Geospatial Wildfire & Flood Resilience Trigger Agent with OpenAI Agents SDK
Climate physical risk modeling requires real-time agentic evaluation of asset resilience investments against severe weather triggers. You will construct an autonomous resilience evaluation agent using the OpenAI Agents SDK to analyze satellite flood inundation maps and property wildfire mitigation features. The agent must yield an optimal resilience ROI allocation with an MAE under 2.5%.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an autonomous climate resilience agent using the OpenAI Agents SDK to optimize property flood and wildfire mitigation investments.
How work is evaluated
Evaluates climate resilience optimization accuracy across 100 high-risk coastal and wildfire zone properties.
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.
valid_loss_reduction
Expected loss reduction must be between 0% and 100%.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
roi_mae
Mean Absolute Error of expected loss reduction percentage relative to ground truth physics model. • target: 2.5 • range: 0-10
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Initialize OpenAI Agents SDK agent using GPT-4o with custom function calling capabilities.
Connect agent to geospatial vector processing tools for flood and wildfire hazard layers.
Implement multi-turn function calling to evaluate property resilience ROI.
Generate auditable resilience investment proposals for risk engineering teams.
Reference links and supporting material
Dataset of 150 residential and commercial property profiles with geospatial hazard scores and structural mitigation cost tables.
Challenge setup
Evaluates climate resilience optimization accuracy across 100 high-risk coastal and wildfire zone properties.
Challenge input
JSON containing property_id, flood_zone, defensible_space_feet, budget_usd
OpenAI Agents SDK
Provides function calling loops and multi-turn conversation tools for GPT-4o.
Evaluated output
JSON containing recommended_investments (ARRAY), expected_loss_reduction_percent, net_roi_multiplier
- Expected loss reduction must be between 0% and 100%.
- Mean Absolute Error of expected loss reduction percentage relative to ground truth physics model. • target:...
- Benchmark: ClimateResilienceBench-2026
- Roi Mae target: 2.5
- 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.
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Configured tools
- OpenAI · Required
- Milvus · Optional
- Qwen 3 · Optional
Evaluation contract
- valid_loss_reduction · Weight 1
- roi_mae · Weight 1
Metrics
- roi_mae · Target 2.5
Benchmarks
- ClimateResilienceBench-2026
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing property_id, flood_zone, defensible_space_feet, budget_usd
Output: JSON containing recommended_investments (ARRAY), expected_loss_reduction_percent, net_roi_multiplier
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · Autonomous Agent Framework
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