Best AI SRE Agents for Ruby

Around 25 million engineers work across dozens of distinct functions. Engineers are using New Relic as every company is becoming a software company to gather real-time insight and trending data on the performance of their software. This allows them to be more resilient and provide exceptional customer experiences. New Relic is the only platform that offers an all-in one solution. New Relic offers customers a secure cloud for all metrics and events, powerful full-stack analytics tools, and simple, transparent pricing based on usage. New Relic also has curated the largest open source ecosystem in the industry, making it simple for engineers to get started using observability.

Dash0 is an OpenTelemetry-native observability platform for developers and SRE teams. Metrics, logs, traces, and resources sit in one place, linked by OpenTelemetry semantic conventions, so you move from a slow trace to the logs around it without switching tools or rebuilding context by hand. Telemetry arrives over OTLP. There is no proprietary agent to install and nothing to re-instrument: send the OpenTelemetry data you already collect, and take it elsewhere unchanged if you ever want to. Dash0 ingests Prometheus metrics alongside OpenTelemetry, supports PromQL, and imports existing Prometheus alerting rules and Grafana dashboards. A Kubernetes operator handles collection across clusters, covering workloads, nodes, and control plane. Dashboards are built on Perses and defined as code, so they live in Git and ship through the same review process as the rest of your infrastructure. Checks and alerts are configured the same way. Heatmap drilldowns and filtering on high-cardinality attributes narrow a broad symptom down to the specific requests behind it. AI works on the data rather than in a chat window. Log AI infers severity for logs that arrive without it, extracts patterns, and groups related records, which makes unstructured output from third-party services searchable and filterable. Trace triage uses the SIFT framework to narrow a failing request toward a likely cause. Spend is visible in the product. You can see which services, attributes, and log volumes drive cost and cut them at the source, rather than reconciling a bill after the fact.