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Challenge

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.

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Challenge brief

What you are building

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

Build a collaborative CrewAI multi-agent workforce leveraging GitHub Copilot tooling to evaluate global reinsurance treaty exposure and portfolio accumulation.

Delivery guide

How work is evaluated

Evaluation

Evaluates multi-agent CrewAI output for reinsurance treaty concentration accuracy and exposure limit compliance.

Datasets

Shared data for this challenge

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

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

check_exposure_math

Accumulated exposure must equal sum of individual limits.

Binary check

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

Dimension 2

accuracy_score

Accuracy in identifying treaty breach conditions. • target: 0.95 • 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

  • 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 and assets

Reference links and supporting material

Dataset notes

Synthetic dataset of 100 global reinsurance treaties modeling ceding limits, retentions, and hazard triggers across top tier global reinsurers.

How this agent runs

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)

Checks for
  • Accumulated exposure must equal sum of individual limits.
  • Accuracy in identifying treaty breach conditions. • target: 0.95 • range: 0-1
Proof of success
  • Benchmark: ReinsuranceAgentBench-2026
  • Accuracy Score target: 0.95
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • GitHub Copilot · Required
  • GitHub · Optional
  • CrewAI · Optional
Observation
  • GitHub Copilot · Required

Evaluation contract

  • check_exposure_math · Weight 1
  • accuracy_score · Weight 1

Recipe state

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

Frequently Asked Questions about Multi-Agent Reinsurance Portfolio Treaty Analysis with CrewAI and GitHub Copilot