AI Engineering Lifecycle

The AI engineering lifecycle is the loop from observing production traces, to curating datasets, to running experiments, to releasing and monitoring, so that production evidence directly informs the next change.

LLM observability is the practice of recording traces, sessions, model and tool spans, cost, latency, errors, and feedback for AI applications so their behavior can be inspected and debugged like any other production system.

A representative trace or user signal is promoted into a versioned dataset with an expected outcome, which then becomes a repeatable check for future prompt, model, or workflow changes.

Connecting release context to live traces lets a team compare deployed behavior directly against evaluated expectations, closing the loop between what was tested and what shipped.

AI product teams that need to coordinate debugging, prompt iteration, evaluation, experimentation, and release monitoring without rebuilding context at every stage.