Software-Defined Controls (SDC) Testing Agent with Pydantic AI
Software-defined controls require strict type-safe automated testing to prevent runtime PLC crashes. Create a deterministic control software testing harness using Pydantic AI to validate IEC 61131-3 logic against edge simulation models. Ensure 100% type-safe payload validation and zero unhandled exceptions during execution.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a type-safe test runner agent using Pydantic AI to validate software-defined control logic against physical boundary conditions.
How work is evaluated
Evaluate test generation agent's output against expected type constraints and test coverage criteria.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
pydantic_schema_valid
Output passes Pydantic runtime schema validation
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
test_coverage
Coverage of specified control loop edge cases • target: 1 • 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 type-safe control validation models using Pydantic and Pydantic AI
Implement test case generation for software-defined control loops (PID, state machines)
Enforce rigorous validation on telemetry input/output data ranges
Generate automated compliance certificates for edge deployment
Reference links and supporting material
Control loop parameter specifications and physical limits for SDC testing.
How this agent runs
Evaluate test generation agent's output against expected type constraints and test coverage criteria.
Challenge input
JSON object describing PID loop setpoints and tolerance limits
Pydantic AI
Guarantees strict schema validation for model outputs.
Evaluated output
JSON test report detailing pass/fail results for edge test vectors
- Output passes Pydantic runtime schema validation
- Coverage of specified control loop edge cases • target: 1 • range: 0-1
- Benchmark: Type-Safe Agent Verification Benchmark
- Test Coverage target: 1
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Pydantic AI · Required
- Pydantic AI · Required
Evaluation contract
- pydantic_schema_valid · Weight 1
- test_coverage · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.