Skip to main content
Loading navigation
Back to challenges

Motor Control Center Automated Estimator with OpenAI Agents SDK and Cerebrium

Motor control center (MCC) engineering and configuration leads to long delivery lead times due to manual bill-of-materials generation. Implement an automated MCC configurator using OpenAI Agents SDK deployed on Cerebrium to generate BOMs and wiring diagrams from single-line drawings. Reduce design review iteration time by 80%.

EngineeringHosted by Vera
Sign in free to start

What you are building

Build an automated motor control center estimation pipeline using OpenAI Agents SDK deployed serverlessly on Cerebrium.

How work is evaluated

Evaluation

Evaluate the correctness of the generated MCC Bill of Materials given standard motor power and control requirements.

Datasets

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

How submissions are scored

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

complete_bom_coverage

All input motors have corresponding control gear in output

Binary check

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

Dimension 2

bom_accuracy

Accuracy of component rating selection • target: 0.95 • range: 0-1

Binary check

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

Learning goals

  • Deploy an OpenAI Agents SDK worker on Cerebrium serverless infrastructure

  • Parse single-line diagram specs into structured motor control center (MCC) requirements

  • Generate valid Bills of Materials (BOM) including circuit breakers, starters, and VFDs

  • Evaluate lead-time reduction and accuracy against industry benchmarks

Resources

Dataset notes

Load lists and electrical specifications for 30 motor control center configurations.

Challenge setup

Evaluate the correctness of the generated MCC Bill of Materials given standard motor power and control requirements.

Challenge input

JSON array of motor loads with HP, voltage, and starter type requirements

OpenAI Agents SDK

Agent building framework with tools.

Cerebrium

Serverless container execution host for low-latency agent hosting.

Evaluated output

JSON object with bom_items list containing component descriptions and part numbers

Evaluation checks
  • All input motors have corresponding control gear in output
  • Accuracy of component rating selection • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: Electrical Systems BOM Generation Benchmark
  • Bom Accuracy target: 0.95
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • OpenAI · Required
  • Cerebrium · Optional
  • Azure OpenAI Service · Optional

Evaluation contract

  • complete_bom_coverage · Weight 1
  • bom_accuracy · Weight 1

Metrics

  • bom_accuracy · Target 0.95

Benchmarks

  • Electrical Systems BOM Generation Benchmark
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON array of motor loads with HP, voltage, and starter type requirements

    Output: JSON object with bom_items list containing component descriptions and part numbers

    Execution harness: Python

Evaluation tools

  • OpenAI Agents SDK · Agent Framework
  • Cerebrium · Inference Runtime

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Motor Control Center Automated Estimator with OpenAI Agents SDK and Cerebrium