# What does a production span carry that a dataset row cannot?

A production span carries context nobody writes into a dataset row by hand: `session.id`, `deployment.environment.name`, and the exact tool call, `gen_ai.tool.name` plus the arguments it actually ran with, not a stand-in for one. A hand-built eval case can approximate a prompt and an expected answer, but it can't manufacture those fields, because they only exist because a real request happened at a real moment inside a real deployment. That's what makes production traces catch regressions a static dataset can't: they carry the conditions a failure happened under, not just the text that was said. It's also why they're evidence rather than examples. When something goes wrong, the trace is what a root cause analysis points back to; a dataset row assembled ahead of time was never anchored to an actual event.

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Source: https://tessary.ai/answers/tessary-traces/what-does-a-production-span-carry-that-a-dataset-row-cannot
More on Tessary traces: https://tessary.ai/answers/tessary-traces
From Tessary, agent reliability for AI agents in production: https://tessary.ai