Implement a VASP License Risk Auditor with Pydantic AI & AI SDK
Regulatory compliance officers require fast, type-safe audits of Virtual Asset Service Provider (VASP) license applications against MAS mandates. Develop a Pydantic AI agent with Vercel AI SDK streaming to validate corporate governance and crypto asset custody flows, attaining 100% structured schema compliance.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a type-safe VASP compliance verification service using Pydantic AI and Vercel AI SDK to audit digital asset license submissions.
How work is evaluated
Evaluates schema strictness and compliance rule checking against VASP submission documents.
Challenge datasets
Public datasets, plus the private datasets you have access to.
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
Verifies output perfectly adheres to Pydantic schema definitions.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
audit_accuracy
Accuracy of pass/fail regulatory decisions on synthetic VASP filings. • target: 0.98 • 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
Master Pydantic AI for strongly-typed output generation and validation
Integrate Vercel AI SDK streaming responses into compliance audit dashboards
Implement automated regulatory checks based on MAS VASP licensing guidelines
Handle validation errors cleanly using Pydantic's dynamic fallback patterns
Reference links and supporting material
Synthetic dataset of 50 VASP license applications with varied compliance defects.
Challenge setup
Evaluates schema strictness and compliance rule checking against VASP submission documents.
Challenge input
JSON containing entity details and custody architecture
Pydantic AI
Provides type-safe structured output generation and dependency injection.
Vercel AI SDK
Seamless streaming of agent outputs and UI state updates.
Evaluated output
JSON conforming to strict Pydantic AuditResult model
- Verifies output perfectly adheres to Pydantic schema definitions.
- Accuracy of pass/fail regulatory decisions on synthetic VASP filings. • target: 0.98 • range: 0-1
- Benchmark: RegTechComplianceEval
- Audit Accuracy target: 0.98
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
- Pydantic AI · Required
- Vercel AI SDK · Optional
- LangWatch · Optional
- Pydantic AI · Required
- Vercel AI SDK · Optional
Evaluation contract
- pydantic_schema_valid · Weight 1
- audit_accuracy · Weight 1
Metrics
- audit_accuracy · Target 0.98
Benchmarks
- RegTechComplianceEval
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing entity details and custody architecture
Output: JSON conforming to strict Pydantic AuditResult model
Execution harness: Python
Evaluation tools
- Pydantic AI · Agent Framework
- Vercel AI SDK · Streaming Infrastructure
Recipe freeze marker
No recipe freeze time is recorded.