Skip to main content
Back to challenges
Challenge

Build a Purdue Model Edge Synchronization Agent System with CrewAI

Enterprise digital transformation initiatives frequently stall due to uncoordinated data transfers across Purdue Model network layers. Build a multi-agent team using CrewAI to safely bridge Level 2 SCADA data to Level 4 enterprise lakes without exposing operational networks to security risks.

EngineeringHosted by Vera
Sign in free to start
Challenge brief

What you are building

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

Orchestrate collaborative CrewAI agents to handle cross-layer OT/IT data architecture design following Purdue Model rules.

Delivery guide

How work is evaluated

Evaluation

Evaluates CrewAI agent team output for compliant architecture design and gateway configs.

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

dmz_enforced

Verifies configuration routes through DMZ host without direct L2 to L4 connection.

Binary check

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

Dimension 2

compliance_score

Purdue Model security compliance score • target: 1 • 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 CrewAI roles: OT Data Engineer, IT Cloud Architect, and Cybersecurity Reviewer

  • Coordinate automated schema validation between MQTT Sparkplug B and cloud data lakes

  • Enforce DMZ conduit proxy requirements between Purdue Level 3 and Level 4

  • Generate verifiable Infrastructure-as-Code deployment scripts for edge gateways

Resources and assets

Reference links and supporting material

Dataset notes

Network hierarchy requirements mapping field sensors through industrial DMZs to enterprise clouds.

Challenge setup

Evaluates CrewAI agent team output for compliant architecture design and gateway configs.

Challenge input

JSON object with level2_nodes, level4_destination, security_policy

CrewAI

Framework for multi-agent role orchestration and collaborative tasks

Evaluated output

JSON object containing dynamic_proxy_config (string), architectural_approval (boolean)

Evaluation checks
  • Verifies configuration routes through DMZ host without direct L2 to L4 connection.
  • Purdue Model security compliance score • target: 1 • range: 0-1
Configured evaluation evidence
  • Benchmark: Purdue-DataArch-2025
  • Compliance Score target: 1
  • 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

Evaluation contract

  • dmz_enforced · Weight 1
  • compliance_score · Weight 1

Metrics

  • compliance_score · Target 1

Benchmarks

  • Purdue-DataArch-2025
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with level2_nodes, level4_destination, security_policy

    Output: JSON object containing dynamic_proxy_config (string), architectural_approval (boolean)

    Execution harness: Python

Evaluation tools

  • CrewAI · agent_framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build a Purdue Model Edge Synchronization Agent System with CrewAI