Multi-Agent for Real-time Sports Prediction Market Analysis
Inspired by the sudden rise in prediction markets adoption, this challenge focuses on building a sophisticated multi-agent system for real-time sports prediction market analysis. Your system will leverage Autogen to orchestrate a team of specialized agents that collectively monitor live sports data, news feeds, social media sentiment, and historical statistics to generate informed predictions for simulated events. This requires robust agent-to-agent communication via A2A protocol, allowing agents to share findings, challenge assumptions, and collaboratively refine their models. Claude Opus 4.1 will be the central reasoning engine for its nuanced understanding and complex data synthesis capabilities. The system must adapt its reasoning based on data volatility and event urgency, demonstrating cutting-edge adaptive AI for high-stakes, real-time decision-making in a dynamic environment.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a multi-agent system using AutoGen with Claude Opus 4.1 for real-time sports prediction market analysis, utilizing A2A protocol and adaptive reasoning.
How work is evaluated
The evaluation will focus on the multi-agent system's ability to accurately analyze real-time sports data, generate plausible predictions with supporting explanations, and demonstrate effective, adaptive agent collaboration in a dynamic environment.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Implement A2A protocol for seamless agent-to-agent communication.
Utilize Claude Opus 4.1 for sophisticated reasoning and sentiment analysis.
Build adaptive reasoning mechanisms for dynamic prediction generation.
Master AutoGen for orchestrating conversational multi-agent systems.
Develop real-time data processing pipelines for sports events.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The evaluation will focus on the multi-agent system's ability to accurately analyze real-time sports data, generate plausible predictions with supporting explanations, and demonstrate effective, adaptive agent collabo...
Challenge input
{'game_id': 'string', 'sport': 'string', 'event_stream': [{'timestamp': 'float', 'data': 'json'}]}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'prediction_timestamp': 'float', 'predicted_outcome': 'string', 'confidence_score': 'float', 'reasoning': 'string', 'relevant_data_points': ['stri...
- Ensures the Live Game Prediction output includes all required fields and correct data types.
- Verifies the Market Sentiment Analysis output contains overall sentiment, breakdown, and key factors.
- PredictionAccuracy target: 0.8
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
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Evaluation contract
- Ensures the Live Game Prediction output includes all required fields and correct data types.
- Verifies the Market Sentiment Analysis output contains overall sentiment, breakdown, and key factors.
Metrics
- PredictionAccuracy · Target 0.8
- ReasoningQuality · Target 8
- AdaptiveReasoningEffectiveness · Target 8
Workflow tasks
- Task 1
Input: {'game_id': 'string', 'sport': 'string', 'event_stream': [{'timestamp': 'float', 'data': 'json'}]}
Output: {'prediction_timestamp': 'float', 'predicted_outcome': 'string', 'confidence_score': 'float', 'reasoning': 'string', 'relevant_data_points': ['string']}
Execution harness: Python
- Task 2
Input: {'event_id': 'string', 'news_stream': [{'timestamp': 'float', 'text': 'string', 'source': 'string'}]}
Output: {'analysis_timestamp': 'float', 'overall_sentiment': 'positive|negative|neutral', 'sentiment_breakdown': {'team_a': 'string', 'team_b': 'string'}, 'key_factors': ['string'], 'impact_on_odds': 'string'}
Execution harness: Python
Recipe freeze marker
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