HyperProbe gives coding agents read-only probes for live production debugging

Why it matters

Coding agents can write code faster than engineers can instrument it. HyperProbe is betting the next developer-tool layer will capture live runtime evidence, with security controls deciding adoption.

Shailendra Singh and Karan Raina founded HyperProbe, a production-debugging tool that lets coding agents place read-only probes inside running services and inspect the variable state around a failure. The YC Summer 2026 company profile lists HyperProbe as an eight-person San Francisco company. The founders are betting that AI-written code creates a new observability problem: software is shipping faster, while the logs needed to explain its failures are still chosen in advance by humans.

HyperProbe enters through the tools engineers already use. An alert can arrive from PagerDuty, Datadog or Slack; a coding agent examines the available logs and traces, selects a suspected line, and asks HyperProbe to capture the variables and call stack when live traffic reaches it. HyperProbe says the probe does not pause the request thread, modify memory or require a new deployment.

The product is the second act for Singh and Raina, whose previous company, HyperTest, automated API testing. In a LinkedIn post tied to HyperProbe's acceptance into Y Combinator's Summer 2026 batch, Singh wrote that HyperTest had become HyperProbe. Y Combinator lists HyperProbe as an eight-person San Francisco company founded in 2026.

A second company built from the first

Singh studied at IIT Bombay and previously worked in product and operating roles at OYO. Raina studied at Guru Gobind Singh Indraprastha University, later completed a computer science master's degree at Georgia Tech, and worked on LimeTray's engineering team. The pair previously worked on logistics startup Transporter.city, which failed to scale, according to a 2021 profile of HyperTest.

That history helps explain HyperProbe's narrow focus. HyperTest tried to catch failures before release by generating tests from application traffic. HyperProbe moves the founders closer to the incident itself, after faulty code is already running and engineers need evidence that their existing instrumentation did not collect.

Singh's thesis is that coding agents weaken the connection between the person operating a service and the code inside it. Engineers traditionally add logs based on an informal understanding of where their own code may fail. An agent can produce a large change without leaving that mental model behind. When the failure arrives, both the on-call engineer and the coding agent are working from incomplete telemetry.

HyperProbe gives the coding agent another way to ask the running process what happened. That is a more defensible role for AI than asking a model to keep rereading the same logs and proposing increasingly confident guesses.

How the probe works

HyperProbe requires a one-time SDK or runtime-agent deployment. Its quickstart documentation directs Node.js users to install the @hyperprobe/node-sdk package, while Java users attach an agent with the JVM's -javaagent flag. A .hprc file maps the local repository to a HyperProbe service, and a production commit SHA aligns the source in an engineer's editor with the exact build running in production. HyperProbe requires that commit identifier to align the deployed code with the local source.

Once installed, HyperProbe's VS Code extension registers a probe against a selected line. HyperProbe says its Node.js agent uses the V8 inspector protocol, while its Java agent uses runtime instrumentation. The captured data is queued and returned asynchronously rather than blocking the application thread. HyperProbe says the product works with coding agents including Cursor, Claude Code, Codex and Opencode. (How HyperProbe works)

The product depends on the failing path executing again. HyperProbe's documentation instructs users to place a probe and then trigger the relevant endpoint or wait for another matching event. A one-off failure that does not recur leaves no live execution for the probe to capture. That constraint matters most during intermittent incidents, where engineers may know that something broke without knowing how or when they can reproduce it.

HyperProbe's headline performance figures remain HyperProbe claims. Its site compares a three-to-four-hour manual investigation with a workflow that takes less than 10 minutes and advertises less than 1% overhead. The worked incident involving 847 failed requests is a HyperProbe-produced product scenario rather than independently documented customer data. HyperProbe does publish more concrete safety limits in its security documentation, including capture-rate, bandwidth, event-loop-lag and object-size controls. The agent is designed to suspend probes when configured overhead thresholds are crossed. (HyperProbe homepage)

The competition already knows the problem

Live production debugging predates the current wave of coding agents. Lightrun offers dynamic runtime instrumentation and markets agent-driven investigations that capture variables, branch decisions and call stacks from running services. Dynatrace acquired Rookout in 2023 to add production code debugging to its observability platform.

HyperProbe also arrives as observability vendors push their own AI responders. Datadog's Bits Investigation reasons across telemetry and runbooks, while PagerDuty's SRE Agent is designed to triage and diagnose incidents from the escalation workflow. Datadog and PagerDuty can serve as HyperProbe integrations while competing for control of the investigation.

HyperProbe's opening is at the code line where conventional telemetry runs out. That position gives Singh and Raina a focused product boundary, though Lightrun's expanding agent features show that it will be contested. HyperProbe still has to earn access to the most sensitive part of a customer's stack: a running production process and the values held in its memory.

The founders have built their pitch around that trust problem. HyperProbe says probes are read-only, PII is redacted by the agent, and payloads can be constrained before transmission. Those controls will carry more weight with buyers than the AI label. If Singh and Raina can make runtime probing routine and governable, HyperProbe could give coding agents the missing evidence they need to own a larger share of production support.