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Cover page of a Dedicatted whitepaper titled ‘AI Multi-Agent Systems for Data Modeling: Workflows, Metrics, Impact.’ The image shows a computer monitor displaying a digital brain graphic symbolizing artificial intelligence, with code running beside it. The design conveys themes of AI, automation, and data engineering, emphasizing innovation in data modeling processes.
Informational graphic describing Dedicatted’s use of LLM-powered multi-agent systems for data pipeline automation. It explains how the company embedded AI agents into pre-production and production workflows over two years to optimize processes like semantic extraction, ERD generation, schema matching, test-case synthesis, and drift detection. The section ‘What we found from the pilots?’ highlights that AI agents can shorten the path from requirement to production-ready schema by up to 3x. The bottom shows two engineers collaborating at computer screens displaying data visualizations.
Infographic titled 'Automated Schema Generation' showing Dedicatted’s AI-driven workflow for creating conceptual ERDs, logical models, and executable DDLs. The process includes input of user requirements, followed by agents: Semantic Agent, Structuring Agent, Logical Designer, and Validation Agent, with Human Review leading to outputs—ERD, DDL, and Data Dictionary. Below, short descriptions explain each agent’s role and note that usable first drafts are achieved in 5–7 days.

Whitepaper

AI Multi-Agent Systems for Data Modeling: Workflows, Metrics, Impact

October 8, 2025

Dmytro Petlichenko

5 min to read

Data Architecture Generative AI

AI multi-agent systems have been around long enough that most data engineers have tried them at least once (and now use them in some workflows). We did too. 

In 2023, we asked a simple question: how well do AI multi-agents actually automate processes in real production? With this healthy skepticism, we started running focused pilots and embedding agents into production pipelines.

Now, we turned those 2 years of experience into a repeatable set of Agentic AI workflows for: 

  • semantic extraction;
  • ERD generation;
  • schema/field matching;
  • test-case synthesis; 
  • drift detection.

Download the whitepaper to learn what we ran in production, how we measured it, how to run a low-risk pilot, and more agentic AI use cases.

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