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GPT-5 & Agent Framework: A2A Financial Analysis Agent

Develop a multi-agent system using GPT-5 and a modular AI framework for financial analysis. Agents will leverage A2A communication to collaborate on tasks like analyzing valuation, comparing expansion plans with competitor data from external sources, and predicting future financial performance. The framework will provide MCP-enabled access to financial databases and news APIs. Agents should demonstrate adaptive thinking budgets, prioritizing tasks based on importance and available data.

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What you are building

Build an A2A multi-agent system using GPT-5 and AI Framework for financial analysis, integrating external data via MCP.

How work is evaluated

Evaluation

Evaluation based on accuracy of financial analysis, efficiency of agent collaboration, and completeness of reports.

Datasets

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

Learning goals

  • Design agent roles and communication protocols for efficient collaboration.

  • Implement adaptive thinking budgets for agents.

  • Analyze financial data from multiple sources and generate reports.

  • Build a multi-agent system using the A2A protocol.

  • Integrate Haystack for MCP-enabled access to financial data sources.

Resources

Dataset notes

Sample data for 1 tasks

Challenge setup

Evaluation based on accuracy of financial analysis, efficiency of agent collaboration, and completeness of reports.

Challenge input

JSON containing financial data and relevant news articles.

Agent execution

No agent tool is listed for this challenge.

Evaluated output

PDF or Markdown financial report.

Evaluation checks
  • Checks if the report includes all required sections and data.
Configured evaluation evidence
  • Accuracy target: 0.8
Configured runtime
  • Python execution harness

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

No tool records are attached.

Evaluation contract

  • Checks if the report includes all required sections and data.

Metrics

  • Accuracy ยท Target 0.8

Workflow tasks

  1. Task 1

    Input: JSON containing financial data and relevant news articles.

    Output: PDF or Markdown financial report.

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about GPT-5 & Agent Framework: A2A Financial Analysis Agent