BI for AI: Transforming OTel Agent Telemetry into Enterprise Analytics

You’ve instrumented your GenAI application, captured your MLflow traces using OpenTelemetry standards and stored them in Unity Catalog. Now what? Too often, observability stops at ingestion, treating traces like passive, isolated logs instead of valuable corporate data. This session looks past the initial setup to show the analytical power unlocked when agent telemetry lives inside a governed lakehouse. By merging your trace tables with existing enterprise data, you can move past micro-debugging and start running true business intelligence on your artificial intelligence.

Key takeaways:

• Cross-domain data ingestion (join MLflow trace tables with Zendesk/Salesforce data to correlate agent performance with ticket volume/CSAT)
• SQL-driven agent analytics (isolate systemic tool failures, latency bottlenecks, infinite loops across thousands of runs)
• Data-driven evaluation loops (harvest production logs for targeted LLM eval and fine-tuning)