Enterprise Network Reliability Agent Evaluation

Problem / Motivation

Azure’s growing family of SRE, observability, and troubleshooting agents promises to shorten the path from “something is wrong” to “here is the likely cause,” but that promise is hard to assess without a controlled way to compare an agent’s diagnosis against a known-good answer. This project is a research evaluation: injecting controlled, reproducible network incidents into a lab environment and comparing what each agent surfaces against the actual, known root cause.

Approach

The evaluation plan defines a small set of reproducible network incident scenarios (for example: a misconfigured route, an overly restrictive NSG rule, a saturated link) against a lab network with Azure Monitor and Log Analytics telemetry enabled. Each scenario is run against the relevant agent, and the agent’s diagnosis, evidence, and suggested remediation are recorded and compared against the scenario’s known, injected root cause.

Technologies

• Azure Monitor
• Log Analytics / KQL
• Azure AI-based operational agents
• A controlled lab network for incident injection

Current status

This is a research project at the evaluation-design stage. Scenario design is underway; no agent evaluation runs have been completed yet, and no findings, scores, or comparative results exist to report. The status label above reflects that honestly.

Next steps

Finalize the incident scenario set, run each scenario against the available agents, and publish the comparison methodology and results once a meaningful number of scenarios have been evaluated.