Agentic AI-Enhanced Network Performance Monitoring and Diagnostic Analysis for Site Reliability Engineering
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Abstract
Network performance monitoring and diagnostic analysis (NPMD) is becoming a core reliability discipline for modern distributed systems because cloud applications, hybrid connectivity, software-defined networking, and multi-region dependency chains can turn small network degradations into visible service incidents. The original paper explained NPMD through Site Reliability Engineering (SRE) concepts such as service level indicators (SLIs), service level objectives (SLOs), and non-functional requirements. This updated version expands the work with a data-driven operating model, stronger references, explicit table and figure captions, and an Agentic AI solution pattern for bounded autonomous diagnosis and remediation. The proposed approach combines telemetry pipelines, SLO evaluation, topology and change evidence, retrieval-augmented diagnostic reasoning, runbook-constrained tool execution, and human approval controls. The paper treats AI as an operational assistant rather than an uncontrolled replacement for SRE judgment: the agent can summarize evidence, correlate probable causes, recommend remediation, and execute only low-risk approved actions while preserving auditability. The result is a practical framework for reducing alert noise, improving time to detect, accelerating incident triage, and strengthening post-incident learning without relying on unsupported claims or unverifiable performance numbers.
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References
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