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Challenge

Low-Latency MSME Credit Underwriting Agent with OpenAI Agents SDK and Cerebras Inference

Rapid loan underwriting for small businesses requires ultra-fast processing of GST filings and banking records. Build an automated underwriting decision agent using the OpenAI Agents SDK powered by Cerebras Inference runtime to perform instant risk assessment and credit limit calculation for MSME applicants.

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Challenge brief

What you are building

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

Develop an ultra-fast MSME credit underwriting pipeline using the OpenAI Agents SDK powered by Cerebras ultra-low-latency inference.

Delivery guide

How work is evaluated

Evaluation

Evaluates decision precision and sub-second execution latency of underwriting agent.

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

limit_within_turnover_bounds

Ensures approved limit does not exceed 25% of annual turnover.

Binary check

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

Dimension 2

underwriting_decision_accuracy

Accuracy of risk tier bucket classification. • target: 0.95 • 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

  • Implement autonomous agent loops using OpenAI Agents SDK.

  • Integrate Cerebras Inference provider for sub-second execution speeds.

  • Construct MSME risk models combining GST return validation and cashflow analysis.

Resources and assets

Reference links and supporting material

Dataset notes

MSME applicant profiles containing simulated GSTR returns, bank balance records, and credit bureau scores.

Challenge setup

Evaluates decision precision and sub-second execution latency of underwriting agent.

Challenge input

JSON containing annual_turnover_inr, average_monthly_balance_inr, and existing_debts_inr.

OpenAI Agents SDK

Standard agent structure for autonomous tool execution

Cerebras Inference

Ultra-fast LLM inference runtime for instant credit scoring

Evaluated output

JSON containing decision, approved_limit_inr, and dscr_score.

Evaluation checks
  • Ensures approved limit does not exceed 25% of annual turnover.
  • Accuracy of risk tier bucket classification. • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: MSME Underwriting Speed Benchmark
  • Underwriting Decision Accuracy target: 0.95
  • 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
  • OpenAI · Optional
  • ElevenLabs · Optional
Policy Serving
  • Cerebras Inference · Required

Evaluation contract

  • limit_within_turnover_bounds · Weight 1
  • underwriting_decision_accuracy · Weight 1

Metrics

  • underwriting_decision_accuracy · Target 0.95

Benchmarks

  • MSME Underwriting Speed Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing annual_turnover_inr, average_monthly_balance_inr, and existing_debts_inr.

    Output: JSON containing decision, approved_limit_inr, and dscr_score.

    Execution harness: Python

Evaluation tools

  • OpenAI Agents SDK · agent_framework
  • Cerebras Inference · inference_and_model_runtime

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Low-Latency MSME Credit Underwriting Agent with OpenAI Agents SDK and Cerebras Inference