Project Arena is a vendor-neutral benchmark and starter kit for evaluating AI-driven site reliability investigation tools on Kubernetes. It automates a repeatable flow: deploy a disposable cluster (local kind or AWS EKS), install an observability product, inject and verify faults across a selectable suite (21 full scenarios or a 6-scenario smoke suite), capture investigation records, and score final reports with a configurable AI judge. The toolset requires Python 3.10+, kubectl, and Docker/kind for local use; EKS paths add Terraform and the AWS CLI. Deployment supports direct kubectl mutations or a GitOps path via Argo CD, and the suite includes network-policy-aware configurations, multi-architecture image builds, and commands for building, pushing, and running scenarios.
A published benchmark compared Edge Delta’s native AI, Grafana’s native AI, and Anthropic Claude driven through each platform across 21 incidents, with grading by GPT-6-Astra against a rubric. Edge Delta detected/investigated 18/21 (85.7%) and Grafana 12/21 (57.1%). Claude-led investigations scored higher on supported mitigation and implementation readiness (16/21, 76.2%) than the native products (Edge Delta 44.4% supported mitigation; 56.2% readiness). On a controlled 12-incident subset, Edge Delta led root-cause correctness (11/12, 91.7%), Grafana trailed (9/12, 75%), and Claude was 10/12 (83.3%); blast-radius and mitigation metrics showed smaller differences. Scoring categories are detection, root cause, blast radius, supported final mitigation, and implementation readiness; full results, CSV downloads, rubric, and methodology are provided.
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