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.
Enhance your 'Analyst' agent to accept raw financial text data from the 'Researcher'. Implement logic for the Analyst to identify key financial figures (revenue, net income) and to compare information from multiple sources (e.g., annual report vs. simulated news article snippets) to detect discrepancies. The Analyst should be capable of outputting its findings in a structured JSON format.
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.
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.
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.