Post-Launch Care for AI Products: Monitoring, Evals, and Change Control

AI products do not become stable simply because they launched. Models, data, prompts, tools, and user behavior all change. Post-launch care is where the system becomes dependable.

011. Monitor the Workflow, Not Just the Server

Uptime matters, but it is not enough. Track answer quality, tool failures, refusal rates, latency, escalation, approval outcomes, and user corrections.

The most important failures may be semantic, not technical.

AI operations should monitor quality, decisions, and workflow outcomes.

022. Keep Evaluations Current

Evaluation sets should grow with production usage. Add new edge cases, failed answers, policy changes, and examples where users needed a different output.

A stale eval set gives a false sense of safety.

033. Version Prompts and Retrieval

Prompt edits, model changes, embedding refreshes, and source document updates can all change behavior. Record meaningful changes and test the workflows they affect.

Change control helps teams improve without losing the ability to explain what changed.

044. Assign an Operational Owner

Someone should own the live AI product: reviewing metrics, triaging incidents, approving changes, and deciding when the system needs redesign.

Launch proves the system can run. Operations proves it can be trusted.