Getting an AI agent to resolve a ticket once is not the hard part. Getting it to resolve tickets consistently, across edge cases, after product changes, and at scale, is where most deployments quietly struggle. This session gets into the operational discipline behind reliable AI resolution: the knowledge maintenance, the feedback loops, the quality checks, and the team habits that separate AI support that works on launch day from AI support that still works six months later. Key learnings: - What breaks AI resolution consistency and how to catch it before customers do - How to build feedback loops that keep AI performance stable after product changes - What ongoing knowledge maintenance looks like in a live support environment - The team habits and ownership models that sustain AI quality over time If your AI support is live but performance feels unpredictable, this session is for you.