Cross-Platform Hyperscaler AI Investment Strategist
This challenge involves building a Hyperscaler Investment Strategist (in emerging markets), Agent to Agent protocol-enabled multi-agent system using OpenAI Swarm for tracking investments and rationale. The system will leverage Claude Opus 4.5's advanced reasoning to analyze investment opportunities across simulated disparate cloud environments and data sources, generating strategic recommendations for optimal resource allocation and market penetration in emerging AI markets. The agents must communicate securely and effectively to synthesize intelligence from diverse information silos.
What you are building
Develop an A2A protocol multi-agent system orchestrated by OpenAI Swarm, utilizing Claude Opus 4.5 for strategic AI investment analysis across simulated cloud platforms.
How work is evaluated
The evaluation focuses on the multi-agent system's ability to synthesize disparate data, generate coherent strategic investment recommendations, and demonstrate robust A2A communication. Performance will be measured by the quality of strategic output and the efficiency of agent collaboration.
Datasets
Public datasets, plus the private datasets you have access to.
Learning goals
Design RAG systems for cross-platform data synthesis.
Orchestrate multi-agent workflows using OpenAI Swarm across simulated platforms.
Implement A2A protocol for secure, asynchronous agent communication.
Leverage Claude Opus 4.1 for extended strategic reasoning on complex investment data.
Resources
Sample data for 1 tasks
Challenge setup
The evaluation focuses on the multi-agent system's ability to synthesize disparate data, generate coherent strategic investment recommendations, and demonstrate robust A2A communication. Performance will be measured b...
Challenge input
{'aws_investment_data': 'json', 'azure_investment_data': 'json', 'gcp_investment_data': 'json', 'market_trends_report_url': 'str'}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'overall_strategy_summary': 'str', 'resource_allocation_plan': {'aws': 'float', 'azure': 'float', 'gcp': 'float'}, 'risk_mitigation_strategies': [...
- Verify that communication logs show successful A2A message exchanges between at least two distinct agent ty...
- Ensure the 'overall_strategy_summary' is a grammatically correct and logically coherent paragraph.
- StrategicRecommendationQuality target: 88
- Python execution harness
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Verify that communication logs show successful A2A message exchanges between at least two distinct agent types.
- Ensure the 'overall_strategy_summary' is a grammatically correct and logically coherent paragraph.
Metrics
- StrategicRecommendationQuality · Target 88
- CrossPlatformIntegrationScore · Target 85
- AgentCollaborationEfficiency · Target 60
Workflow tasks
- Task 1
Input: {'aws_investment_data': 'json', 'azure_investment_data': 'json', 'gcp_investment_data': 'json', 'market_trends_report_url': 'str'}
Output: {'overall_strategy_summary': 'str', 'resource_allocation_plan': {'aws': 'float', 'azure': 'float', 'gcp': 'float'}, 'risk_mitigation_strategies': ['str'], 'market_penetration_recommendations': ['str'], 'cross_platform_synergies': ['str']}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.