--- title: Launch HN: HyperProbe (YC S26) – Agents that do... description: HyperProbe is an AI-powered on-call agent that automates production incident investigation. It places read-only, non-blocking virtual breakpoints on live... canonical: https://daily.dev/posts/launch-hn-hyperprobe-yc-s26-agents-that-do-read-only-debugging-in-prod-avhggl4mp twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Launch HN: HyperProbe (YC S26) – Agents that do read-only debugging in prod | daily.dev og:description: HyperProbe is an AI-powered on-call agent that automates production incident investigation. It places read-only, non-blocking virtual breakpoints on live... og:url: https://daily.dev/posts/launch-hn-hyperprobe-yc-s26-agents-that-do-read-only-debugging-in-prod-avhggl4mp og:image: https://api.daily.dev/og/posts/AVHggL4mP.png og:image:alt: Launch HN: HyperProbe (YC S26) – Agents that do read-only debugging in prod og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Launch HN: HyperProbe (YC S26) – Agents that do read-only debugging in prod **[Hacker News](https://daily.dev/sources/hn)** · 5 min read · 0 upvotes · 0 comments ## Summary HyperProbe is an AI-powered on-call agent that automates production incident investigation. It places read-only, non-blocking virtual breakpoints on live running services to capture exact variable state at the moment of failure — without redeployment or service restarts. The tool targets hard-to-diagnose issues like silent failures, race conditions, and unhandled vendor status codes that never appear in logs or traces. It supports Node.js, TypeScript, Java, and Kotlin, runs in self-hosted or private VPC environments, and claims less than 1% overhead at 3,000 RPS. Pricing is unlimited on probes and captures with no seat or host counts. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Community take How the wider developer community reacted, aggregated from 1 discussion and 88 comments across hackernews (as of 2026-08-06). **TL;DR:** Developers find the core concept of read-only, no-redeploy production debugging genuinely useful, especially for silent failures and race conditions, but raise pointed questions about overhead guarantees, data privacy/redaction auditability, differentiation from existing tools like Rollbar, and the risk of confident-but-wrong AI diagnoses. **Sentiment:** 55% positive · 35% mixed · 10% skeptical **The case for** - Capturing full variable state across the entire call chain (not just the current frame) fills a real gap that logs and traces leave open. - Read-only, structurally enforced probes mean the running service is never mutated, keeping the tool safe to use in production. - Probe bounds (hit count + expiry time) and a layered guardrail system limit blast radius when a probe lands on a hot path. - Hypothesis-driven conditional probing lets teams gather precise data on demand rather than storing petabytes of static trace data. - The MCP server integration lets coding agents consume runtime evidence directly, making AI-assisted debugging more grounded than log-only reasoning. **The pushback** - Existing mature tools (Rollbar, AppSignal, Embrace) already capture call-stack variables for caught exceptions, so the differentiation story needs sharpening. - An autonomous agent producing a confident but wrong diagnosis — pointing at the wrong subsystem — can be worse than no answer at all. - Redaction audit trails are not yet available, making it hard to verify which sensitive values were captured and which rule matched. - Serverless environments can add 100–200 ms latency per invocation when a probe is active near a function return. - The landing page design (described as generic AI-tool aesthetic) risks losing engineers before they read the technical pitch. - Conditional probe expressions have significant restrictions (no method calls in Python/Java) that require workarounds like placing multiple probes. **By community** - hackernews (mixed): Technically engaged commenters see real value in the runtime-state capture approach but push hard on differentiation from existing APM tools, redaction auditability, serverless overhead, and the danger of confident wrong AI diagnoses. **Hottest debate:** Whether HyperProbe meaningfully differentiates from mature error-tracking tools like Rollbar and AppSignal that already capture call-stack variables, or whether it only adds value for the narrower class of silent, exception-free logic bugs. **Open questions** - Will there be an open-source or fully on-premises version for teams that cannot send any runtime data off-site? - How will the planned custom DSL and AST-based policy layer handle complex conditional expressions safely across all supported runtimes? - Can the tool return explicit uncertainty (competing hypotheses + cheapest discriminating check) rather than a single confident diagnosis when evidence is ambiguous? - When will a full audit trail — showing exactly what each probe captured and which redaction rule fired — be available? - Are there plans to add Kubernetes/sidecar connectors so infrastructure-level causes (OOM kills, swap exhaustion) are visible alongside in-process state? **Highlights** > Read-only in prod is the right constraint. The failure mode I'd most want to hear how you handle isn't a missing signal — it's a confident wrong diagnosis. Running an autonomous pipeline for eight months, the three incidents that cost me the most days all had the surface error naming the wrong subsystem: - "x264: malloc of