Databricks Announces Native OpenTelemetry Observability Support for Claude Platform in MLflow
Databricks has integrated native OpenTelemetry export from Claude Platform into MLflow, allowing developers to unify agent tracing and debugging.
On November 3, 2026, Databricks announced that its machine learning lifecycle management tool, MLflow, now natively supports OpenTelemetry observability data from the Claude Platform. This feature, currently in Public Beta, enables developers using Anthropic SDKs, the Messages API, and Claude Managed Agents to export their agent execution traces directly into the Databricks environment.
Through this native integration, agent execution steps—such as model requests and tool calls—are recorded as OpenTelemetry spans and stored in Unity Catalog tables via Zerobus Ingest technology with low latency. This allows developers to view and analyze Claude agent traces alongside those from other instrumented services within the same MLflow experiment interface, eliminating the need for additional conversion layers or middleware.
The core value of this integration lies in simplifying the debugging and evaluation workflows for AI agents. Developers can use MLflow's built-in Genie Code analysis tools to inspect the execution path behind specific responses, identifying missing context or failed tool calls. Additionally, the MLflow Issues feature can automatically detect recurring problems across sessions, such as token-wasting repeated searches or tool failures caused by missing arguments.
For enterprise users, storing sensitive agent trace data in tables governed by Unity Catalog permissions helps meet data governance and security compliance requirements. Developers can query these traces using SQL and combine them with MLflow Agent Evaluation to quantitatively assess changes to prompts, tools, or skills, ensuring that improvements genuinely enhance agent performance without introducing regressions.