Rapid Motor Control Center (MCC) Configuration Generator with OpenAI Agents
Custom Motor Control Center (MCC) engineering typically requires months of design iterations. Build an automated design assistant using OpenAI Agents SDK and Groq Cloud to process customer electrical specifications and instantly output standardized panel layout wireframes and component bills of materials.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct an OpenAI Agents SDK application running on Groq Cloud to automate electrical specification parsing and MCC panel bill of materials (BOM) generation.
How work is evaluated
Evaluates the agent's speed and correctness in converting raw motor horsepower and voltage requirements into accurate electrical component lists.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
bom_completeness
Validates all necessary starters and breakers are included.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
inference_latency_seconds
Time taken to generate complete configuration using Groq acceleration. • target: 1 • range: 0.1-3
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build interactive conversational agents using OpenAI Agents SDK
Leverage Groq Cloud API endpoints for high-speed LLM function execution
Parse motor rating, starter type, and busbar capacity specifications
Generate complete electrical enclosure BOM JSON datasets
Reference links and supporting material
Catalog of motor starters, circuit breakers, enclosure dimensions, and thermal specs.
Challenge setup
Evaluates the agent's speed and correctness in converting raw motor horsepower and voltage requirements into accurate electrical component lists.
Challenge input
JSON with motor_hp, voltage, starter_type, and bus_rating_amps.
OpenAI Agents SDK
Provides reliable multi-turn conversation and function execution abstractions.
Groq Cloud
Delivers sub-second response times for complex structured tool output requests.
Evaluated output
JSON containing bom_items array and total_enclosure_width_inches.
- Validates all necessary starters and breakers are included.
- Time taken to generate complete configuration using Groq acceleration. • target: 1 • range: 0.1-3
- Benchmark: Electrical CAD Generation Benchmark
- Inference Latency Seconds target: 1
- 1 public reference case
- Python execution harness
- Python 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
- OpenAI · Required
- Azure OpenAI Service · Optional
- Groq Cloud · Optional
Evaluation contract
- bom_completeness · Weight 1
- inference_latency_seconds · Weight 1
Metrics
- inference_latency_seconds · Target 1
Benchmarks
- Electrical CAD Generation Benchmark
- robotics
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with motor_hp, voltage, starter_type, and bus_rating_amps.
Output: JSON containing bom_items array and total_enclosure_width_inches.
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · Agent Framework
- Groq Cloud · Inference Acceleration
Recipe freeze marker
No recipe freeze time is recorded.