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.
What you are building
Orchestrate a CrewAI team consisting of Index Monitor, Verification Analyst, and Settlement Officer agents to process parametric Industry Loss Warranty (ILW) contracts.
How work is evaluated
Evaluates CrewAI team execution and accurate verification of ILW industry loss triggers.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
zero_false_positives
Ensures payouts are never triggered below threshold.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
settlement_accuracy
Accuracy of payout decision and amount across test suite. • target: 1 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
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
Dataset of 30 catastrophe events with reported PCS loss estimates and 60 ILW contract conditions.
Challenge setup
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
- Ensures payouts are never triggered below threshold.
- Accuracy of payout decision and amount across test suite. • target: 1 • range: 0-1
- Benchmark: Role-Playing Agent Orchestration Benchmark
- Settlement Accuracy target: 1
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
- CrewAI · Required
- crewAI · Optional
- Alibaba · Optional
Evaluation contract
- zero_false_positives · Weight 1
- settlement_accuracy · Weight 1
Metrics
- settlement_accuracy · Target 1
Benchmarks
- Role-Playing Agent Orchestration Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing event_name, pcs_reported_loss_usd, contract_trigger_threshold_usd
Output: JSON containing payout_triggered (boolean), verification_status, settlement_amount_usd
Execution harness: Python
Evaluation tools
- CrewAI · Role-Playing Agent Framework
Recipe freeze marker
No recipe freeze time is recorded.