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.
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.
How work is evaluated
Evaluates multi-agent conversation termination and output yield pricing error against ground truth actuary benchmarks.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
market_clearing_pass
Validates that agents reached consensus on bond structure.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
yield_prediction_error_pct
Percentage error between calculated yield and ground truth benchmark. • target: 0.04 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
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%.
Reference links and supporting material
Dataset of 40 historical and synthetic casualty ILS issuance parameters and pricing outcomes.
How this agent runs
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
- Validates that agents reached consensus on bond structure.
- Percentage error between calculated yield and ground truth benchmark. • target: 0.04 • range: 0-1
- Benchmark: Multi-Agent Consensus Benchmark
- Yield Prediction Error Pct target: 0.04
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- AutoGen · Required
- Novita AI · Optional
- Lingo · Optional
Evaluation contract
- market_clearing_pass · Weight 1
- yield_prediction_error_pct · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.