M&A Analyst Team for Acquisition Bid Analysis
For strategic M&A analysis, this challenge involves building a sophisticated multi-agent system capable of analyzing such high-stakes financial maneuvers. Participants will design, implement, and orchestrate a team of specialized agents using LangGraph to model a strategic advisory firm, leveraging GPT-5 for advanced reasoning and decision-making. This system will utilize an A2A protocol for seamless communication between agents, enabling complex collaborative workflows. Critical to its functionality will be MCP-enabled tool integration, allowing agents to access real-time financial data, news feeds, and market sentiment analysis platforms. The agents will employ extended thinking patterns with adaptive reasoning budgets to conduct in-depth due diligence, scenario planning, and risk assessment for a hostile takeover situation, culminating in comprehensive strategic recommendations.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a graph-based multi-agent system using LangGraph with GPT-5 Pro for strategic M&A analysis, incorporating A2A protocol and MCP tool integration.
How work is evaluated
The evaluation will assess the multi-agent system's ability to provide a comprehensive and accurate M&A strategic advisory memo. This includes the depth of analysis, accuracy of financial insights, identification of key risks, and quality of strategic recommendations, as well as the efficiency of agent collaboration and tool utilization.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Apply extended thinking with adaptive reasoning budgets for M&A scenario planning.
Orchestrate a team of role-based agents for comprehensive strategic insights.
Design MCP-enabled tool integration for real-time financial data and news feeds.
Build graph-based multi-agent workflows for complex financial analysis.
Implement A2A protocol for secure and structured agent-to-agent communication.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
The evaluation will assess the multi-agent system's ability to provide a comprehensive and accurate M&A strategic advisory memo. This includes the depth of analysis, accuracy of financial insights, identification of k...
Challenge input
{'news_articles': [{'title': '...', 'content': '...'}], 'company_profiles': {'Paramount': {'market_cap': '...', 'revenue': '...'}, 'WBD': {...}}}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
A JSON object containing: {'memo_title': '...', 'executive_summary': '...', 'market_analysis': '...', 'financial_impact': '...', 'key_risks': '...'...
- Ensure the output is a valid JSON adhering to the specified schema.
- Check if all critical sections (e.g., 'executive_summary', 'strategic_recommendations') are present and non...
- Factual Accuracy Score target: 0.9
- 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.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Ensure the output is a valid JSON adhering to the specified schema.
- Check if all critical sections (e.g., 'executive_summary', 'strategic_recommendations') are present and non-empty.
Metrics
- Factual Accuracy Score · Target 0.9
- Analysis Depth Score · Target 4
- Recommendation Relevance Score · Target 4
- Tool Utilization Efficiency · Target 0.8
Workflow tasks
- Task 1
Input: {'news_articles': [{'title': '...', 'content': '...'}], 'company_profiles': {'Paramount': {'market_cap': '...', 'revenue': '...'}, 'WBD': {...}}}
Output: A JSON object containing: {'memo_title': '...', 'executive_summary': '...', 'market_analysis': '...', 'financial_impact': '...', 'key_risks': '...', 'strategic_recommendations': '...', 'tool_usage_log': [{...}]}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.