Data agents that keep blindly probing a schema through repeated exploratory queries could get a big boost from one thing: an ontology layer that evolves from actual evidence. Title: EvoOntology: A Self-Evolving Ontology Layer for Data Agents URL: 📝 Overview EvoOntology is a self-evolving ontology layer, implemented as an MCP server, for agents that work across heterogeneous data like tables, files, and databases. ❗ Problem it solves Agents have no prior knowledge of data structure and must repeatedly issue exploratory queries. Static semantic layers need manual upkeep and can't adapt from execution history. ⚙️ Methodology Only candidates verified by actual probe queries get committed into the ontology. From there, agent trajectories surface intervention candidates, which are paired against the current state and adopted only when they show a clear improvement. 📊 Results On DDR-Bench it improved by an average of +17.8 points across 6 backbones, and on BIRD by +8.6 points, beating prior work — while cutting dialogue turns from 14.6 to 8.4 and reducing tokens by about 20%. 🔬 Use cases Even where static semantic layers actually hurt performance for some models, EvoOntology improved consistently — making it a practical fit for real-world data agent infrastructure. #DataAgents# #LLM#