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Challenge

Deploy MSME Credit Assessment Agents with Microsoft AutoGen

Evaluating micro and small enterprises (MSMEs) for lending requires cross-referencing GST filing compliance, banking velocity, and promoter disclosures. Build a multi-agent debate and consensus framework using Microsoft AutoGen where Risk Analyst and Business Evaluator agents perform peer assessment on credit applications. Achieve a 91% alignment with expert credit committee decisions.

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Challenge brief

What you are building

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

Construct a multi-agent conversational consensus system using Microsoft AutoGen to evaluate MSME credit eligibility from GST and bank data.

Delivery guide

How work is evaluated

Evaluation

Evaluates AutoGen multi-agent consensus decisions against human credit committee labels.

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

consensus_validity

Ensures recommendation is explicitly APPROVE or REJECT

Binary check

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

Dimension 2

committee_alignment

Accuracy relative to expert credit committee benchmarks • target: 0.91 • range: 0-1

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

  • Configure multi-agent multi-turn group chats using Microsoft AutoGen

  • Establish distinct risk personas (Conservative Risk Officer vs Growth Evaluator)

  • Process GST tax filings alongside Account Aggregator bank cashflows

  • Generate unified, policy-compliant credit assessment reports through agent consensus

Resources and assets

Reference links and supporting material

Dataset notes

GST turnover metrics, Account Aggregator monthly summaries, and loan performance history.

Challenge setup

Evaluates AutoGen multi-agent consensus decisions against human credit committee labels.

Challenge input

JSON object with GST turnover, AA cashflows, and loan request

AutoGen

Enables multi-agent conversational consensus and role-playing execution.

Evaluated output

JSON object with final consensus recommendation and approved limit

Evaluation checks
  • Ensures recommendation is explicitly APPROVE or REJECT
  • Accuracy relative to expert credit committee benchmarks • target: 0.91 • range: 0-1
Configured evaluation evidence
  • Benchmark: MSME Credit Consensus Benchmark
  • Committee Alignment target: 0.91
  • 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
  • Microsoft AutoGen · Optional
  • Aim · Optional

Evaluation contract

  • consensus_validity · Weight 1
  • committee_alignment · Weight 1

Metrics

  • committee_alignment · Target 0.91

Benchmarks

  • MSME Credit Consensus Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with GST turnover, AA cashflows, and loan request

    Output: JSON object with final consensus recommendation and approved limit

    Execution harness: Python

Evaluation tools

  • AutoGen · Multi-Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Deploy MSME Credit Assessment Agents with Microsoft AutoGen