Multi-Agent Integrator Knowledge Assistant with AutoGen and Qwen 3
Control system integrators lose hundreds of hours searching legacy technical manuals during commissioning. Build a multi-agent system using AutoGen and Qwen 3 to synthesize field manuals and troubleshoot PLC/SCADA integration errors. Achieve over 90% precision on industrial troubleshooting queries.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct an AutoGen multi-agent team powered by Qwen 3 to solve complex system integrator technical queries and hardware faults.
How work is evaluated
Assess precision and recall of multi-agent answers against expert solutions for CSIA integrator troubleshooting cases.
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.
correct_manual_citation
Cites appropriate hardware reference manual
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
solution_precision
Precision of recommended troubleshooting actions • target: 0.9 • 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
Build an AutoGen multi-agent setup (User Proxy, Technical Search Agent, PLC Expert Agent)
Incorporate Qwen 3 for high-accuracy multilingual industrial reasoning
Integrate vector search tools for hardware manual documentation retrieval
Evaluate multi-agent convergence on complex commissioning scenarios
Reference links and supporting material
Collection of CSIA integrator troubleshooting cases across Ethernet/IP, Modbus TCP, and Profinet error logs.
Challenge setup
Assess precision and recall of multi-agent answers against expert solutions for CSIA integrator troubleshooting cases.
Challenge input
JSON containing error_code, controller_type, and symptom_description
AutoGen
Enables multi-agent conversational decision workflows.
Qwen 3
Provides underlying domain reasoning for technical manuals.
Evaluated output
JSON with diagnostic_step, solution_code, and manual_reference
- Cites appropriate hardware reference manual
- Precision of recommended troubleshooting actions • target: 0.9 • range: 0-1
- Benchmark: Industrial System Integrator QA Benchmark
- Solution Precision target: 0.9
- 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
- Alibaba Cloud (Qwen) · Optional
- Qwen 3 · Optional
Evaluation contract
- correct_manual_citation · Weight 1
- solution_precision · Weight 1
Metrics
- solution_precision · Target 0.9
Benchmarks
- Industrial System Integrator QA Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing error_code, controller_type, and symptom_description
Output: JSON with diagnostic_step, solution_code, and manual_reference
Execution harness: Python
Evaluation tools
- AutoGen · Multi-Agent Framework
- Qwen 3 · Language Model
Recipe freeze marker
No recipe freeze time is recorded.