--- title: "How AI observability works with MLflow" url: https://daily.dev/posts/how-ai-observability-works-with-mlflow-7nadkipz3 source_url: https://developers.redhat.com/articles/2026/08/26/how-ai-observability-works-mlflow type: article source: "Red Hat Developer" published: 2026-08-26T09:50:59.214Z updated: 2026-08-26T09:51:22.359Z tags: ["machine-learning", "llm", "observability", "opentelemetry", "agentic-ai"] reading_time: 5 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # How AI observability works with MLflow **[Red Hat Developer](https://daily.dev/sources/rhdev)** · 5 min read · 0 upvotes · 0 comments ## Summary Debugging a mismatch between an AI assistant's answer and dashboard numbers in a mortgage lending demo app illustrates why AI observability matters. Using MLflow, distributed traces reveal model call latency, tool call inputs/outputs, and decision-making steps, showing the agent accurately relayed data from a tool call rather than hallucinating—meaning the real discrepancy lies further upstream in how the data was calculated. MLflow is open source, OpenTelemetry-compatible, and integrates with frameworks like LangChain and Claude Code, and pairs with Red Hat OpenShift AI for trace isolation across hybrid cloud environments. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How can I tell if an AI agent hallucinated versus a bug in the tool it called? Trace the request with a tool like MLflow to inspect what data the model actually received from its tool calls versus what it reported. In a mortgage lending example, the agent's response matched the exact numbers returned by its tool call, proving the model didn't invent the figures—the real discrepancy was further upstream, where the tool and a dashboard disagreed on calculation or date range. _daily.dev surfaces practical guides for teams debugging AI agent behavior with tracing tools._ ### What does MLflow tracing capture for a single agentic AI request? A single agentic query can generate more than 30 metrics, such as token counts, LLM latency, and database query times, along with more than 15 log events like agent decisions and tool parameters, all captured in one distributed trace. This lets teams see model call duration, tool call inputs and outputs, and pinpoint where time and errors originate without manually correlating multiple services. _Developers tracking AI observability tooling changes can follow updates like this on daily.dev._ ### Does MLflow tracing work with Claude Code and LangChain? Yes, MLflow provides automatic tracing integrations for popular model and agent frameworks including LangChain, Claude Code, and other AI tools, and it is compatible with OpenTelemetry. Red Hat OpenShift AI adds logical isolation for traces and experiments so teams can debug requests and monitor AI application performance across hybrid cloud environments. _daily.dev helps developers compare observability integrations across agent frameworks like this one._ ## Similar posts on daily.dev - [Observability tools weren’t built for AI debugging](https://daily.dev/posts/observability-tools-weren-t-built-for-ai-debugging-tnfxqsngm) · LeadDev · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#llm](https://daily.dev/tags/llm), [#observability](https://daily.dev/tags/observability), [#opentelemetry](https://daily.dev/tags/opentelemetry), [#agentic-ai](https://daily.dev/tags/agentic-ai) [View this post on daily.dev](https://daily.dev/posts/how-ai-observability-works-with-mlflow-7nadkipz3)