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autonomous diagnostics agent

Your system failed in production. VolatileSys already found the root cause — and wrote the fix.

From a fleet of microcontrollers to a backend service or a game: it ingests your telemetry, autonomously diagnoses the failure against the evidence — the datasheet, the stack trace, the metrics — and drafts the fix, for a human to approve.

  • firmware
  • backend
  • web
  • games
  1. ingest
  2. decode
  3. diagnose
  4. approve
needs reviewdevice 9f3a12c4…
confidence0.91
  1. gatherpulled 300 s telemetry window before the fault
  2. localizeISR starvation — TIM2 handler blocks the scheduler
  3. researchmatched RM0090 reset behaviour, p.412
  4. resolvedrafted patch · validated (clang -fsyntax-only)
root cause

Frame processing runs inside the TIM2 interrupt, starving the RTOS scheduler until the watchdog resets the MCU.

faultfirmware/tim2_irq.c:88· citeddatasheet p.412
generated patch · tim2_irq.c
void TIM2_IRQHandler(void) {
- process_frame(); // blocks inside the ISR
+ xQueueSendFromISR(rx_q, &frame, NULL);
TIM2->SR &= ~TIM_SR_UIF;
}
awaiting engineer review
  • 174
    backend tests on every commit
  • 100%
    agent output behind a human approval gate
  • org-scoped
    tenant isolation on every query
  • SOC 2
    on the roadmap — ask us where we are
why production failures cost so much

Diagnosing a production failure is fragmented — and it's your senior engineers who pay for it.

Something fails in production and the clock starts. Three disconnected gaps stand between the alert and the fix.

visibility gap

Generic dashboards weren't built for what you ship.

Heap and stack pressure and ISR frequencies on a device; the unhandled rejection and the latency spike in a service — the signals that precede a failure get flattened into charts that never modeled them. You find out it died, not why it was dying.

context gap

Your tickets aren't connected to your telemetry.

A crash report arrives as a screenshot and a hunch. The data that actually explains it lives in another system, at a timestamp no one has correlated yet. Every investigation starts from zero.

solution gap

Every root-cause analysis is manual, senior work.

An engineer pulls logs by hand, scrolls a 500-page datasheet for one register or digs through a stack trace and the code, reasons about the failure, and hand-writes the patch — the same slow loop, incident after incident.

the cost

Mean time to resolution stretches into days while your most expensive people burn hours per incident — reproducing the crash, reading datasheets, and writing patches by hand. It doesn't scale with your fleet, and it doesn't scale with your team.

one substrate, every domain

A crash is a crash — a device, a service, or a game.

The same pipeline ingests telemetry and errors from anything you ship, groups them into issues, and alerts on them — then points the same diagnostics agent at the failure. Binary decoding and datasheet grounding are the firmware specialties; the rest of the platform is domain-agnostic.

firmware & iot

Microcontroller fleets

Bit-packed frames decoded to named metrics: heap and stack pressure, ISR rates, brown-outs, watchdog resets — grounded in the chip datasheet.

backend

Services & APIs

Structured errors, latency, and throughput from your APIs and workers — stack traces grouped into issues, ready for the agent to investigate.

web

Browser apps

Uncaught exceptions and unhandled rejections captured client-side with two lines, alongside the metrics and events around the crash.

games

Clients & game servers

Crashes, frame-time, and session events from players and servers — on launch day and every patch after, with alerting when it spikes.

the same agent · on a software crash

needs reviewcheckout-api · issue a41f…
confidence0.88
  1. gathergrouped 214 occurrences + the request telemetry
  2. localizeunawaited DB call rejects after the response is sent
  3. researchgrounded in the stack trace — checkout.ts:42
  4. resolvedrafted patch · format-checked (typescript)
root cause

The debit call is not awaited, so its rejection surfaces after the response is sent and crashes the request worker under load.

faultsrc/routes/checkout.ts:42stack-grounded
generated patch · checkout.ts
export async function charge(account, amount) {
- const row = db.debit(account, amount); // promise floats
+ const row = await db.debit(account, amount);
return receipt(row);
}
awaiting engineer review
how it works

From alert to approved fix, on one connected path.

The agent runs the investigation a senior engineer would — in order, showing its work at every step.

  1. A crash alert or ticket fires.

    A threshold breach in the telemetry — or an incoming support ticket — triggers an investigation automatically. No one has to notice first.

