HyperProbe

HyperProbe positions its AI SRE as an investigation layer that diagnoses specific failures after alerts, rather than as an application performance monitoring system. While APM collects predefined telemetry and shows what happened across a system, HyperProbe takes alerts from PagerDuty, Datadog, or Slack and examines existing logs and traces. When those sources lack evidence, it places a read-only probe on the failing line to capture variable state, then delivers a root cause without a redeploy or restart.

Sep 2026HyperProbe BlogNews article

HyperProbe describes an AI SRE as software that investigates production incidents by gathering operational evidence, testing likely causes, and presenting root-cause explanations for human review. It starts with an alert, identifies involved services, and brings related logs, metrics, and traces into one view. HyperProbe says its AI on-call agent can also capture live variable values, branch choices, or returned payloads that existing logs may not contain.

Sep 2026HyperProbe BlogNews article

HyperProbe publicly debuts a read-only debugging agent for AI-assisted troubleshooting in production environments. Released as a YC S26 project, the product initially offers a Java integration, with an official Java SDK Agent available under the co.hyperprobe namespace on Maven Central. The article notes that permissions, data security, and runtime overhead still require validation.

Aug 2026openai-hub.comNews article

HyperProbe builds an AI-powered on-call agent that investigates production incidents using read-only virtual breakpoints on live services. It captures variable state at failure without redeployments or restarts, targeting issues such as silent failures, race conditions, and unhandled vendor status codes missing from logs or traces. The tool supports Node.js, TypeScript, Java, and Kotlin, and runs self-hosted or in private VPC environments; HyperProbe claims less than 1% overhead at 3,000 RPS.

Aug 2026daily.devNews article