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Challenge

Build a Marine Tourism Footprint Analyst using OpenAI Agents SDK

Balancing Oman's expanding marine tourism projects with coastal ecosystem preservation requires rapid environmental impact assessment. Build an autonomous agent with OpenAI Agents SDK to analyze marine vessel traffic, evaluate reef risk indices, and generate sustainable mooring plans.

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Challenge brief

What you are building

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

Deploy an OpenAI Agents SDK workflow to calculate coastal sensitivity indices and optimize marine vessel mooring assignments in Oman.

Delivery guide

How work is evaluated

Evaluation

Evaluates calculation accuracy for coastal coral reef proximity and vessel capacity allocation.

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

capacity_exceeded_check

Ensure overflow status is correctly assigned

Binary check

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

Dimension 2

spatial_precision

Precision of reef buffer proximity calculations • target: 0.98 • range: 0.9-1

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

  • Process marine vessel AIS location telemetry and coastal reef proximity datasets

  • Implement OpenAI Agents SDK tools for calculating coral sensitivity scores

  • Automate generation of mooring zone recommendations to mitigate ecosystem damage

  • Output standardized environmental assessment reports for tourism ministries

Resources and assets

Reference links and supporting material

Dataset notes

Vessel tracking logs, coral reef map vectors, and visitor capacity metrics along the Musandam and Daymaniyat coasts.

Challenge setup

Evaluates calculation accuracy for coastal coral reef proximity and vessel capacity allocation.

Challenge input

JSON containing vessel density and reef distance

OpenAI Agents SDK

Provides tool integration capabilities for geospatial calculation

Evaluated output

JSON capacity allocation

Evaluation checks
  • Ensure overflow status is correctly assigned
  • Precision of reef buffer proximity calculations • target: 0.98 • range: 0.9-1
Configured evaluation evidence
  • Benchmark: EcoGISBench
  • Spatial Precision target: 0.98
  • 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
  • OpenAI · Required
  • Alibi Detect · Optional
  • Voiceflow · Optional

Evaluation contract

  • capacity_exceeded_check · Weight 1
  • spatial_precision · Weight 1

Metrics

  • spatial_precision · Target 0.98

Benchmarks

  • EcoGISBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing vessel density and reef distance

    Output: JSON capacity allocation

    Execution harness: Python

Evaluation tools

  • OpenAI Agents SDK · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

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