AegisFlow
A multi-agent Agentic AI framework designed for self-healing data pipelines. It combines Watchdog and Repair agent types with a MAPE-K control loop to enable automatic fault detection and remediation. The framework supports non-intrusive patch validation through a Parallel Shadow Patching mechanism.
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AegisFlow is a multi-agent Agentic AI framework designed to enable data pipelines to self-heal and close the loop between detection and remediation.
AegisFlow's full name is Agentic Engine for Intelligent Self-healing and Graph-driven Operations for Workload remediation.
AegisFlow is built on the MAPE-K loop, which consists of five components: Monitor, Analyze, Plan, Execute, and Knowledge.
AegisFlow adopts a non-invasive execution model called Parallel Shadow Patching, which generates and validates patches in a digital twin environment before deciding whether to deploy them to the production environment.
In the paper's experimental evaluation, AegisFlow improved MTTR by 98.1%, reducing the average repair time per patch from 170 minutes to 3.2 minutes.
AegisFlow achieved an overall patch success rate of 92% in experiments.
In scenario-specific experiments, AegisFlow achieved a 96% success rate for JSON schema changes, 98% for punctuation drift, and 85% for Shadow DOM scenarios (the weakest).
The paper conducted an experimental evaluation of AegisFlow across five common failure scenarios.
The paper claims that AegisFlow can free approximately 98% of data engineering on-call time from firefighting.
AegisFlow is deployment agnostic and can be integrated into existing pipeline orchestration systems as a plugin with minimal changes to existing systems.
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All Facts (11)
AegisFlow is a multi-agent Agentic AI framework designed to enable data pipelines to self-heal and close the loop between detection and remediation.
50%UnverifiedAegisFlow's full name is Agentic Engine for Intelligent Self-healing and Graph-driven Operations for Workload remediation.
50%UnverifiedAegisFlow is built on the MAPE-K loop, which consists of five components: Monitor, Analyze, Plan, Execute, and Knowledge.
50%UnverifiedAegisFlow adopts a non-invasive execution model called Parallel Shadow Patching, which generates and validates patches in a digital twin environment before deciding whether to deploy them to the production environment.
50%UnverifiedIn the paper's experimental evaluation, AegisFlow improved MTTR by 98.1%, reducing the average repair time per patch from 170 minutes to 3.2 minutes.
50%UnverifiedAegisFlow achieved an overall patch success rate of 92% in experiments.
50%UnverifiedIn scenario-specific experiments, AegisFlow achieved a 96% success rate for JSON schema changes, 98% for punctuation drift, and 85% for Shadow DOM scenarios (the weakest).
50%UnverifiedThe paper conducted an experimental evaluation of AegisFlow across five common failure scenarios.
50%UnverifiedThe paper claims that AegisFlow can free approximately 98% of data engineering on-call time from firefighting.
50%UnverifiedAegisFlow is deployment agnostic and can be integrated into existing pipeline orchestration systems as a plugin with minimal changes to existing systems.
50%UnverifiedThe experimental data in this paper comes from the authors' own experimental environment, and whether the 92% patch success rate can be reproduced in real complex production pipelines still requires practical validation.
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