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
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.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
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.
Learning goals
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
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
- 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)
Shows the first task, up to three tools, and up to three evidence items.
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
Metrics
- Safety Precision · Target 1
Benchmarks
- MHS Safety Verification Suite
- robotics
Reference cases
- 1 public reference case
Workflow tasks
- 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.