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Challenge

Multi-Agent M&A Due Diligence

This challenge focuses on building a sophisticated multi-agent system for automated financial and strategic due diligence. You will use LangGraph to orchestrate a team of specialized agents, each leveraging Claude Opus 4.5 or Claude Sonnet 4.5, communicating via an A2A Protocol. This system will analyze complex financial filings, market data, and strategic reports, collaborating to provide a comprehensive M&A recommendation. Key elements include graph-based workflows, extended thinking for multi-step analysis, and secure MCP tool integration for accessing enterprise financial data.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build a graph-based multi-agent system using LangGraph and A2A Protocol, powered by Claude Opus 4.5, to perform automated M&A financial and strategic due diligence with MCP tool integration.

Delivery guide

How work is evaluated

Evaluation

The evaluation will assess the multi-agent system's ability to provide a comprehensive M&A due diligence report, including financial analysis, strategic fit assessment, and potential risks, demonstrating effective LangGraph orchestration, A2A communication, and Claude model utilization.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Learning goals

What you should walk away with

  • Implement A2A Protocol for secure and structured agent-to-agent communication.

  • Integrate MCP-enabled tools for accessing simulated financial data and market insights.

  • Utilize Claude Opus 4.1 and Claude Sonnet 4 for specialized financial and strategic analysis.

  • Design a multi-agent system using LangGraph for M&A due diligence workflows.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 1 tasks

Challenge setup

The evaluation will assess the multi-agent system's ability to provide a comprehensive M&A due diligence report, including financial analysis, strategic fit assessment, and potential risks, demonstrating effective Lan...

Challenge input

{'acquisition_target': {'name': 'TechCo Innovators', 'industry': 'AI Software', 'public_filings': ['url_to_simulated_sec_filing'], 'market_data_que...

Agent execution

No agent tool is listed for this challenge.

Evaluated output

{'report': {'executive_summary': '...', 'financial_analysis': {'valuation': '...', 'debt_equity': '...', 'growth_projections': '...'}, 'market_anal...

Evaluation checks
  • All required sections of the M&A report are present and non-empty.
  • The communication log shows clear agent-to-agent interactions, indicating A2A Protocol use.
  • The communication log and report structure reflect a logical, multi-step LangGraph workflow.
Configured evaluation evidence
  • FinancialAnalysisAccuracy target: 0.85
Configured runtime
  • 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

  • All required sections of the M&A report are present and non-empty.
  • The communication log shows clear agent-to-agent interactions, indicating A2A Protocol use.
  • The communication log and report structure reflect a logical, multi-step LangGraph workflow.

Metrics

  • FinancialAnalysisAccuracy · Target 0.85
  • StrategicInsightQuality · Target 0.9

Workflow tasks

  1. Task 1

    Input: {'acquisition_target': {'name': 'TechCo Innovators', 'industry': 'AI Software', 'public_filings': ['url_to_simulated_sec_filing'], 'market_data_query': 'recent market trends for AI software companies'}, 'acquirer_profile': {'name': 'Global Enterprises', 'strategic_goals': ['expand AI portfolio', 'achieve market leadership']}}

    Output: {'report': {'executive_summary': '...', 'financial_analysis': {'valuation': '...', 'debt_equity': '...', 'growth_projections': '...'}, 'market_analysis': {'competitive_landscape': '...', 'market_share_impact': '...'}, 'strategic_fit': '...', 'risk_assessment': ['legal', 'integration', 'market'], 'recommendation': 'buy|hold|sell'}, 'agent_communication_log': [{'sender': 'FinancialAgent', 'receiver': 'StrategicAgent', 'message': '...'}]}

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Multi-Agent M&A Due Diligence