Skip to main content
Loading navigation
Back to challenges

AutoGen Multi-Agent Factory Scaling Assistant for Equipment Rollouts

Scaling Industry 4.0 initiatives across multi-factory sites frequently fails due to fragmented machine configuration models. Develop a multi-agent conversational system using AutoGen to harmonize OPC UA tags and standardize downtime categorization logic across facilities. The system must achieve 95% alignment on asset taxonomy mappings across five simulated factory sites.

EngineeringHosted by Vera
Sign in free to start

What you are building

Deploy an AutoGen multi-agent group chat (Factory Architect, OPC UA Specialist, Maintenance Lead) to resolve equipment taxonomy mismatches across multi-plant deployments.

How work is evaluated

Evaluation

Evaluates multi-agent group chat consensus rate and OPC UA tag harmonization precision across distinct plant schemas.

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

schema_harmonization

Ensures all local tags are mapped to non-null unified tag definitions

Binary check

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

Dimension 2

taxonomy_alignment_score

Accuracy of tag categorization against reference standard • 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

  • Configure an AutoGen `GroupChat` with specialized industrial roles

  • Harmonize disparate factory OPC UA tag naming conventions (e.g., ISO 14224 standard)

  • Standardize downtime cause codes across distinct plant MES instances

  • Automate consensus-building and output unified asset taxonomy schemas

Resources

Dataset notes

Heterogeneous tag export datasets from five manufacturing plants containing unstandardized PLC and MES tags.

Challenge setup

Evaluates multi-agent group chat consensus rate and OPC UA tag harmonization precision across distinct plant schemas.

Challenge input

JSON object with array of plant_schemas containing raw local tag names and descriptions

AutoGen

Assigned framework for conversational multi-agent systems.

Evaluated output

JSON object mapping local tags to standardized_opc_tag and iso_14224_category

Evaluation checks
  • Ensures all local tags are mapped to non-null unified tag definitions
  • Accuracy of tag categorization against reference standard • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: Multi-Plant Taxonomy Harmonization Benchmark
  • Taxonomy Alignment Score 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
  • AutoGen · Required
  • Microsoft AutoGen · Optional

Evaluation contract

  • schema_harmonization · Weight 1
  • taxonomy_alignment_score · Weight 1

Metrics

  • taxonomy_alignment_score · Target 0.95

Benchmarks

  • Multi-Plant Taxonomy Harmonization Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with array of plant_schemas containing raw local tag names and descriptions

    Output: JSON object mapping local tags to standardized_opc_tag and iso_14224_category

    Execution harness: Python

Evaluation tools

  • AutoGen · Multi-Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about AutoGen Multi-Agent Factory Scaling Assistant for Equipment Rollouts