2分钟，补丁成功率达92%。
中文摘要 AI 辅助
传统数据管道以脆弱著称，常因上游模式漂移、API契约变更或网站DOM修改而失败。现有的可观测性工具仅向人类工程师发出警报，导致平均修复时间（MTTR）高企和运维疲劳。在本文中，我们提出AegisFlow（用于智能自愈和基于图的工作负载修复操作的代理式引擎），这是一种新颖的代理式框架，弥合了检测与解决之间的鸿沟。AegisFlow使用Watchdog代理收集运行时遥测数据，并设有Repair代理，基于大型语言模型（LLMs）自动创建、测试和部署代码补丁。该框架提出了一种非侵入式执行模型，称为并行影子修补（Parallel Shadow Patching），这是一种基于监控、分析、计划、执行、知识（MAPE-K）循环的非侵入式执行模型，用于在数字孪生环境中生成和验证补丁。通过实验测试，我们在五种常见故障场景中评估了AegisFlow，观察到MTTR提高了98.1%（从平均每次补丁170分钟降至3.2分钟），补丁成功率为92%。特别是，该系统在处理JSON模式变更（96%）和标点漂移（98%）方面表现出色，而在Shadow DOM情况下成功率最低（85%）。AegisFlow释放了约98%的数据工程待命时间，从救火式工作中解脱出来，并将其重新分配给创新工作。该框架与部署无关，由一个系统组成，可以以插件方式部署到现有管道编排系统中，对现有系统的提升最小。
英文摘要
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.
发表机构
- Ministry of Defense（国防部）
- IQRA University（伊克拉大学）
- South East Technological University（东南理工大学）
机构由 AI 辅助整理，请以论文原文为准。
