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.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct an AutoGen multi-agent system executing code safely inside an E2B cloud sandbox to compute Account Aggregator financial scores.
How work is evaluated
Evaluates multi-agent score accuracy and sandbox isolation security during financial statement evaluation.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
consent_check
Confirms valid consent verification before calculation
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
calculated_score
Underwriting credit risk score output • target: 780 • range: 300-900
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
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
Reference links and supporting material
Synthetic Account Aggregator cash flow payloads and financial statement metrics.
How this agent runs
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
- Confirms valid consent verification before calculation
- Underwriting credit risk score output • target: 780 • range: 300-900
- Benchmark: AA Financial Underwriting Benchmark
- Calculated Score target: 780
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- E2B · Optional
- AutoGen · Required
- E2B · Optional
- Arcee AI · Optional
Evaluation contract
- consent_check · Weight 1
- calculated_score · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.