Multi-Agent VASP Licensing Risk Auditing with AutoGen and Oracle OCI GenAI
Regulators and compliance teams require robust multi-agent dialogue systems when auditing Virtual Asset Service Providers (VASPs). Build an AutoGen multi-agent system powered by Oracle OCI Generative AI to evaluate VASP operational compliance, custody security, and anti-money laundering controls.
What you are building
The core problem, expected build, and operating context for this challenge.
Orchestrate an AutoGen multi-agent system backed by Oracle OCI Generative AI to perform automated risk assessments for virtual asset service providers.
How work is evaluated
Evaluates multi-agent conversation outcomes in classifying VASP risk profiles and wallet custody parameters.
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.
travel_rule_rejection
Verifies application is rejected or flagged conditional if Travel Rule is missing
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
audit_thoroughness
Percentage of key compliance risks accurately identified in summary • target: 0.9 • 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
Orchestrate conversational multi-agent systems using AutoGen framework
Connect AutoGen agents to Oracle OCI Generative AI inference endpoints
Evaluate VASP licensing applications against MAS Payment Services Act rules
Incorporate human-in-the-loop sign-off steps for risk exception approvals
Reference links and supporting material
Synthetic licensing filings for Singapore Virtual Asset Service Providers.
Challenge setup
Evaluates multi-agent conversation outcomes in classifying VASP risk profiles and wallet custody parameters.
Challenge input
JSON object with vasp_name, cold_wallet_ratio, travel_rule_solution, reserve_audit_frequency
AutoGen
Enables multi-turn conversational debate between specialized agents
Oracle OCI Generative AI
Provides enterprise hosted LLM backends
Evaluated output
JSON object with licensing_recommendation, key_risks, and required_remediations
- Verifies application is rejected or flagged conditional if Travel Rule is missing
- Percentage of key compliance risks accurately identified in summary • target: 0.9 • range: 0-1
- Benchmark: VASP-Audit-Bench
- Audit Thoroughness target: 0.9
- 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
- AutoGen · Required
- Oracle OCI Generative AI · Optional
- Dora · Optional
Evaluation contract
- travel_rule_rejection · Weight 1
- audit_thoroughness · Weight 1
Metrics
- audit_thoroughness · Target 0.9
Benchmarks
- VASP-Audit-Bench
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object with vasp_name, cold_wallet_ratio, travel_rule_solution, reserve_audit_frequency
Output: JSON object with licensing_recommendation, key_risks, and required_remediations
Execution harness: Python
Evaluation tools
- AutoGen · Multi-Agent Framework
- Oracle OCI Generative AI · Inference Platform
Recipe freeze marker
No recipe freeze time is recorded.