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Challenge

Build an MHS Hardware Control Safety Adapter with Mastra AI and OpenAI o3

Deploy a Model Hardware Standard (MHS) safety adapter in TypeScript using Mastra AI and OpenAI o3 to validate device control actions against power and thermal bounds. Complete the task by achieving 100% detection of unauthorized actuator commands under simulated high-throughput workloads.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build an MHS safety filter using Mastra AI workflows and OpenAI o3 reasoning to intercept hardware control packets.

Delivery guide

How work is evaluated

Evaluation

Evaluates MHS compliance parsing accuracy, invalid action rejection rate, and validation throughput.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

Unsafe Torque Rejection

Verifies that out-of-bounds torque values are correctly flagged as unsafe.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

Safety Precision

Ratio of correctly identified safety violations. • target: 1 • range: 0.99-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Implement the Model Hardware Standard (MHS) specification interface in Mastra AI.

  • Integrate OpenAI o3 for real-time safety constraint verification of hardware actions.

  • Create a deterministic telemetry interceptor for physical hardware actuators.

  • Evaluate safety validation latency under high message volume.

Resources and assets

Reference links and supporting material

Dataset notes

Simulated stream of 500 MHS actuation requests including 50 intentional safety limit violations.

Challenge setup

Evaluates MHS compliance parsing accuracy, invalid action rejection rate, and validation throughput.

Challenge input

JSON array of MHS action payloads

Mastra AI

Primary framework for agentic workflow orchestration in TypeScript

OpenAI o3

Provides deep reasoning for complex physical constraints

Evaluated output

JSON array of safety verdicts with latency metrics

Evaluation checks
  • Verifies that out-of-bounds torque values are correctly flagged as unsafe.
  • Ratio of correctly identified safety violations. • target: 1 • range: 0.99-1
Configured evaluation evidence
  • Benchmark: MHS Safety Verification Suite
  • Safety Precision target: 1
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Mastra AI · Required
  • OpenAI · Optional
Policy Serving
  • o3 · Optional
Orchestration
  • Mastra AI · Required

Evaluation contract

  • Unsafe Torque Rejection · Weight 1
  • Safety Precision · Weight 1

Metrics

  • Safety Precision · Target 1

Benchmarks

  • MHS Safety Verification Suite
  • robotics

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON array of MHS action payloads

    Output: JSON array of safety verdicts with latency metrics

    Execution harness: JavaScript

Evaluation tools

  • Mastra AI · Agent Framework
  • OpenAI o3 · Reasoning Model

Recipe freeze marker

No recipe freeze time is recorded.

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