A Y Combinator Startup Wants AI Agents to Debug Your Production Code—While It's Running
HyperProbe, a recent Y Combinator graduate, has launched a runtime debugger that lets AI coding agents place read-only breakpoints directly inside live production systems. The tool captures exact variable states and stack traces when real traffic hits a suspect line of code, confirming root causes without redeploying or adding log statements.
The premise is straightforward, if slightly audacious: instead of making engineers reason backward from incomplete logs when production breaks, give AI assistants the ability to inject new evidence-gathering points into running services on demand.
Engineering teams today lose hours—sometimes days—triangulating what went wrong from distributed traces and log files that never quite captured the right context. HyperProbe's founders say they built a different path. Their tool promises to deliver exact runtime snapshots in minutes, not the usual cycle of hypothesis, redeploy, wait, repeat.
Hooks in the Hot Path
The company's SDKs attach non-blocking hooks inside Node.js (via V8 Inspector), Python (using sys.monitoring in Python 3.12 and later), and Java (through JVM bytecode instrumentation), according to product documentation. When an AI coding agent receives an incident alert, it can place a "virtual breakpoint" at a specific source line through HyperProbe's Model Context Protocol server.
The next time production traffic executes that line, the SDK serializes local variables, the call stack, and optional watch expressions. Sensitive data gets redacted in-process. The snapshot flows to a dashboard and back to the agent.
"We are building a fast and safe way for coding agents to debug issues live in production," CEO and co-founder Shailendra Singh wrote in the Hacker News launch thread. Co-founder and CTO Karan Raina added that the system enforces read-only guarantees by using Node's throwOnSideEffect flag in the V8 inspector and restricting Python and Java conditional expressions to a safe subset of comparators that cannot trigger side effects.
It's the sort of technical claim that invites skepticism. Production systems are delicate. Injecting anything at runtime carries risk, even if the company promises zero writes.
Safety Rails
HyperProbe's architecture runs a real-time monitor with green, yellow, and red states that track CPU overhead, event-loop lag, and memory pressure, the company said. When Java heap free memory drops below 15 percent, the system enters a yellow state. Below 5 percent triggers red and automatically suspends probes.
The SDKs also enforce structural limits, capping object depth, array length, and string size. Automatic PII redaction targets keys including password, secret, token, authorization, cookie, ssn, and creditCard during serialization.
HyperProbe claims less than 1 percent CPU overhead at 3,000 requests per second, though this performance figure has not been independently verified. For serverless environments, Raina explained in the launch thread that the SDK uses a wrapper pattern to flush telemetry before each function return, adding 100 to 200 milliseconds in the worst case near function exits. Probes carry time and hit-count bounds to prevent runaway resource consumption.
Those guarantees matter. A tool that slows down production traffic or crashes a service would be worse than the bug it's trying to fix.
Pivot and Pedigree
Singh and Raina previously built production SDKs for HyperTest, an API testing platform that raised a $1.5 million seed round in 2022 from India-ecosystem investors. The founders announced in July that HyperTest had become HyperProbe, pivoting the company's mission to "enable coding agents to remotely debug running systems," Singh wrote on LinkedIn.
HyperProbe's Y Combinator profile lists a team size of eight. LinkedIn shows a range of 11-50 employees with 13 visible profiles as of late August, a discrepancy that may reflect contractors or recent hires. The founders declined to disclose funding raised beyond Y Combinator's standard investment.
Pricing Tiers
HyperProbe offers a free tier with unlimited probes and captures for one service on its managed cloud. The Professional plan costs $99 per service per month, billed annually at $79 per service with a three-service minimum, and includes 30-day capture history and shared workspaces.
Enterprise customers get custom annual contracts, self-hosting options, role-based access controls, and tailored PII redaction rules. The company released Python SDK version 1.2.24 on July 31, version 1.2.25 on August 14, and beta versions throughout late August, according to PyPI.
Self-hosting requires Ubuntu 22.04 or 24.04, four virtual CPUs, and 16 GB of RAM, running a Docker Compose stack that bundles the dashboard UI, telemetry broker, database, and reverse proxy.
Customer Voices, Lightly Verified
Two testimonials on the company's website describe faster incident resolution. A tech lead at CheQ Digital said HyperProbe caught a "silent data mismatch" on the first probe attempt, while a software engineer at Housing.com reported fixing a race condition "the same hour" using captured evidence.
The quotes are undated and presented as marketing claims. No dated or verified customer counts are publicly available.
A Crowded Field
HyperProbe competes in a crowded landscape of agent observability and debugging tools. LangSmith, maintained by LangChain, offers framework-agnostic agent tracing, evaluations, and a failure-clustering engine. Pricing starts at $0 per seat for a Developer plan and $39 per seat monthly for Plus.
Langfuse provides open-source, self-hostable traces, prompts, and experiments with a free tier of 50,000 observations per month. Datadog offers LLM Observability with pricing that varies by tier and usage volume, according to their current pricing documentation. Helicone and Arize Phoenix focus on usage-based or seat pricing for LLM gateway and tracing workflows.
HyperProbe differentiates itself by injecting new runtime evidence into production services rather than analyzing telemetry already emitted. "Every other tool reasons hard over data you already have," the company wrote on its product page. "HyperProbe captures exact evidence."
Traditional observability platforms like Datadog, New Relic, and Splunk offer APM and distributed tracing but do not provide agent-driven, on-demand variable inspection inside running processes.
Research signals from Microsoft, Google, and academic labs show rising interest in systematic debugging for AI agents. Microsoft Research published papers earlier this year on AgentRx and failure-anchored recovery frameworks for software-engineering agents. Google introduced Chrome DevTools for Agents recently, formalizing runtime hooks to let third-party tools give browser-debugging agents introspection capabilities. Brex detailed building an "AI oncall engineer" in a blog post several months ago, signaling enterprise investment in AI-assisted incident response.
Integration Points
HyperProbe's documentation includes quickstart guides for Node, Python, and Java that promise setup in under two minutes, as well as workspace-scoped MCP configurations for integrating the tool with Cursor, Claude Code, Windsurf, and similar AI coding assistants.
The company offers a VS Code extension for manually placing probes, though a public Marketplace listing was not found during recent searches. The documentation provides an install command as the authoritative source. Java developers can attach the agent using a -javaagent flag. Maven Central coordinates cite the co.hyperprobe group, though the artifact was not independently located in public indexes as of late August.
Whether the market needs yet another debugging tool remains an open question. But HyperProbe's bet is clear: as AI agents take on more operational work, they'll need runtime introspection that goes deeper than logs and metrics can provide. Singh said the company's mission is to let coding agents "remotely debug running systems." The founders appear confident that production environments, for all their fragility, are ready for that kind of access.
