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Challenge

Build a Stablecoin Settlement Compliance Agent with CrewAI and OpenAI o3

Cross-border stablecoin settlements require automated risk compliance, address sanction screening, and multi-currency liquidity validation. Build a CrewAI agentic workflow using OpenAI o3 to parse transaction events, verify compliance rules, and flag high-risk transfers in real time while reducing false positives below 5%.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Orchestrate multi-agent role-playing teams using CrewAI and OpenAI o3 reasoning to evaluate stablecoin ledger transactions against OFAC sanctions and velocity thresholds.

Delivery guide

How work is evaluated

Evaluation

Evaluates the agentic workflow on classifying real-time stablecoin transactions into compliant, flagged, or rejected categories.

Datasets

Shared data for this challenge

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

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

schema_validation_test

Ensures output conforms strictly to the expected decision schema

Binary check

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

Dimension 2

classification_accuracy

Percentage of test cases correctly assigned compliance decisions • target: 0.95 • 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

  • Construct a multi-agent CrewAI orchestration flow for cross-border stablecoin compliance

  • Integrate OpenAI o3 reasoning models for complex compliance rule validation

  • Implement real-time address risk scoring and sanction list matching

  • Establish automated audit logging and investigator triage queue generation

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic dataset of 500 stablecoin transaction payloads with annotated risk levels and sanction list correlations.

How this agent runs

Evaluates the agentic workflow on classifying real-time stablecoin transactions into compliant, flagged, or rejected categories.

Challenge input

JSON transaction event containing sender, recipient, amount, token, and chain metadata

CrewAI

Provides multi-agent task execution and role specialization.

OpenAI o3

Delivers superior logical chain-of-thought processing for complex rule evaluations.

Evaluated output

JSON containing risk_score, compliance_decision, matched_rules, and explanation

Checks for
  • Ensures output conforms strictly to the expected decision schema
  • Percentage of test cases correctly assigned compliance decisions • target: 0.95 • range: 0-1
Proof of success
  • Benchmark: Financial Crime Detection Benchmark v1
  • Classification Accuracy target: 0.95
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • CrewAI · Required
  • OpenAI · Optional
Policy Serving
  • o3 · Optional

Evaluation contract

  • schema_validation_test · Weight 1
  • classification_accuracy · Weight 1

Recipe state

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

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