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Challenge

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.

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Challenge brief

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.

Delivery guide

How work is evaluated

Evaluation

Evaluates schema strictness and compliance rule checking against VASP submission documents.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

pydantic_schema_valid

Verifies output perfectly adheres to Pydantic schema definitions.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

audit_accuracy

Accuracy of pass/fail regulatory decisions on synthetic VASP filings. • target: 0.98 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

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

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic dataset of 50 VASP license applications with varied compliance defects.

How this agent runs

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

Checks for
  • Verifies output perfectly adheres to Pydantic schema definitions.
  • Accuracy of pass/fail regulatory decisions on synthetic VASP filings. • target: 0.98 • range: 0-1
Proof of success
  • Benchmark: RegTechComplianceEval
  • Audit Accuracy target: 0.98
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox
View technical recipe

Configured tools

Action Space
  • Pydantic AI · Required
  • Vercel AI SDK · Optional
  • LangWatch · Optional
Orchestration
  • Pydantic AI · Required
  • Vercel AI SDK · Optional

Evaluation contract

  • pydantic_schema_valid · Weight 1
  • audit_accuracy · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

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