size N failed / incorrect parameters" — I read it as a codec or bad-args bug and went looking there. It was RAM exhaustion. The encoder was the victim, not the cause. - A 22x slowdown in an LLM step that was indistinguishable from a hang. It was swap: the model no longer fit in RAM, and the page file did the rest. - A 27-minute "freeze" in a background job. The process was healthy; the pipe was buffering, so nothing appeared until exit. In all three the logs were complete and the metrics were green. The mistake was in the inference drawn from them — and an agent will produce that wrong inference far faster than I did, with better prose attached to it. So: does HyperProbe ever return "I don't know — here are two competing hypotheses and the cheapest check that separates them"? The discriminating check is the part I'd pay for. A single confident answer that's wrong is worse than no answer, because it sends a human down a road with the agent's credibility behind it. > — [IgorVoytyuk on hackernews · 1 comments](https://news.ycombinator.com/item?id=49192345) > How is HyperProbe different from existing tools like AppSignal, Rollbar, and Embrace? Such very mature tools exist that auto-instrument, collect variables from the call stack, and pinpoint error causes. > Every log-and-trace tool hands the agent data that already exists and asks it to reason backward to what probably happened If the app is using a decent instrumentation tool, the data shows what 'actually' happened, not what 'probably' happened. > "checkout returns 200 but some users are seeing their order fail, find out why." Does this tool only exist to shore up poor system design? Failing orders at any e-commerce business I've worked with, large and small, are a huge red flag. Typically that is one of the first actions that is logged and traced (alongside onboarding/login), and the metrics are actively monitored. Returning 200 for failure and not catching that error is very bad API design. Similarly, putting engineers in a situation where debugging requires accessing unknown amounts of live sensitive customer data is generally considered bad practice (even if it happens often IRL) -- in a hurry to debug, it's easy to miss that a property should have been redacted; by then it's too late and sensitive data is exposed. Plus, in most systems with significant usage the volume of trace data is prohibitive to individually examine and search through. That's why Rollbar etc aggregate errors and captured data to identify patterns before a human (or agent, or tool) ever takes a look at it. A single captured instance can also be very misleading as to the true cause. How are you addressing these common concerns? > — [doublerebel on hackernews · 1 comments](https://news.ycombinator.com/item?id=49191089) > > How is HyperProbe different from existing tools like AppSignal, Rollbar, and Embrace? These work only on either uncaught exceptions or wrapping up caught exceptions with their sdk. These tools will not help you with silent failures, like logic bugs where code executes cleanly without throwing, but produces the wrong business state. If every problem in your app ends up as an exception, sure you'll be able to catch the symptoms of where the exception got thrown. we can deal with these too, but these tools cant deal with the messy bugs where no exception fires. > Such very mature tools exist that auto-instrument, collect variables from the call stack, and pinpoint error causes. That is true for python using frame.f_locals (we use this as well) nodejs only gives it only till the lasy async boundary, after that v8 itself drops this data. java only gives you the current frame, to get variables beyond that you would needs JDI/JVMTI which would block your threads, usually unnacceptable in production To get around this safely, we add multiple probes all across the call chain and collate collected data using the traceId from the context (or thread id as a fallback); > Does this tool only exist to shore up poor system design? Returning 200 OK on a silent failure is 100% bad system design, I completely agree. But real-world production systems are full of legacy edge cases. (if that weren't true, L1/L2/L3 support team shenanigans wouldn't exist) Also, the exception will tell you that an exception occured in order service in GET /orders/{id}/payment, your trace will tell you payment service is giving 404 for that order ID what it wont tell you it happened becuase the webhook endpoint that your payment gateway calls is now receiving a new payment state called 'PENDING' and that you dont handle but still mark the payment as 'processed' for idempotency check. and now your order service is calling the payment service and its giving 404 because it never got written Bad design. 