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Challenge

AutoGen Casualty ILS: Multi-Agent Tail Risk Structuring System

Structuring casualty insurance-linked securities requires collaborative analysis of long-tail risk distributions and trigger conditions across counterparty agents. Build an AutoGen multi-agent conversational framework that models loss distributions and achieves accurate secondary market yield predictions within 5% error.

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Challenge brief

What you are building

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

Build an AutoGen multi-agent system comprising Risk Modeler, Structurer, and Investor agents to design casualty ILS instruments.

Delivery guide

How work is evaluated

Evaluation

Evaluates multi-agent conversation termination and output yield pricing error against ground truth actuary benchmarks.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

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

market_clearing_pass

Validates that agents reached consensus on bond structure.

Binary check

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

Dimension 2

yield_prediction_error_pct

Percentage error between calculated yield and ground truth benchmark. • target: 0.04 • 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

  • Set up an AutoGen multi-agent group chat environment.

  • Assign specialist roles (ActuaryAgent, StructuringAgent, RatingAgencyAgent).

  • Simulate negotiation of coupon yield based on long-tail casualty loss distributions.

  • Produce final bond terms with target yield prediction error under 5%.

Resources and assets

Reference links and supporting material

Dataset notes

Dataset of 40 historical and synthetic casualty ILS issuance parameters and pricing outcomes.

Challenge setup

Evaluates multi-agent conversation termination and output yield pricing error against ground truth actuary benchmarks.

Challenge input

JSON containing expected_loss_pct, attachment_point, tail_factor

AutoGen

Microsoft framework for orchestrating conversational agents.

Evaluated output

JSON containing agreed_coupon_bps, rating_grade, market_clearing_status

Evaluation checks
  • Validates that agents reached consensus on bond structure.
  • Percentage error between calculated yield and ground truth benchmark. • target: 0.04 • range: 0-1
Configured evaluation evidence
  • Benchmark: Multi-Agent Consensus Benchmark
  • Yield Prediction Error Pct target: 0.04
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • AutoGen · Required
  • Novita AI · Optional
  • Lingo · Optional

Evaluation contract

  • market_clearing_pass · Weight 1
  • yield_prediction_error_pct · Weight 1

Metrics

  • yield_prediction_error_pct · Target 0.04

Benchmarks

  • Multi-Agent Consensus Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing expected_loss_pct, attachment_point, tail_factor

    Output: JSON containing agreed_coupon_bps, rating_grade, market_clearing_status

    Execution harness: Python

Evaluation tools

  • AutoGen · Multi-Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

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