Supply Chain Compliance Agent with Adaptive Budgets
Addressing concerns about supply chain integrity, this challenge involves building an intelligent multi-agent system using AutoGen and DSPy. Your system will act as a 'Supply Chain Auditor' to investigate potential compliance breaches related to restricted technologies. Leveraging GPT-5 for its advanced reasoning capabilities, a team of specialized agents (e.g., Data Gatherer, Risk Analyst, Report Generator) will collaborate to analyze vast datasets including simulated financial transactions, shipping manifests, public sanctions lists, and news articles. DSPy will be used to programmatically optimize LLM calls for robust information extraction and verification. The system must employ adaptive thinking budgets to manage computational resources and integrate with an MCP for secure access to sensitive enterprise data and reporting suspected violations. This project emphasizes multi-agent orchestration, ethical AI considerations, and data-driven risk assessment.
What you are building
The core problem, expected build, and operating context for this challenge.
Addressing concerns about supply chain integrity, this challenge involves building an intelligent multi-agent system using AutoGen and DSPy. Your system will act as a 'Supply Chain Auditor' to investigate potential compliance breaches related to restricted technologies. Leveraging GPT-5 for its advanced reasoning capabilities, a team of specialized agents (e.g., Data Gatherer, Risk Analyst, Report Generator) will collaborate to analyze vast datasets including simulated financial transactions, shipping manifests, public sanctions lists, and news articles. DSPy will be used to programmatically optimize LLM calls for robust information extraction and verification. The system must employ adaptive thinking budgets to manage computational resources and integrate with an MCP for secure access to sensitive enterprise data and reporting suspected violations. This project emphasizes multi-agent orchestration, ethical AI considerations, and data-driven risk assessment.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master AutoGen for defining and orchestrating collaborative, role-based multi-agent teams for complex problem-solving.
Implement DSPy to build robust LLM pipelines, focusing on optimizing prompts for information extraction, verification, and reasoning with GPT-5.
Design and integrate an MCP to securely connect agents to simulated enterprise databases (e.g., ERP, CRM, logistics) and external public data (sanctions lists).
Develop specific agent roles (e.g., 'Data Gatherer', 'Risk Analyst', 'Legal Advisor', 'Report Generator') with distinct responsibilities and communication protocols.
Leverage GPT-5's advanced reasoning for detecting subtle anomalies, synthesizing information from disparate sources, and assessing compliance risks.
Implement adaptive thinking budgets, allowing agents to dynamically request more tokens or deeper reasoning steps based on the complexity and criticality of the information being processed.
Create a structured compliance report generation module that summarizes findings, evidence, and risk levels, accessible via the MCP.
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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