Multi-Agent Reinsurance Portfolio Treaty Analysis with CrewAI and GitHub Copilot
Global reinsurers manage massive exposure concentrations across primary carriers and catastrophe treaties. You will create a CrewAI agent team assisted by GitHub Copilot code generation to analyze capital reserves, catastrophe risk limits, and treaty terms across Munich Re and Lloyd's portfolios. The multi-agent crew must correctly identify portfolio treaty overlaps with 90%+ accuracy.
What you are building
Build a collaborative CrewAI multi-agent workforce leveraging GitHub Copilot tooling to evaluate global reinsurance treaty exposure and portfolio accumulation.
How work is evaluated
Evaluates multi-agent CrewAI output for reinsurance treaty concentration accuracy and exposure limit compliance.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
check_exposure_math
Accumulated exposure must equal sum of individual limits.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
accuracy_score
Accuracy in identifying treaty breach conditions. • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Configure role-playing agents in CrewAI for Treaty Analysis, Exposure Accumulation, and Capital Allocation.
Integrate custom financial aggregation tools assisted by GitHub Copilot boilerplate logic.
Execute multi-turn agent delegation to evaluate Lloyd's syndicate catastrophe commitments.
Generate actionable treaty optimization reports with auditable capital allocation metrics.
Resources
Synthetic dataset of 100 global reinsurance treaties modeling ceding limits, retentions, and hazard triggers across top tier global reinsurers.
Challenge setup
Evaluates multi-agent CrewAI output for reinsurance treaty concentration accuracy and exposure limit compliance.
Challenge input
JSON array of treaties containing reinsurer_name, ceding_carrier, limit_usd, retention_usd, hazard_type
CrewAI
Orchestrates multi-agent role-playing workflows for complex reinsurance portfolio reviews.
GitHub Copilot
Accelerates creation of custom domain tools and mathematical helper utilities.
Evaluated output
JSON object with accumulated_exposure, limit_breaches (ARRAY), and recommended_hedges (ARRAY)
- Accumulated exposure must equal sum of individual limits.
- Accuracy in identifying treaty breach conditions. • target: 0.95 • range: 0-1
- Benchmark: ReinsuranceAgentBench-2026
- Accuracy Score target: 0.95
- 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
- GitHub Copilot · Required
- GitHub · Optional
- CrewAI · Optional
- GitHub Copilot · Required
Evaluation contract
- check_exposure_math · Weight 1
- accuracy_score · Weight 1
Metrics
- accuracy_score · Target 0.95
Benchmarks
- ReinsuranceAgentBench-2026
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON array of treaties containing reinsurer_name, ceding_carrier, limit_usd, retention_usd, hazard_type
Output: JSON object with accumulated_exposure, limit_breaches (ARRAY), and recommended_hedges (ARRAY)
Execution harness: Python
Evaluation tools
- CrewAI · Multi-Agent Framework
- GitHub Copilot · AI Code Assistant
Recipe freeze marker
No recipe freeze time is recorded.