Multi-Agent Cyber Defense Mesh for Reinsurance using AutoGen
Rapidly evolving AI cyber exploits require automated multi-agent defense coordination across enterprise reinsurance networks. You will build a multi-agent conversational mesh using AutoGen where security agents collaborate to diagnose emerging zero-day vulnerabilities and adjust treaty exposure limits dynamically. Achieve a threat mitigation success rate exceeding 94% on simulated zero-day attacks.
What you are building
Construct an AutoGen multi-agent conversational system to automate reinsurance cyber threat defense and exposure adaptation.
How work is evaluated
Evaluate threat mitigation success rate and group chat convergence across zero-day attack scenarios.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
consensus_reached
Multi-agent group chat reached decision in under 10 conversational rounds.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
mitigation_success_rate
Percentage of simulated zero-day attacks successfully neutralized or mitigated • target: 0.94 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Orchestrate multi-agent conversational networks using AutoGen (AssistantAgent, UserProxyAgent)
Implement group chat managers to coordinate vulnerability diagnosis
Dynamically adjust reinsurance exposure limits based on live threat signals
Mitigate automated zero-day cyber attack propagation across insured portfolios
Resources
Synthetic cyber zero-day exploit scenarios, enterprise treaty portfolio records, and simulated attack progression timelines.
Challenge setup
Evaluate threat mitigation success rate and group chat convergence across zero-day attack scenarios.
Challenge input
JSON containing zero-day exploit signature and affected enterprise treaty portfolio
AutoGen
Multi-agent group chat conversations with automated code execution capabilities.
Evaluated output
JSON containing containment strategy, treaty sub-limit adjustments, and mitigation outcome
- Multi-agent group chat reached decision in under 10 conversational rounds.
- Percentage of simulated zero-day attacks successfully neutralized or mitigated • target: 0.94 • range: 0-1
- Benchmark: CyberDefenseMeshBench-2026
- Mitigation Success Rate target: 94%
- 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
- AutoGen · Required
- Azure OpenAI Service · Optional
- Hunyuan · Optional
Evaluation contract
- consensus_reached · Weight 1
- mitigation_success_rate · Weight 1
Metrics
- mitigation_success_rate · Target 0.94
Benchmarks
- CyberDefenseMeshBench-2026
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing zero-day exploit signature and affected enterprise treaty portfolio
Output: JSON containing containment strategy, treaty sub-limit adjustments, and mitigation outcome
Execution harness: Python
Evaluation tools
- AutoGen · Agent Framework
Recipe freeze marker
No recipe freeze time is recorded.