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Challenge

Build a CrewAI Agritech Advisory & Subsidy Verification Crew

Smallholder farmers require real-time agricultural advisories paired with digital subsidy validation across government agritech portals. Build a multi-agent CrewAI system that verifies soil data, assesses crop health, and cross-references farmer welfare schemes with 90% recommendation accuracy.

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Challenge brief

What you are building

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

Construct a collaborative multi-agent CrewAI system combining agronomist and subsidy compliance specialist roles for smallholder farmers.

Delivery guide

How work is evaluated

Evaluation

Evaluates multi-agent task execution and crop recommendation correctness.

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

recommendation_check

Verifies valid advisory output

Binary check

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

Dimension 2

accuracy_score

Agronomist recommendation correctness • target: 0.92 • 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

  • Define specialized Agent roles in CrewAI for Agronomist, Scheme Auditor, and Soil Specialist

  • Orchestrate sequential and hierarchical Tasks in CrewAI workflows

  • Extract land parcel data and verify crop subsidy eligibility requirements

  • Output structured agritech advisory cards for farmer mobile applications

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic dataset containing regional soil sensor readings and state agricultural subsidy rules.

Challenge setup

Evaluates multi-agent task execution and crop recommendation correctness.

Challenge input

JSON soil test data and land location

CrewAI

Role-playing agent orchestration framework

Evaluated output

JSON advisory recommendation with crop_choice and subsidy_eligible

Evaluation checks
  • Verifies valid advisory output
  • Agronomist recommendation correctness • target: 0.92 • range: 0-1
Configured evaluation evidence
  • Benchmark: Agritech Multi-Agent Benchmark
  • Accuracy Score target: 0.92
  • 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
  • CrewAI · Required
  • crewAI · Optional
  • Kore.ai · Optional

Evaluation contract

  • recommendation_check · Weight 1
  • accuracy_score · Weight 1

Metrics

  • accuracy_score · Target 0.92

Benchmarks

  • Agritech Multi-Agent Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON soil test data and land location

    Output: JSON advisory recommendation with crop_choice and subsidy_eligible

    Execution harness: Python

Evaluation tools

  • CrewAI · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build a CrewAI Agritech Advisory & Subsidy Verification Crew