implementation

Implement Data Scraping and Initial Extraction Tool

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Multi-Agent System for Automated Audit Evidence Collection

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Linked challenge

Multi-Agent System for Automated Audit Evidence Collection

Prompt source

Original prompt text with formatting preserved for inspection.

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Develop a tool function (e.g., `scrape_financial_data(url)`) that simulates or uses Bright Data to fetch content from a given URL. Integrate this tool with your 'Researcher' agent so it can be called programmatically. Ensure the tool returns structured text data that the 'Analyst' can then process. Focus on extracting annual report text.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

Prompt diagnostics

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Purpose

implementation

This prompt already mixes executable detail with instructions, so tune examples and interfaces before rewriting the scaffold.

Linked challenge

Multi-Agent System for Automated Audit Evidence Collection

Develop a sophisticated multi-agent system using Microsoft's AutoGen framework to automate the collection and initial analysis of financial audit evidence. This challenge focuses on creating a team of specialized AI agents that can autonomously navigate public financial documents, extract relevant data, reconcile inconsistencies, and present findings in a structured format. The system should mimic the workflow of junior auditors, but with AI-driven efficiency and consistency, leveraging advanced LLM capabilities for reasoning and information synthesis. The final output should be a summary report highlighting key extracted data points and any identified discrepancies, preparing the ground for human oversight. This project will involve designing conversational agent roles, defining their communication protocols within AutoGen, and integrating external tools for data access and long-term memory. It emphasizes practical application in a business context, showcasing how generative AI can streamline complex, data-intensive tasks in financial services.

Open challenge

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