Description
Overwhelming amounts of data can often overwhelm users in companies. As a result, the deeper insights these data offer unfortunately often remain hidden. This is where Multi-Agent Text-to-SQL comes in: By using modern AI agents, even non-technical users can query data using natural language and generate precise queries.
Here, new approaches provide a solution by distributing tasks across multiple AI agents. For example, fact-based answers are delivered by querying a database using Text-to-SQL (Structured Query Language), and the responsible agent uses these insights in its response.
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Experts
Dr. Frederic Engelke
Agenda
Welcome and Introduction
Limitations of Classic LLM Approaches
The Deterministic Solution: Text-to-SQL
Architecture of a Modern Multi-Agent System
Advanced Architectures
Security via Human-in-the-Loop
Comparison: LLM vs. Human Queries
Technical Example
Conclusion
Basic information
No prior knowledge is explicitly required, but familiarity with AI agents or SQL is helpful.
Online-Webinar
Technical users looking to build or deploy similar systems; decision-makers evaluating whether AI agents make sense for text-to-SQL integration.
German
