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.
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.
How work is evaluated
Evaluates MHS compliance parsing accuracy, invalid action rejection rate, and validation throughput.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Unsafe Torque Rejection
Verifies that out-of-bounds torque values are correctly flagged as unsafe.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Safety Precision
Ratio of correctly identified safety violations. • target: 1 • range: 0.99-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
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.
Reference links and supporting material
Simulated stream of 500 MHS actuation requests including 50 intentional safety limit violations.
How this agent runs
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
- Verifies that out-of-bounds torque values are correctly flagged as unsafe.
- Ratio of correctly identified safety violations. • target: 1 • range: 0.99-1
- Benchmark: MHS Safety Verification Suite
- Safety Precision target: 1
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Mastra AI · Required
- OpenAI · Optional
- o3 · Optional
- Mastra AI · Required
Evaluation contract
- Unsafe Torque Rejection · Weight 1
- Safety Precision · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.