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.
What you are building
The core problem, expected build, and operating context for this challenge.
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
Evaluates multi-agent group chat consensus rate and OPC UA tag harmonization precision across distinct plant schemas.
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.
schema_harmonization
Ensures all local tags are mapped to non-null unified tag definitions
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
taxonomy_alignment_score
Accuracy of tag categorization against reference standard • target: 0.95 • range: 0-1
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 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
Reference links and supporting material
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
- 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
- Benchmark: Multi-Plant Taxonomy Harmonization Benchmark
- Taxonomy Alignment Score target: 0.95
- 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.
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Configured tools
- 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
- 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.