HyperProbe
Infrastructure · tested 2026-08-25 · re-test due 2026-11-23 · by the Hlido desk, not the vendor
In short: A YC-backed AI on-call agent with a genuinely differentiated mechanism — it has your coding agent drop a read-only probe on the exact prod line that failed, capturing evidence your logs never had, without a redeploy.
5 PASS · 0 FAIL of 5 public-surface claims
Quick answer
HyperProbe scores 71/100 (STEADY) on Hlido’s independent, hands-on test (reviewed 2026-08-25). STEADY (71), just into the band, because HyperProbe has a genuinely differentiated mechanism (capturing new, previously-unlogged production evidence via read-only probes without redeploy), YC backing, multi-agent and mul Pricing: Paid.
HyperProbe positions itself as the AI that works an incident so your engineers do not have to: alert to confirmed root cause before someone opens their laptop. Most incident-AI tools reason harder over the telemetry you already collected; HyperProbe's differentiator is that it captures new evidence. It has your coding agents place a read-only probe on the exact line where a problem occurred in production, capturing values that were never logged, without redeploying or restarting the service. That is a real mechanism, not a repackaging of log search, and it targets the true bottleneck the page names correctly — 'the fix takes 10 minutes, finding it takes hours, because the value that explains failure is never logged.' It is Y-Combinator-backed, works with Cursor, Claude Code, Codex and Opencode across Node.js, TypeScript, Java and Python, and has a pricing page and demo booking, so the commercial surface is more complete than most of this batch. What holds it to the low-STEADY band is that the entire value rests on strong, quantified outcome claims — 3–4 hours to under 10 minutes for time-to-root-cause, redeployments per incident from 3–4 to zero — that are the vendor's own and unverifiable from the public surface, and the read-only-probe-in-production mechanism, while powerful, is exactly the kind of capability a security-conscious buyer will want to interrogate hard (what it can see, how access is scoped, what audit trail exists). Compelling idea, credible backing, claims that need a buyer's own proof-of-concept before they are trusted.
Why STEADY
STEADY (71), just into the band, because HyperProbe has a genuinely differentiated mechanism (capturing new, previously-unlogged production evidence via read-only probes without redeploy), YC backing, multi-agent and multi-language support, and a complete commercial surface. It sits at the band floor because its core value rests on strong quantified outcome claims that are vendor-stated and unverifiable publicly, and a probe-in-production capability whose security scoping a buyer must interrogate before trusting.
Public-surface checklist
- PASS Homepage loads (required)
- PASS Primary value prop (required) — Your 24/7 AI on-call agent — alert to confirmed root cause before engineers open their laptop
- PASS Cta present (required) — Try Now / Book a Demo
- PASS Pricing or access — Pricing page present in navigation; Try Now and Book a Demo access paths
- PASS Evidence or demo — Concrete mechanism described (in-prod read-only probes); demo booking; docs linked. Outcome metrics are vendor-stated, not demonstrated
What we saw
1 screenshot captured by the Hlido engine during the reviewed run (run-63518942b42ac822-hyperprobe-co). Our own captures — not vendor marketing material.
What it does well
- Differentiated mechanism: captures new evidence by probing the exact failing prod line, rather than only reasoning over existing logs
- Names the real bottleneck accurately — finding the cause, not applying the fix
- Read-only probes without redeployment or service restart — low operational disruption in principle
- Y-Combinator-backed, signalling vetting and some continuity
- Works with Cursor, Claude Code, Codex and Opencode across Node.js, TypeScript, Java and Python
- Complete commercial surface: pricing page and demo booking present
What it fails at
- Core value rests on strong quantified claims (3–4h → <10min, redeploys 3–4 → 0) that are vendor-stated and unverifiable from the surface
- A read-only probe running in production is a capability a security team must scrutinise — scoping and audit trail are not detailed on the surface
- Young company; limited independent evidence or named customer references on the reviewed page
- Language/runtime support is bounded (Node/TS/Java/Python) — outside that, no coverage
- The 'engineers didn't join to be on call' framing is persuasive but is marketing, not evidence
Red flags
- Headline outcome metrics (time-to-root-cause 3–4h → <10min; redeployments 3–4 → 0) are vendor-stated and not independently verifiable from the public surface
- A read-only agent probe running inside production needs explicit access-scoping and audit detail that the surface does not provide
Best for
- Engineering teams drowning in on-call toil who want faster, evidence-backed root-cause on production incidents
- Shops already using Cursor, Claude Code, Codex or Opencode in Node/TS/Java/Python stacks
- Teams willing to run a scoped proof-of-concept to validate the time-to-root-cause claims themselves
Not recommended for
- Security-constrained environments that cannot allow a third-party probe to run in production without deep review
- Buyers who need independent, non-vendor evidence for the headline outcome numbers before adopting
- Stacks outside the supported languages and runtimes
Pricing & access
- ModelPaid
- Pricing findable on the public surfacePASS Pricing page present in navigation; Try Now and Book a Demo access paths (tested 2026-08-25)
Derived from Hlido-held evidence only (engine checklist + editorial text); quotes are verbatim from the scorecard; not vendor-supplied; re-derived daily. Verify current prices on the vendor's pricing page. Last verified 2026-08-25.
Compared to
-
devops-incident-response
new-evidence-capture
General incident-response agents coordinate and reason over existing signals; HyperProbe's distinct move is capturing new, previously-unlogged evidence at the failure point. Conventional tools for orchestration over what you already have, HyperProbe for gathering what you never logged.
-
incidentops-openenv
root-cause-specialisation
IncidentOps-style environments focus on the incident workflow and tooling harness; HyperProbe focuses narrowly on root-cause evidence capture via in-prod probes. The former for workflow breadth, HyperProbe for the specific find-the-cause bottleneck.
Agent relevance
No programmatic surfaces
Agentic-Commerce Readiness 30/100 · SURFACE-ONLY
Independent readiness for agent delegation & transaction. How it’s scored · check live
HyperProbe operates through existing coding agents (Cursor, Claude Code, Codex, Opencode) — it directs them to place read-only probes at the failure point. The public surface documents which agents it works with but does not surface a public API, MCP server, CLI or SDK for arbitrary programmatic integration, so an outside agent cannot discover and drive HyperProbe directly from the reviewed page.
Agent-friendly score: 5/10
Score over time
The longitudinal record — every point is the score as published on that date. Raw series.
Evidence
- Has coding agents drop a read-only probe on the exact failing prod line to capture unlogged data without redeploy — source (2026-08-25) verified
- Y-Combinator-backed AI on-call agent working with major coding agents across several languages — source (2026-08-25) verified
- Claims time-to-root-cause of 3–4 hours reduced to under 10 minutes and redeployments per incident reduced to zero — source (2026-08-25)
scorecard.json · transparency passport · registry · methodology
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