Pydantic AI Muni Debt Disaster Recovery Financing Auditor
Nonprofits and municipalities issuing municipal debt to rebuild wildfire-impacted communities require stringent, type-safe financial compliance audits. Build a type-safe Pydantic AI agent that validates municipal bond allocations, construction draws, and regulatory debt limits.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a type-safe financial audit system using Pydantic AI to validate municipal debt financing for wildfire housing rebuilding.
How work is evaluated
Evaluates compliance validation accuracy for municipal debt draw requests.
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_type_validation
Ensures response strictly validates against expected Pydantic model.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
covenant_detection_accuracy
Accuracy in identifying covenant limit overages. • 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
Define type-safe Pydantic models for bond proceed allocation and draw requests
Build a Pydantic AI agent (`Agent(model, result_type=...)`) enforcing output validation
Implement dependency injection for accessing real-time bond covenant databases
Audit disaster recovery housing expenditures against municipal tax-exempt status rules
Reference links and supporting material
Synthetic municipal bond financing draw requests and covenant limit datasets representing disaster rebuilding efforts.
How this agent runs
Evaluates compliance validation accuracy for municipal debt draw requests.
Challenge input
JSON draw request containing cost_categories, amount_usd, and bond_covenant_limits
Pydantic AI
Provides type-safe agent execution and structured output parsing.
Evaluated output
JSON audit result with is_compliant, flagged_items, approved_amount_usd
- Ensures response strictly validates against expected Pydantic model.
- Accuracy in identifying covenant limit overages. • target: 1 • range: 0-1
- Benchmark: MuniBondAuditBench
- Covenant Detection Accuracy target: 1
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Pydantic AI · Required
- Aim · Optional
- AWS · Optional
- Pydantic AI · Required
Evaluation contract
- pydantic_type_validation · Weight 1
- covenant_detection_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.