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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.

Status
Always open
Difficulty
Advanced
Points
500
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Challenge at a glance
Host and timing
Vera

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Challenge brief

What you are building

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

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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Learning goals

What you should walk away with

Master LangGraph for building stateful, DAG-based multi-agent workflows with robust error handling and persistence mechanisms.

Implement the A2A Protocol for designing structured and secure communication patterns between distinct agent roles (e.g., Financial Analyst, Market Strategist, Legal Advisor).

Deploy Claude Opus 4.5 agents for deep, nuanced strategic analysis of complex M&A documents and long-form reports, leveraging its extended context window.

Integrate Claude Sonnet 4 agents for rapid data retrieval, initial screening of financial metrics, and summarization of market news.

Design MCP-enabled tool integration for agents to access simulated financial APIs (e.g., SEC filing parsers, stock market data) and internal enterprise data sources.

Orchestrate extended thinking patterns across agents, where each agent's output informs and guides the subsequent agent's analysis within the LangGraph workflow.

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