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Challenge

Mastra AI Agricultural Risk Assessment Workflow with Llama 3.3 70B

Agritech platforms providing credit to smallholder farmers require real-time satellite visual index analysis combined with localized meteorological data. Build an agricultural credit underwriting workflow using Mastra AI and Llama 3.3 70B to generate field-level risk profiles.

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Challenge brief

What you are building

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

Build a multi-source agritech credit scoring agent using Mastra AI framework and Llama 3.3 70B.

Delivery guide

How work is evaluated

Evaluation

Evaluates agricultural risk classification and yield model accuracy.

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

correct_risk_category

Ensures risk output matches benchmark threshold.

Binary check

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

Dimension 2

scoring_precision

Precision of loan qualification criteria • 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

  • Orchestrate Mastra AI workflows for multi-step agritech data ingestion

  • Utilize Llama 3.3 70B for synthesis of unstructured weather and crop yields

  • Output standardized agronomic risk scores compliant with priority sector lending guidelines

Resources and assets

Reference links and supporting material

Dataset notes

Agronomic data containing NDVI indices, historical rainfall, and crop yield records.

Challenge setup

Evaluates agricultural risk classification and yield model accuracy.

Challenge input

JSON farm assessment payload

Mastra AI

Mandatory framework

Llama 3.3 70B

Assigned model

Evaluated output

JSON crop risk profile

Evaluation checks
  • Ensures risk output matches benchmark threshold.
  • Precision of loan qualification criteria • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: Agri-Credit-Bench
  • Scoring Precision target: 0.95
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript 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
  • Mastra AI · Required
  • Llama 3.3 70B · Optional
  • Exa · Optional
Orchestration
  • Mastra AI · Required

Evaluation contract

  • correct_risk_category · Weight 1
  • scoring_precision · Weight 1

Metrics

  • scoring_precision · Target 0.95

Benchmarks

  • Agri-Credit-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON farm assessment payload

    Output: JSON crop risk profile

    Execution harness: JavaScript

Evaluation tools

  • Mastra AI · Agent Framework
  • Llama 3.3 70B · Large Language Model

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Mastra AI Agricultural Risk Assessment Workflow with Llama 3.3 70B