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Develop an AutoGen Account Aggregator Credit Verification Agent in E2B

Financial service providers require isolated environments to evaluate sensitive user financial statements without exposing raw PII. Build a multi-agent conversational audit system using AutoGen inside an E2B sandbox environment to compute underwriting scores while maintaining 100% consent ledger compliance.

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What you are building

Construct an AutoGen multi-agent system executing code safely inside an E2B cloud sandbox to compute Account Aggregator financial scores.

How work is evaluated

Evaluation

Evaluates multi-agent score accuracy and sandbox isolation security during financial statement evaluation.

Datasets

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

How submissions are scored

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

consent_check

Confirms valid consent verification before calculation

Binary check

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

Dimension 2

calculated_score

Underwriting credit risk score output • target: 780 • range: 300-900

Binary check

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

Learning goals

  • Configure AutoGen UserProxyAgent and AssistantAgent pairs for financial data processing

  • Deploy E2B Code Interpreter micro-sandboxes for safe execution of financial metric scripts

  • Validate consent artifact tokens issued by Account Aggregator frameworks

  • Generate comprehensive debt-to-income and cash flow risk reports

Resources

Dataset notes

Synthetic Account Aggregator cash flow payloads and financial statement metrics.

Challenge setup

Evaluates multi-agent score accuracy and sandbox isolation security during financial statement evaluation.

Challenge input

JSON payload containing consent_token and statement data

AutoGen

Multi-agent conversation orchestration

E2B

Secure code execution in isolated environments

Evaluated output

JSON report containing credit_score and risk_tier

Evaluation checks
  • Confirms valid consent verification before calculation
  • Underwriting credit risk score output • target: 780 • range: 300-900
Configured evaluation evidence
  • Benchmark: AA Financial Underwriting Benchmark
  • Calculated Score target: 780
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Environment
  • E2B · Optional
Action Space
  • AutoGen · Required
  • E2B · Optional
  • Arcee AI · Optional

Evaluation contract

  • consent_check · Weight 1
  • calculated_score · Weight 1

Metrics

  • calculated_score · Target 780

Benchmarks

  • AA Financial Underwriting Benchmark
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON payload containing consent_token and statement data

    Output: JSON report containing credit_score and risk_tier

    Execution harness: Python

Evaluation tools

  • AutoGen · Agent Framework
  • E2B · Sandbox Execution

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Develop an AutoGen Account Aggregator Credit Verification Agent in E2B