Distributed Safety Logic and PLC Orchestration with AutoGen
Managing complex plant operations across distributed safety controllers requires continuous communication and state verification between PLC agents. You will build a multi-agent conversational control system using Microsoft AutoGen where virtual PLC agents perform automated peer reviews on safety interlock state updates. The system must achieve zero undetected state deadlocks during automated negotiations.
What you are building
The core problem, expected build, and operating context for this challenge.
Implement a Microsoft AutoGen multi-agent conversational setup where PLC controller agents negotiate safety interlock conditions.
How work is evaluated
Evaluates deadlock prevention and state matrix validity in AutoGen conversational safety negotiations.
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.
deadlock_free_check
Ensures conversation terminated naturally without reaching maximum loop iterations.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
negotiation_turn_count
Turns needed to achieve safe interlock agreement • target: 3 • range: 1-10
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Construct multi-agent conversational networks using Microsoft AutoGen (`AssistantAgent`, `UserProxyAgent`)
Implement group chat managers to coordinate inter-PLC state negotiation loops
Integrate formal state-machine checking tools into AutoGen agent execution steps
Evaluate conversation convergence and detect execution deadlocks in distributed controls
Reference links and supporting material
Distributed PLC IO link tables and safety cause-and-effect matrix specifications.
Challenge setup
Evaluates deadlock prevention and state matrix validity in AutoGen conversational safety negotiations.
Challenge input
JSON with initiating_event, zone1_state, and zone2_state
AutoGen
Required framework assignment.
Evaluated output
JSON containing safe_target_state, interlock_verified, conversation_turns, and deadlock_detected
- Ensures conversation terminated naturally without reaching maximum loop iterations.
- Turns needed to achieve safe interlock agreement • target: 3 • range: 1-10
- Benchmark: Distributed Industrial Agent Communication Benchmark
- Negotiation Turn Count target: 3
- 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
- deadlock_free_check · Weight 1
- negotiation_turn_count · Weight 1
Metrics
- negotiation_turn_count · Target 3
Benchmarks
- Distributed Industrial Agent Communication Benchmark
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with initiating_event, zone1_state, and zone2_state
Output: JSON containing safe_target_state, interlock_verified, conversation_turns, and deadlock_detected
Execution harness: Python
Evaluation tools
- AutoGen · Multi-Agent Framework
Recipe freeze marker
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