Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Within your LangGraph workflow, implement the A2A protocol for seamless communication between the 'Financial Analyst Agent' and the 'Risk Assessment Agent'. The Financial Analyst should pass its findings to the Risk Assessment Agent, which then uses OpenAI o3 to synthesize these findings with legal and market data to generate a comprehensive risk profile.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
M&A Due Diligence: LangGraph & OpenAI o3 with MCP for Financial Analysis
SoftBank's recent acquisition of DigitalBridge underscores the critical need for efficient M&A due diligence. This challenge invites developers to construct a robust, graph-based multi-agent system using LangGraph to automate and enhance various aspects of M&A analysis. The system will leverage the cutting-edge reasoning capabilities of OpenAI o3, combined with a sophisticated A2A protocol for seamless agent-to-agent collaboration. Key to this challenge is implementing MCP tool integration, enabling agents to securely access and analyze vast amounts of financial data from both public and proprietary sources. Participants will design intricate LangGraph workflows, integrate RAG with Pinecone for document retrieval, and apply adaptive thinking budgets, mimicking the iterative and resource-intensive nature of real-world M&A processes, ultimately generating strategic recommendations and risk assessments.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.