Automated Sustainable Rubber Commodity Traceability & EUDR Compliance Agent
European Union Deforestation Regulation (EUDR) mandates strict geolocation tracking and deforestation-free proof for rubber imported from Malaysia and Southeast Asia. Commodity traders must trace every batch of raw latex back to precise smallholder plot GPS coordinates. In this challenge, you will orchestrate a multi-agent EUDR compliance verification crew using CrewAI. The crew consists of a Geolocation Inspector Agent, a Satellite Plot Auditor Agent, and a Due Diligence Statement (DDS) Generator Agent.
What you are building
The core problem, expected build, and operating context for this challenge.
Orchestrate a multi-agent rubber traceability and EUDR compliance verification workflow using CrewAI.
How work is evaluated
Evaluates GeoJSON plot verification accuracy and compliant DDS output generation.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
dds_compliance_check
Must output COMPLIANT for non-deforested plot coordinates.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
audit_execution_speed_s
Seconds taken to audit plot polygon and construct DDS • target: 4.2 • range: 0.5-30
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Construct a CrewAI multi-agent team for supply chain due diligence verification
Verify polygon plot GPS coordinates against historical deforestation satellite boundaries
Generate compliant EU Due Diligence Statements (DDS) in structured format
Implement inter-agent verification loops for land title authenticity check
Reference links and supporting material
Malaysian smallholder rubber plot GeoJSON polygons, land title registries, and synthetic satellite forest cover logs.
Challenge setup
Evaluates GeoJSON plot verification accuracy and compliant DDS output generation.
Challenge input
JSON containing rubber batch ID, supplier details, and GeoJSON polygon coordinates
CrewAI
Required framework for multi-agent due diligence workflow orchestration.
Evaluated output
JSON containing compliance_status, deforestation_risk_level, and DDS reference code
- Must output COMPLIANT for non-deforested plot coordinates.
- Seconds taken to audit plot polygon and construct DDS • target: 4.2 • range: 0.5-30
- Benchmark: GeoCompliance-Bench
- Audit Execution Speed S target: 4.2
- 1 public reference case
- 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
- Metaflow · Optional
- CrewAI · Required
- crewAI · Optional
Evaluation contract
- dds_compliance_check · Weight 1
- audit_execution_speed_s · Weight 1
Metrics
- audit_execution_speed_s · Target 4.2
Benchmarks
- GeoCompliance-Bench
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing rubber batch ID, supplier details, and GeoJSON polygon coordinates
Output: JSON containing compliance_status, deforestation_risk_level, and DDS reference code
Execution harness: Python
Evaluation tools
- CrewAI · Multi-Agent Framework
Recipe freeze marker
No recipe freeze time is recorded.