For figure ai infrastructure and model engineering teams

Helix model-runtime incident: offline worker or robot-control issue?

Separate dataset preparation and model execution from the robot action loop. Compare a resolved offline incident using relevant worker and device evidence.

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Why this workflow may fit Figure AI

Figure describes Helix as a vision-language-action system with distinct reasoning and control components running onboard GPUs. This evaluation focuses on approved offline training and model-runtime logs, not live robot control.

Read the official Figure AI workflow reference

Evaluation scope: The source describes the published Helix architecture, not every later release. Denpex is not a robot controller, safety monitor or safety-validation system. Limit this evaluation to non-production offline evidence approved for sharing.

Prepared from public materials. This is a proposed evaluation, not a report of an incident at Figure AI or an endorsement.

Selectable demo logs are shared examples, not logs from Figure AI. Upload a shareable incident from your own workflow to evaluate the fit.

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Replay a resolved Figure AI workflow incident

Upload your log file. Review the selected evidence and masking, then run diagnosis. Keep the original log for any follow-up questions.

Before uploading, review cloud data handling and local options.

training_logs.txt

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Upload or drop a plain-text log

Upload your log file. Large files are processed in your browser. Only selected evidence is submitted when you run diagnosis.

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No credit card. No automatic subscription. See whether Denpex helps with an incident you already understand.

Start your free 30-day Team evaluation

Up to 50 diagnoses a day for 30 days. Verify your work email to activate an eligible company evaluation, then replay a resolved incident and compare the evidence and next steps with your team's confirmed cause.

What a useful evaluation should establish

Compare one incident your team has already resolved with the first failure, quoted evidence and remaining questions. Begin with this question: Is this an offline training or model-loading incident? Only assign a process, device or recovery state when the submitted evidence establishes it.

Is this an offline training or model-loading incident?

Evidence to compare: Job context, runtime component and the first retained worker exception.

A real-world behavior issue is not automatically an infrastructure fault. Establish the offline process before interpreting device evidence.

Which model component produced the first failure?

Evidence to compare: Model revision, component identity and synchronized process timestamps.

The published architecture has distinct components with different roles. A high-level symptom does not establish which one failed.

What can be verified without executing robot actions?

Evidence to compare: Approved historical logs and the incident resolution already confirmed by the team.

Use a replay comparison rather than interacting with a robot. Infrastructure diagnosis cannot establish that a policy or robot behavior is safe.

Use Denpex alongside your existing telemetry tools to investigate submitted incident evidence. With monitoring configured on a supported plan, detected failures can trigger alerts and an automatic diagnosis from the captured evidence.

Inside Denpex

See the evidence, not just the claim

Follow the causal chain, inspect the quoted evidence and prepare the next handoff. These actual product captures use founder-account examples and the VS Code extension, with the source identified under each image. Open a capture to inspect it at full size.