Simulate European Urban Heat Parametric Insurance with AutoGen and Together AI
Chronic heatwaves in European cities increase municipal public health expenditures and infrastructure downtime. Build a multi-agent simulation using AutoGen and Together AI to automate parametric payout triggers based on weather feed spikes. Achieve accurate automated payout triggers with zero false positives on baseline historical temperatures.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an AutoGen multi-agent system powered by Together AI fast inference to evaluate daily heat index metrics against parametric trigger thresholds.
How work is evaluated
Evaluates AutoGen agents on triggering parametric policy payouts under various heat event scenarios.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
zero_false_positives
No parametric triggers fired during non-extreme weather periods.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
trigger_f1_score
F1 score of triggering payouts when actual thresholds are exceeded • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build a multi-agent collaborative workflow using AutoGen framework.
Leverage Together AI endpoint for low-latency time-series evaluation.
Construct parametric trigger verification logic for urban heat indices.
Design an auditable decision logging module for reinsurance settlement.
Reference links and supporting material
Historical and projected ERA5 European heatwave climate data formatted for parametric trigger benchmarking.
How this agent runs
Evaluates AutoGen agents on triggering parametric policy payouts under various heat event scenarios.
Challenge input
JSON list of daily weather observations (temp, humidity, duration)
AutoGen
Enables multi-agent conversational consensus.
Together AI
Provides fast open-weights LLM execution.
Evaluated output
JSON detailing triggered events, calculation proof, and total payout
- No parametric triggers fired during non-extreme weather periods.
- F1 score of triggering payouts when actual thresholds are exceeded • target: 0.95 • range: 0-1
- Benchmark: ParametricTriggerBench
- Trigger F1 Score target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- RAI · Optional
- 01.AI · Optional
- Together AI · Required
- Together AI · Required
Evaluation contract
- zero_false_positives · Weight 1
- trigger_f1_score · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.