  2. The agent pulls the telemetry leading up to the failure.

    full reasoning trace

    It gathers the exact window before the crash — heap and stack, ISR rates, latency, the errors and events that fired — already correlated to the source and the moment.

  3. It localizes the fault — down to the function, loop, or pin.

    Instead of a vague "it reset," you get a specific location: the interrupt handler that starved the scheduler, the unawaited call that crashed the worker.

  4. It researches the evidence.

    cited evidence

    For firmware, it retrieves the relevant register behavior from the chip datasheet — the 500-page search in seconds. For software, it grounds in the stack trace and the grouped occurrences.

  5. It drafts an RCA and a patch.

    A root-cause report grounded in the telemetry window and the cited evidence, plus a patch — syntax-validated for firmware, format-checked for software — every claim pointing back to its source.

  6. A human approves before anything ships.

    approval gate

    The patch lands in front of an engineer for review. Nothing is posted to a ticket or a repo until a person signs off.

replay a real investigation

Scrub the trace. Every step is on the record.

The persisted trace of one investigation — here, a firmware field failure; a software crash leaves the same audit trail. Drag through it.

step 05 · review

Waiting on a human — nothing ships without approval

status=needs_review
assigned: on-call firmware engineer
auto-post: DISABLED (always)
the telemetry foundation

A dashboard worth having on its own — and the ground truth the agent reasons over.

Before an agent can diagnose anything, it needs telemetry it can trust. VolatileSys decodes a firmware fleet down to the register, and takes structured events straight from an app — which is exactly why its conclusions hold up.

payload decoder · live
↓ rule: fleet-42 (bit-field layout)
heap_freeu16@0
11234 B
stack_freeu16@16
6100 B
cpu_pctu8@32
82 %
batt_mvu16@40
3714 mV
isr_tim2u16@56
14600 Hz
temp_ci8@72
23.5 °C
watchdogu1@80
1
crash_countu7@81
2
ingest

Bit-packed frames or structured JSON

High-frequency payloads from microcontroller fleets over MQTT or REST — or named metrics, events, and errors posted straight from an app. Buffered and processed off the request path, so nothing drops when a source floods.

decode

A payload decoder you can see

For firmware, user-defined bit-field rules turn raw hex into named metrics — change a rule and re-decode the stored history. Software sources skip decoding and report their metrics directly.

health

Signals for silicon and services

Heap-vs-stack leak detection, ISR frequencies, and watchdog resets for devices; errors grouped into issues, latency, and crash logging for apps — the signals generic dashboards never modeled.

why you can trust it

Autonomous, not unaccountable.

Handing diagnosis to an agent is a real leap. It earns the trust by making every conclusion checkable and keeping a human in control of anything that ships.

grounded

Every claim links to its evidence.

A root cause points to a specific telemetry window or a cited datasheet page — never an assertion the agent can’t back. If it can’t ground a claim, it doesn’t make it.

inspectable

The full reasoning trace is open.

Every step, tool call, and retrieved passage is persisted and replayable. You audit exactly how the agent reached its conclusion — it is never a black box.

human gate

Nothing ships without your approval.

The agent drafts; a person decides. No RCA or patch reaches an engineer, a ticket, or a repo until someone explicitly approves it — the gate is mandatory, not optional.

isolated

Your data stays yours.

Proprietary datasheets, source, telemetry, and errors are scoped to your organization. No other tenant’s agent can retrieve, embed, or cite them — isolation is enforced on every read.

pricing

Start with the dashboard. Add the agent when you're ready.

Pro
$49–99/mo

The telemetry dashboard — everything you need to see your fleet or your services.

  • Ingestion for any project — bit-packed frames or structured JSON, MQTT + REST
  • The visual payload decoder, or named metrics straight from your app
  • Real-time dashboards, health, error issues, and alerting
Get early access
the agent tier
Enterprise / Agent
$499+/mo

Everything in Pro, plus the autonomous agent that diagnoses failures and drafts the fix.

  • Everything in Pro
  • The autonomous diagnostics agent — RCA + firmware and code patches
  • Private datasheet uploads, scoped to your org
  • Ticketing integrations
Talk to us

Put the agent on your next production failure.

Early access is opening for teams shipping firmware, services, and games today — or talk to us about the Enterprise / Agent tier.