100% Agree, but has happened IRL. > putting engineers in a situation where debugging requires accessing unknown amounts of live sensitive customer data is generally considered bad practice (even if it happens often IRL) I think tells that teams would go to these extents to fix issues. Not ideal. I agree. > in a hurry to debug, it's easy to miss that a property should have been redacted; by then it's too late and sensitive data is exposed fair critique. we currently use in-process rule engines to filter known sensitive patterns, and users can add on to it. but we are also building out-of-process secondary checks (using NER/classifiers) to sanitize payloads before storage. It requires strict rules, but getting verified runtime evidence is far safer and faster than blindly guessing and shipping trial-and-error hotfixes to production. or waiting to be too sure.. a luxury that might not be possible everytime. > Rollbar etc aggregate errors and captured data to identify patterns before a human (or agent, or tool) ever takes a look at it. There is merit in that as well, if you are looking at so many logs/traces, you kinda have to do it. We have a different approach, we use hypothesis driven conditional probing instead. probes are dropped dynamically as the understanding of the bug evolves in a session exmaple: console.log('hello'); const x = await getThisValueSomehow(); if (condition A) { console.log('i m in condition A'); // do something; } else if (condtion B) { console.log('i m in condition B'); // do something; } You can also place a probe before the branch to capture variable state when neither condition evaluates to true. You gather precise data on demand rather than paying to store petabytes of static trace data. > A single captured instance can also be very misleading as to the true cause. We collect multiple snapshots per probe run. However, because we capture full variable state at the exact execution line, a single snapshot frequently reveals the root cause for tha > — [karanraina on hackernews](https://news.ycombinator.com/item?id=49191792) > we have a lot of guardrails (https://docs.hyperprobe.co/how-it-works#built-in-safety-guar...) if any guardrails fails, we suspend probes till cooldown. also 1. every probe is bounded by hits/expiry time (whichever comes earlier) 2. hit budgeting happens with a token bucket at a global level, per probe was an overkill (numbers are configurable) 3. we even measure the execution time that probes have when active and suspend if that that takes longer than threshold (again configurable) 4. we even have budgets for the network bandwidth it would take (approximated by the size of payloads) 5. collection itself is bounded by max no of total snapshots we can keep in memory. 6. every snapshot has a size limit as well, every variable has a size limit as well. 7. depth of objects, no of objects, size of lists is capped by default. latency delta varies by platform under load but is mostly negligible nodejs: ~7-10ms python: ~4-9ms java: 1-2ms the main reason for this is guardrails suspending probes, having loosened guardrails will increase this under load regarding localization of failures.. absolutely we even report the error in the probe snapshot (confirmed by adding side effects in an expression and commenting out the guardrails during testing) huge payload size doesnt matter.. we limit the objects depth, list length, remove duplicate refs from data etc.. even string length is truncated., but even if it happens, your request would still survive. also, even if the collector dies or there's a network failure, your service remains unaffected, we just are unable to collect telemetry we are boring under extreme conditions :) > — [karanraina on hackernews](https://news.ycombinator.com/item?id=49192224) > There are two pieces. An SDK that runs inside your service, and an MCP server your coding agent talks to. The SDK is what makes setting probes (virtual breakpoints, log or metric) possible without a redeploy. In Node and Python it hooks in-process. In Java it attaches as a JVM agent, instrumenting at the bytecode level. Either way the service keeps running and serving traffic. Nothing pauses. Consider putting this near the beginning rather than 2/3 of the way down your pitch. I nearly stopped reading because these dramatic 1-2 sentence paragraphs are unpleasantly like listening to TV commercials. I think your target audience should not be CTOs or their direct reports, but engineers themselves, and I think you need a more focused pitch that takes less time to get to the point. Anyway, an MCP-managed passive debugger seems like a useful tool. Best of luck with it. > — [anigbrowl on hackernews · 1 comments](https://news.ycombinator.com/item?id=49188339) **Source threads** - [hackernews](https://news.ycombinator.com/item?id=49185389) · 63 points · 88 comments ## Similar posts on daily.dev - [HyperProbe — Your 24/7 AI On-Call Agent](https://daily.dev/posts/hyperprobe-your-24-7-ai-on-call-agent-kyhajmah5) · Hacker News · 0 upvotes · 0 comments - [Hoplite](https://daily.dev/posts/hoplite-8krnqu4mi) · Hacker News · 0 upvotes · 0 comments - [Automate SageMaker HyperPod incident triage and root-cause-analysis with AWS DevOps Agent](https://daily.dev/posts/automate-sagemaker-hyperpod-incident-triage-and-root-cause-analysis-with-aws-devops-agent-8q1vhxqcn) · AWS · 0 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#observability](https://daily.dev/tags/observability) [View this post on daily.dev](https://daily.dev/posts/launch-hn-hyperprobe-yc-s26-agents-that-do-read-only-debugging-in-prod-avhggl4mp) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Launch HN: HyperProbe (YC S26) – Agents that do read-only debugging in prod","url":"https://daily.dev/posts/launch-hn-hyperprobe-yc-s26-agents-that-do-read-only-debugging-in-prod-avhggl4mp","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/launch-hn-hyperprobe-yc-s26-agents-that-do-read-only-debugging-in-prod-avhggl4mp"},"datePublished":"2026-08-06T14:18:15.307Z","dateModified":"2026-08-06T14:18:56.055Z","description":"HyperProbe is an AI-powered on-call agent that automates production incident investigation. 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