YC-backed HyperProbe launches AI-native production debugging tool
The startup enables engineers and AI agents to capture live memory and variable states without service restarts.
HyperProbe, a Y Combinator S26 startup, has launched a production debugging tool that allows engineers and AI agents to insert read-only, non-blocking probes into live services. The platform eliminates the need for redeployments or service restarts to capture the exact state of a running application.
Founded in 2026 by Shailendra Singh and Karan Raina, HyperProbe enables the capture of live variable states and call stacks across Node.js, TypeScript, Java, and Python environments. According to company specifications, the tool is designed to be non-blocking and read-only, maintaining less than 1% overhead at 3,000 requests per second (RPS). A key technical differentiator is the tool's integration with the Model Context Protocol (MCP), which allows AI assistants such as Cursor, Windsurf, and Claude Desktop to autonomously interact with the debugging engine to diagnose production incidents.
The End of the Log-and-Deploy Loop
Traditional production debugging typically requires a repetitive cycle: engineers must add logging statements to the code, commit those changes, wait for CI/CD pipelines to complete, and redeploy the service. This process is often too slow to capture transient bugs and can introduce further instability into a production environment. HyperProbe replaces this workflow by allowing users to drop "virtual breakpoints" into running services, providing immediate visibility into live memory without pausing execution.
Shifting the On-Call Experience
By bridging the gap between local IDEs and live production state, HyperProbe shifts the role of AI in the development lifecycle. Rather than simply suggesting code changes based on static files, AI agents can now use real-time evidence from production to perform root cause analysis. This capability potentially removes senior engineers from the initial stages of incident investigation, as the AI can autonomously gather the necessary state data to identify a bug.
Future Outlook
As the tool integrates deeper into AI-native workflows, the industry will be watching how autonomously these agents can move from diagnosis to resolution. The ability to catch silent data mismatches that are difficult to reproduce locally remains a core value proposition. Ultimately, the broader impact on mean time to recovery (MTTR) across diverse enterprise architectures will be the primary metric for the tool's long-term adoption.