Agentic Self-Healing for Data and AI Pipelines: An Affordable Vendor-Agnostic Architecture using Open-Source Software
2026-08-03 • Emerging Technologies
Emerging TechnologiesArtificial IntelligenceDatabases
AI summaryⓘ
The authors explain that many organizations use complex data and machine learning pipelines that often break due to various issues like bad data or system failures. They review existing tools that help find and fix these problems but find them costly or hard to use for smaller teams. They argue that the main problem is how these tools are spread out and not well connected. To solve this, the authors propose a new, affordable system that combines different monitoring and fixing tools in a simple, adaptable way for many types of tech teams.
data pipelinemachine learning operationsZeroOpsobservabilityroot-cause analysisincident managementautomated remediationagentic systemsopen-source tools
Authors
Solomon Eshun, Dennis Murage, Sharleen Muoki, Chih-Chun Chen, Stephen Adjignon, Matteo Staar, Oliver Angélil
Abstract
Modern organizations rely on data, machine learning, and software delivery pipelines to move data, train models, deploy applications, refresh dashboards, and support business-critical decisions. However, these pipelines often fail because of data quality issues, schema changes, upstream source changes, infrastructure problems, orchestration failures, and model workflow issues. Existing ZeroOps, observability, and AI operations platforms can help teams detect incidents, investigate root causes, and in some cases recommend or execute fixes. However, many of these solutions are expensive, vendor-specific, or difficult for smaller teams to adapt across different tools and environments. This paper first compares existing off-the-shelf solutions for AI-assisted pipeline monitoring, root-cause analysis, and automated remediation, including their strengths, limitations, and practical trade-offs. Based on this comparison, we find that the main gap is architectural rather than technological: the required ingredients for self-healing pipelines already exist, but they are fragmented across vendor-specific platforms, observability tools, incident systems, and open-source components. We therefore propose an affordable, vendor-agnostic reference architecture for agentic self-healing pipelines using open-source and low-cost tools. The proposed architecture combines monitoring, pipeline metadata, incident history, deterministic policy checks, AI-assisted diagnosis, approval workflows, and controlled remediation actions to help teams detect, diagnose, repair, verify, and learn from pipeline issues with less manual effort. The goal is to provide a practical reference architecture that can be adapted across data engineering, machine learning operations, and software delivery environments.