Motor Control Center Assembly Multi-Agent Scheduler using AutoGen and Claude 4 Opus
Designing Motor Control Center (MCC) units often suffers from long engineering lead times due to bottlenecked procurement and electrical schematic validation. Build a collaborative multi-agent assembly system with AutoGen and Claude 4 Opus to automatically verify electrical wiring constraints and cut delivery lead time estimates by 50%.
What you are building
The core problem, expected build, and operating context for this challenge.
Deploy an AutoGen multi-agent system powered by Claude 4 Opus to cross-validate MCC component schematics and optimize fabrication task order.
How work is evaluated
Evaluates accuracy of detected schematic mismatch errors and delivery schedule reduction percentage.
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.
Violation Flag Check
Ensures undersized busbar is flagged as non-compliant.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
lead_time_reduction_percent
Simulated production schedule optimization percentage. • target: 50 • range: 0-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Configure AutoGen multi-agent group chats with distinct roles (Electrical Engineer, Procurement Specialist, Assembly Scheduler)
Utilize Claude 4 Opus reasoning capabilities to detect busbar sizing and breaker mismatch errors in schematics
Generate an optimized assembly critical path schedule under dynamic component lead-time constraints
Reference links and supporting material
Specifications for Motor Control Center panels including circuit breaker ratings, busbar dimensions, and component lead times.
Challenge setup
Evaluates accuracy of detected schematic mismatch errors and delivery schedule reduction percentage.
Challenge input
JSON with breaker_rating_amp, busbar_rating_amp, and line_voltage
AutoGen
Enables conversing multi-agent groups with specialized system prompts.
Claude 4 Opus
Provides deep reasoning for complex electrical code and constraint verification.
Evaluated output
JSON with is_compliant, violation_type, and suggested_rating_amp
- Ensures undersized busbar is flagged as non-compliant.
- Simulated production schedule optimization percentage. • target: 50 • range: 0-100
- Benchmark: Multi-Agent Industrial Planning Benchmark
- Lead Time Reduction Percent target: 50
- 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
- AutoGen · Required
- Claude Code · Optional
- Claude Code · Optional
- Claude 4 Opus · Optional
Evaluation contract
- Violation Flag Check · Weight 1
- lead_time_reduction_percent · Weight 1
Metrics
- lead_time_reduction_percent · Target 50
Benchmarks
- Multi-Agent Industrial Planning Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with breaker_rating_amp, busbar_rating_amp, and line_voltage
Output: JSON with is_compliant, violation_type, and suggested_rating_amp
Execution harness: Python
Evaluation tools
- AutoGen · Multi-Agent Framework
- Claude 4 Opus · High-Reasoning LLM
Recipe freeze marker
No recipe freeze time is recorded.