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Challenge

Gulf Logistics Corridor Exception Multi-Agent System using CrewAI

Geopolitical tensions require real-time rerouting of trade corridors across Gulf ports and pipelines. Build a multi-agent simulation using CrewAI with specialized Logistics, Customs, and Security agents to orchestrate alternative maritime shipment routes and minimize transit delays.

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Challenge brief

What you are building

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

Orchestrate role-playing CrewAI agents collaborating to resolve port disruptions and rerun infrastructure routing along Gulf trade corridors.

Delivery guide

How work is evaluated

Evaluation

Evaluates multi-agent rerouting efficiency and alternative port selection given straight blockage events.

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

Bypass Route Selection Test

Confirms agents reroute around Strait blockage via Red Sea port.

Binary check

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

Dimension 2

Route Optimization Score

Minimization of delay hours relative to total blockade impact. • target: 18 • range: 0-100

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 (Route Analyst, Risk Officer, Port Dispatcher)

  • Orchestrate hierarchical agent task execution for port cargo rerouting

  • Integrate live vessel and pipeline constraint tools into CrewAI tasks

  • Evaluate rerouting recommendation efficiency and cost reduction

Resources and assets

Reference links and supporting material

Dataset notes

Simulated Gulf maritime transit bottlenecks, port capacity metrics, and regional oil pipeline connection schemas.

Challenge setup

Evaluates multi-agent rerouting efficiency and alternative port selection given straight blockage events.

Challenge input

JSON object with blocked_strait, cargo_type, origin_port, and destination_port.

CrewAI

Role-playing multi-agent system collaboration

Evaluated output

JSON object with recommended_route, alternate_port, and estimated_delay_hours.

Evaluation checks
  • Confirms agents reroute around Strait blockage via Red Sea port.
  • Minimization of delay hours relative to total blockade impact. • target: 18 • range: 0-100
Configured evaluation evidence
  • Benchmark: GCC Logistics Corridor Benchmark
  • Route Optimization Score target: 18
  • 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
  • Zed · Optional

Evaluation contract

  • Bypass Route Selection Test · Weight 1
  • Route Optimization Score · Weight 1

Metrics

  • Route Optimization Score · Target 18

Benchmarks

  • GCC Logistics Corridor Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with blocked_strait, cargo_type, origin_port, and destination_port.

    Output: JSON object with recommended_route, alternate_port, and estimated_delay_hours.

    Execution harness: Python

Evaluation tools

  • CrewAI · Agent Orchestration

Recipe freeze marker

No recipe freeze time is recorded.

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