testing

Orchestrate Full Audit Workflow and Evaluation

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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Create a `UserProxyAgent` to orchestrate a full audit workflow, initiating the process by providing a company name and relevant URLs. The agents should converse, use their tools, and produce the final `AuditReportGeneration` JSON output. Test the full system with a sample company and evaluate the report's accuracy against known facts. Use Libretto for observing the agent's interaction flow and debugging.

Adaptation plan

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

Keep stable

Preserve the rubric, target behavior, and pass-fail criteria as the baseline for evaluation.

Tune next

Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.

Verify after

Make sure the prompt catches regressions instead of just mirroring the happy-path examples.

Prompt diagnostics

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Purpose

testing

This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

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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