Combining traces and evals in one platform collapses the manual regression-fixing workflow into a fast, automatable process.
- AI regressions often go unnoticed by standard dashboards and are first detected in support channels.
- Separate tools for traces and evals force a long manual journey from detecting to fixing a regression.
- Unified tools allow a suspicious trace to become a dataset entry with one click and reuse the same scoring function in production.
- Running online evals on production traffic enables immediate alerts when a regression occurs, bypassing support queues.
- With traces and evals in the same place, automation can propose prompt changes, suggest new scorers, and link score drops to deploys.
The problem with separate tools
Most AI regressions don't show up on dashboards. Latency and error rates remain normal while the agent's output is wrong. The first sign often arrives via a support queue or escalation thread. Teams that run evals in the same place they log traces catch these regressions fast. When the two are in different tools, every regression requires a long, manual sequence of handoffs, each prone to stalling.
How unification speeds debugging
Finding AI failures requires complex metrics — like whether the agent picked the right tool or gave a coherent answer — that standard observability misses. Typically, someone scrolling logs notices a weird trace, then exports it, moves to an eval tool, builds a dataset, writes a scoring function, runs the eval, decides if the regression is real, writes a fix, re-runs the eval, and deploys. Every step involves a different mental model and context switch.
When traces and evals share a single tool, that overhead vanishes. A suspicious trace becomes a dataset entry with one click. The scoring function that catches the regression also monitors the fix in production, ensuring the regression doesn't return. Debugging can be completed in an afternoon.
Automation as the next layer
Being alerted by a support queue is the slowest quality signal. Online evals running against production traffic can trigger alerts, dashboards, or tickets the moment a regression occurs. Once traces and evals are unified, automation can take over: the system already knows when a score dropped, which traces caused it, and what the dataset looks like. Braintrust can propose a prompt change, run the eval, and surface the result for review. It can also spot recurring failure patterns without a predefined scorer and link score drops to specific deploys.
None of this works when traces and evals are in separate tools, because no single tool has the full picture. Putting them together is the prerequisite for a faster workflow on day one and for progressively automating that workflow away.
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