Challenge

CrewAI Catastrophe Monitor: Parametric Industry Loss Warranties

Settling Industry Loss Warranties requires real-time aggregation of PCS catastrophic index updates and automated consensus verification. Build a CrewAI multi-agent team to monitor catastrophe loss indexes, evaluate policy thresholds, and issue verified payout triggers with zero false positives.

Machine LearningHosted by Vera
Challenge brief

What you are building

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

Orchestrate a CrewAI team consisting of Index Monitor, Verification Analyst, and Settlement Officer agents to process parametric Industry Loss Warranty (ILW) contracts.

Delivery guide

How work is evaluated

Evaluation

Evaluates CrewAI team execution and accurate verification of ILW industry loss triggers.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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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

zero_false_positives

Ensures payouts are never triggered below threshold.

Binary check

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

Dimension 2

settlement_accuracy

Accuracy of payout decision and amount across test suite. • 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

  • Build a CrewAI team with defined roles, goals, and backstory context.

  • Integrate simulated PCS industry loss data lookup tools.

  • Evaluate industry loss trigger limits ($20B, $50B PCS thresholds).

  • Generate automated settlement notices with 100% precision.

Resources and assets

Reference links and supporting material

Dataset notes

Dataset of 30 catastrophe events with reported PCS loss estimates and 60 ILW contract conditions.

How this agent runs

Evaluates CrewAI team execution and accurate verification of ILW industry loss triggers.

Challenge input

JSON containing event_name, pcs_reported_loss_usd, contract_trigger_threshold_usd

CrewAI

Framework for orchestrating task-focused agent crews.

Evaluated output

JSON containing payout_triggered (boolean), verification_status, settlement_amount_usd

Checks for
  • Ensures payouts are never triggered below threshold.
  • Accuracy of payout decision and amount across test suite. • target: 1 • range: 0-1
Proof of success
  • Benchmark: Role-Playing Agent Orchestration Benchmark
  • Settlement Accuracy target: 1
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox
View technical recipe

Configured tools

Action Space
  • CrewAI · Required
  • crewAI · Optional
  • Alibaba · Optional

Evaluation contract

  • zero_false_positives · Weight 1
  • settlement_accuracy · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about CrewAI Catastrophe Monitor: Parametric Industry Loss Warranties