Your coding agent writes code.Now let it fix prod too.
If your team uses Cursor, Claude Code, or Codex, you know the workflow. When prod breaks, you’re grepping logs, adding console.logs, redeploying, hoping the AI’s guess is right. It can take hours.
Hyperprobe’s SDK lets your coding agent capture actual runtime state from your running services. Non-blocking probes, real variable values, fix in minutes.
Read-only. PII redacted by default. <1% overhead.
Works with Cursor, Claude Code, Codex·Node.js·Java·Python
The runtime data layer for AI-native software.
60%
Snapshot captured.
Root cause in the same thread.
Your coding agent flags the suspect line. The SDK probes, captures the variable state, and hands it back. Your agent diagnoses, scopes the blast radius, and proposes a fix you can verify before merging. One conversation. One fix that works.
Thread not paused · Zero user impact
user.tier = 'free'
amount = 299.99
balance = None ← null silently
db_conn = timeout (3,000ms)
db.pool.active = 5 / 5 in use
db.pool.waiting = 12 queued
get_balance() returns None on DB timeout — the free-tier connection pool is saturated (5/5 slots). The code silently treats None as zero balance and throws InsufficientFunds. Users see the wrong error. Paid-tier pool is separate and unaffected.
847 failures · 89% free-tier · pool exhausted → null → wrong error shown
Pool 5→20. Raise DBTimeoutException explicitly, don't return None.
Circuit breaker on balance endpoint. Alert at pool > 80%.
Your AI agent saw the same exception.
It still can't tell you what caused it.
Your AI agent reads the same logs you do. It reasons over the same traces, the same metrics, the same stack traces. And when it diagnoses? It produces a confident, plausible answer that turns out to be wrong — because the evidence it needed wasn't in the log stream. The bug is still there. Your customers still hit it.
3-4h
Average time spent searching through scattered log lines to manually reconstruct application state.
45m
Average time required for a developer to re-enter the deep flow zone after a context switch.
10m
Total time spent identifying, tracing variables, and deploying a permanent patch using Hyperprobe.
Runtime evidence, not log guesses.
Up and running in three steps.
Hyperprobe does not require writing complex span metrics or wrapping services. Deploy the daemon alongside your app in minutes.
Install the Agent
<dependency>
<groupId>io.hyperprobe</groupId>
<artifactId>agent</artifactId>
<scope>runtime</scope>
</dependency>
Add the SDK dependency, instrument your service, deploy. Commit once — never touch it again.
Set a Breakpoint from IDE
Incident fires → open VS Code → click to set a live breakpoint. No redeploy. No restart.
Snapshot Captured
SNAPSHOT — line 34 · 14:23:07 UTC
Thread not paused · Zero user impact
b.amount = null
o.total = 299.99
o.userId = 'usr_9182'
process():34 → handleRequest():112
When the breakpoint is hit by a real request, Hyperprobe captures the full state without pausing the thread.
Same incident. Same engineer. Same logs.
One probe changes everything.
Why waste days adding log statements, rebuilding containers, and redeploying microservices just to capture a single undefined variable?
| What the engineer needs | LOGS + APM | With HyperProbe |
|---|---|---|
| Where did it break? | 5 min | 5 min |
| Which users are affected? | 25 min — tier not in logs, needs separate DB query | Instant — user.tier = 'free', 89% of failures |
| What did the database return? | 60 min — redeploy needed, not logged | Instant — balance = null, timed out after 3000ms |
| Why did the database fail? | 60 min — pool state invisible to logs | Instant — pool 5/5 full, 12 requests queued |
| What damage to users? | 90 min — silent null→zero only visible after multiple deploys | Instant — null treated as zero, user saw wrong error |
| Total Time | 3-4 hours | 9 minutes |
Your three hard requirements.
Met.
Production debugging requires absolute data compliance. Hyperprobe redacts sensitive information locally, meaning database-level and token-level PII never leaves your networks.
Read-only architecture
The agent cannot mutate application state, cannot crash your pod, cannot affect customer requests. Probes capture variable values from running memory. They never write. Zero blast radius by design.
PII redaction in-memory
Sensitive data is masked at the agent, in your infrastructure, before any snapshot is captured. PII never leaves your network. You configure the redaction rules; we never see the raw values.
Zero AI training on your code
Your snapshots, your code paths, your runtime data are never used to train foundation models. Not by HyperProbe. Not by upstream providers. Your IP stays your IP.
See what our clients are saying.
Learn how our solutions have empowered our clients to debug in minutes.
"Sync issues used to take us days to reproduce locally. Hyperprobe caught the silent data mismatch in production on the first attempt."
Aishwarya Maurya
Tech Lead @ CheQ Digital
"During peak traffic, our listing service was black-boxing failures. Hyperprobe let us inspect the live memory state during the spike. We fixed the race condition in the same hour."
Bhagwan Bansal
SDE @ Housing.com
Questions engineers
actually ask.
Still got questions?
Schedule a short session with our platform engineering team.
Your next production incident is coming.
Will your team have Hyperprobe ready?
Set up takes 5 minutes. No code changes required.
Zero code changes. PII redacted. Under 1% CPU overhead.