AegisFlow delivers 98.1 percent MTTR reduction via Parallel Shadow Patching on LLM-driven repairs
AegisFlow closes the remediation loop for fragile data pipelines using agentic LLM patches verified in shadow environments. Reported metrics demonstrate order-of-magnitude MTTR gains and high success on schema and formatting failures. The plugin model enables adoption without re-architecture of existing orchestration layers.
AegisFlow implements a Watchdog agent for telemetry collection and a Repair agent that generates, tests, and deploys LLM-based patches. The system runs under Parallel Shadow Patching inside a MAPE-K loop, executing candidate fixes in isolated digital twins before any production change. Deployment occurs as a plugin into existing orchestrators with no pipeline modification required.
Benchmarks cover JSON schema drift at 96 percent success, punctuation drift at 98 percent, and Shadow DOM mutations at 85 percent. Aggregate results show 92 percent overall patch acceptance and 98 percent reduction in engineer on-call time. These figures derive from controlled experiments on five standard failure classes without reference to external production telemetry.
The architecture directly targets brittle upstream contracts that observability tools only surface. By closing the detection-to-remediation loop inside the same runtime, AegisFlow shifts data engineering effort from incident response to pipeline evolution. Future deployments will require measurement of false-positive patch rates once the framework leaves the reported lab setting.
RepairAgent: 75 percent of evaluated pipelines reach autonomous patch acceptance above 90 percent within 90 days of plugin deployment
Sources (2)
- [1]Primary Source(https://arxiv.org/abs/2610.06971)
- [2]Supporting Source(https://arxiv.org/abs/2308.00352)