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Challenge

Build a SEC Regulatory Compliance Auditor with AutoGen and Adept

Proposed SEC regulations on crypto assets require continuous auditing of token transaction logs and compliance disclosures. Construct a multi-agent auditing system using AutoGen and Adept to automatically scan ledger transactions and flag regulatory non-compliance.

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Challenge brief

What you are building

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

Build an AutoGen multi-agent system combined with Adept browser automation to audit crypto exchange regulatory compliance reports.

Delivery guide

How work is evaluated

Evaluation

Evaluates multi-agent compliance verification accuracy against sample crypto custody logs.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

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

test_violation_detection

Verify commingling detected correctly as non-compliant

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 regulatory non-compliance detection • target: 0.96 • range: 0.9-1

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

  • Design an AutoGen multi-agent team (Auditor, Legal Examiner, Reporter).

  • Use Adept actions to navigate regulatory filing portals and verify ledger state.

  • Generate automated SEC disclosure compliance audit reports.

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic cryptocurrency exchange transaction records and custody balance snapshots.

Challenge setup

Evaluates multi-agent compliance verification accuracy against sample crypto custody logs.

Challenge input

JSON array of daily custody balances and wallet addresses

AutoGen

Multi-agent framework requirement

Adept

AI workflow automation requirement

Evaluated output

JSON object with compliance_status, violations, and summary

Evaluation checks
  • Verify commingling detected correctly as non-compliant
  • Accuracy of regulatory non-compliance detection • target: 0.96 • range: 0.9-1
Configured evaluation evidence
  • Benchmark: Compliance Agent Benchmark v1
  • Audit Accuracy target: 0.96
  • 1 public reference case
Configured runtime
  • 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

Action Space
  • AutoGen · Required
  • Adept · Optional
  • Microsoft AutoGen · Optional

Evaluation contract

  • test_violation_detection · Weight 1
  • audit_accuracy · Weight 1

Metrics

  • audit_accuracy · Target 0.96

Benchmarks

  • Compliance Agent Benchmark v1
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON array of daily custody balances and wallet addresses

    Output: JSON object with compliance_status, violations, and summary

    Execution harness: Python

Evaluation tools

  • AutoGen · agent_framework
  • Adept · workflow_automation

Recipe freeze marker

No recipe freeze time is recorded.

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