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Challenge

Automate GenAI Data Prep & System Integration

IT industry is adapting to GenAI by focusing on data cleanup and system integration. This challenge aims to build an autonomous multi-agent system using AutoGen, powered by Gemini 3 Pro, to automate complex data preparation and schema mapping for enterprise AI adoption. Agents will collaborate to ingest raw data, perform transformations, reconcile schemas across disparate enterprise systems, and integrate cleaned data using MCP-enabled tools. The system will employ hybrid reasoning, combining Gemini's advanced data understanding with structured data processing tools, to ensure data quality and seamless integration, thereby accelerating enterprise GenAI readiness.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

IT industry is adapting to GenAI by focusing on data cleanup and system integration. This challenge aims to build an autonomous multi-agent system using AutoGen, powered by Gemini 3 Pro, to automate complex data preparation and schema mapping for enterprise AI adoption. Agents will collaborate to ingest raw data, perform transformations, reconcile schemas across disparate enterprise systems, and integrate cleaned data using MCP-enabled tools. The system will employ hybrid reasoning, combining Gemini's advanced data understanding with structured data processing tools, to ensure data quality and seamless integration, thereby accelerating enterprise GenAI readiness.

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Learning goals

What you should walk away with

  • Orchestrate AutoGen role-based agent teams (e.g., Data Engineer Agent, Schema Mapper Agent, Quality Assurance Agent) for collaborative data pipeline execution

  • Leverage Gemini 3 Pro's multi-modal capabilities for understanding diverse data formats (e.g., spreadsheets, PDFs, JSON schemas) and generating transformation logic

  • Implement MCP-enabled tool integration with enterprise data sources (e.g., mock CRM/ERP APIs, SQL databases) for automated data extraction and loading

  • Build dynamic schema mapping agents that use LlamaIndex for RAG over enterprise documentation and data dictionaries to intelligently reconcile disparate schemas

  • Develop hybrid reasoning workflows where Gemini 2.5 Pro identifies data quality issues and generates Python scripts (via its code generation capabilities) for rectification, executed by a Code Executor Agent

  • Design graph-based data lineage tracking and transformation workflows to visualize data flow and dependencies across agents and systems

  • Implement self-correcting mechanisms where agents can detect and resolve data inconsistencies or integration failures autonomously

Start from your terminal
$npx -y @versalist/cli start automate-genai-data-prep-system-integration

[ok] Wrote CHALLENGE.md

[ok] Wrote .versalist.json

[ok] Wrote eval/examples.json

Requires VERSALIST_API_KEY. Works with any MCP-aware editor.

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