Jafurah Gas Field Expansion Risk Evaluation with Pydantic AI
Evaluating contractor bids for Saudi Aramco's Jafurah fifth expansion phase requires type-safe extraction of reservoir pressure ratings and execution timelines. Build a Pydantic AI agent that enforces strong type hints and Pydantic schema validation for bid technical submittals. Achieve 100% type validation compliance with zero unhandled schema errors.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a type-safe Pydantic AI evaluation agent for Jafurah gas expansion contractor bids.
How work is evaluated
Evaluates Pydantic AI agent schema enforcement and bid evaluation accuracy.
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 Check
Verifies that output passes Pydantic data type validation without raising ValidationError.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Schema Parsing Success Rate
Percentage of agent evaluations successfully parsed into Pydantic models. • 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
Implement Pydantic AI `Agent` with structured response validation
Validate technical drilling parameters against Saudi Aramco safety specs
Inject external dependency tools for live cost-index lookups
Generate clean JSON evaluation reports guaranteed to match target schema
Reference links and supporting material
Contractor bid proposals narrative text for Saudi Aramco Jafurah unconventional gas field expansion.
How this agent runs
Evaluates Pydantic AI agent schema enforcement and bid evaluation accuracy.
Challenge input
Text prompt containing contractor proposal narrative
Pydantic AI
Assigned framework for structured validation.
Evaluated output
Validated JSON object adhering to JafurahBidEval Pydantic model
- Verifies that output passes Pydantic data type validation without raising ValidationError.
- Percentage of agent evaluations successfully parsed into Pydantic models. • target: 1 • range: 0-1
- Benchmark: PydanticBench
- Schema Parsing Success Rate target: 100%
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- Pydantic AI · Required
- AutoML (H2O) · Optional
- All Hands AI · Optional
- Pydantic AI · Required
Evaluation contract
- Schema Validation Check · Weight 1
- Schema Parsing Success Rate · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.