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%.
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.
How work is evaluated
Evaluates the agentic workflow on classifying real-time stablecoin transactions into compliant, flagged, or rejected categories.
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.
schema_validation_test
Ensures output conforms strictly to the expected decision schema
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
classification_accuracy
Percentage of test cases correctly assigned compliance decisions • target: 0.95 • 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
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
Reference links and supporting material
Synthetic dataset of 500 stablecoin transaction payloads with annotated risk levels and sanction list correlations.
Challenge setup
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
- Ensures output conforms strictly to the expected decision schema
- Percentage of test cases correctly assigned compliance decisions • target: 0.95 • range: 0-1
- Benchmark: Financial Crime Detection Benchmark v1
- Classification Accuracy target: 0.95
- 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.
View technical recipe
Configured tools
- CrewAI · Required
- OpenAI · Optional
- o3 · Optional
Evaluation contract
- schema_validation_test · Weight 1
- classification_accuracy · Weight 1
Metrics
- classification_accuracy · Target 0.95
Benchmarks
- Financial Crime Detection Benchmark v1
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON transaction event containing sender, recipient, amount, token, and chain metadata
Output: JSON containing risk_score, compliance_decision, matched_rules, and explanation
Execution harness: Python
Evaluation tools
- CrewAI · Agent Orchestration Framework
- OpenAI o3 · Reasoning LLM Engine
Recipe freeze marker
No recipe freeze time is recorded.