Process Flow Measurement Telemetry Validation Agent with Pydantic AI
Industrial process flowmeters frequently experience multiphase flow distortion or aeration bursts that corrupt volumetric flow totals. Construct a type-safe telemetry validation agent using Pydantic AI to validate raw sensor telemetry streams and output structured flow corrections. Achieve 99.5% schema validation pass rate with precise invalid packet handling.
What you are building
The core problem, expected build, and operating context for this challenge.
Implement a Pydantic AI agent with explicit schema types to parse flowmeter telemetry streams, catch sensor noise, and calculate adjusted flow totals.
How work is evaluated
Evaluates Pydantic AI type safety, validation pass rates, and flow calculation precision.
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.
strict_pydantic_validation
Ensures the response passes Pydantic model validation without exceptions.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
schema_pass_rate
Percentage of agent outputs adhering exactly to output target schema. • target: 0.995 • 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
Define Pydantic schemas for Coriolis and Electromagnetic flowmeter telemetry packages
Build a Pydantic AI agent with type-safe tool dependencies and validation functions
Filter out invalid aeration spikes and compute accurate mass/volumetric flow totals
Reference links and supporting material
Coriolis flowmeter time-series data streams containing clean operational segments mixed with entrained gas aeration bursts.
How this agent runs
Evaluates Pydantic AI type safety, validation pass rates, and flow calculation precision.
Challenge input
JSON containing raw_flow_records list with density, volumetric_flow, and pressure
Pydantic AI
Type-safe agent framework with structured validation
Evaluated output
Validated JSON object conforming to ProcessFlowSummary schema
- Ensures the response passes Pydantic model validation without exceptions.
- Percentage of agent outputs adhering exactly to output target schema. • target: 0.995 • range: 0-1
- Benchmark: Structured Industrial Telemetry Validation
- Schema Pass Rate target: 99.5%
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- Pydantic AI · Required
- Pydantic AI · Required
Evaluation contract
- strict_pydantic_validation · Weight 1
- schema_pass_rate · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.