Replace "AI shipped on hope" with an operating model that holds up once real users depend on it. AI quality is multi-dimensional, covering accuracy, tone, safety, and faithfulness to user data, and can't be debugged from outputs alone. Without visibility into what their AI actually did in production, teams miss regressions, reverse-engineer chains by hand, and watch a single bad answer erode trust built over hundreds of right ones. Learn how to operate AI with the same discipline you apply to any production system, anchored in LLM Observability. Start by tracing every prompt, retrieval, and tool call end-to-end so you can see what your agents did and why. Production traffic then becomes your evaluation dataset, replacing synthetic tests that age the moment users do something unexpected. Structured experiments let you compare prompt and model variants with confidence before changes reach users. See how to catch regressions in quality, latency, and cost before users feel them, connect AI behavior to the rest of your stack, and equip every team shipping agents to own their reliability. Learn how to define quality for their AI, investigate faster when outputs look wrong, and ship updates engineering and reliability teams can trust.

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