Type-Safe SCADA Alarm Triaging with Pydantic AI
Factory operators suffer from alarm fatigue when thousands of unprioritized SCADA alarms trigger during line downtime events. Build a type-safe alarm triaging agent using Pydantic AI that ingests raw telemetry alarm vectors and produces strict Pydantic schema validated alert action plans. Achieve 100% schema validation compliance with sub-100ms structured inference.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a type-safe alarm parsing and triaging agent using Pydantic AI for structured SCADA root cause diagnostic responses.
How work is evaluated
Evaluates schema compliance, validation error rates, and alarm grouping accuracy under heavy alarm load.
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.
schema_validation_pass
Ensure zero schema validation errors in response model
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
root_cause_accuracy
Accuracy in correctly identifying primary cascading root cause alarm • 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
Build a Pydantic AI Agent leveraging strict Pydantic model response validation
Parse high-density SCADA alarm bursts into prioritized root cause action hierarchies
Ensure robust error recovery and fallback schema handling when inputs contain missing data
Reference links and supporting material
SCADA alarm logs captured during simulated multi-equipment trip events across chemical process lines.
How this agent runs
Evaluates schema compliance, validation error rates, and alarm grouping accuracy under heavy alarm load.
Challenge input
JSON array of raw SCADA alarm records
Pydantic AI
Provides type-safe agent execution with native Pydantic validation guarantees.
Evaluated output
Pydantic JSON object conforming to TriageResult schema
- Ensure zero schema validation errors in response model
- Accuracy in correctly identifying primary cascading root cause alarm • target: 0.95 • range: 0-1
- Benchmark: ISA-18.2 Alarm Triage Suite (ALARM-TRIAGE-2025)
- Root Cause Accuracy target: 0.95
- 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
- schema_validation_pass · Weight 1
- root_cause_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.