Wanted to share an honest take because most of what I read about AI agents in customer experience makes it sound cleaner than it is in practice. We’ve been running an AI agent on our support channels for about four months now. Trained it on our internal knowledge base, product documentation, escalation policies, and the most common query types our team handles. Deployment wasn’t the hard part. Defining scope was. Where it genuinely performs: tier-one queries, product questions, policy lookups, anything that maps cleanly to documented information. Response time is instant, tone is consistent, and it doesn’t degrade at 2am or on a Friday afternoon. Where it hits a ceiling: anything requiring judgment about a specific customer situation. Complex account histories, emotionally escalated customers who need a human in the loop, queries that pull from data outside what the agent was trained on. Those route to our team. That’s intentional, not a gap we’re trying to close. The framing that actually made sense internally was this: the agent handles the repeatable 80%. Our team handles the 20% that actually requires human judgment. Both sides work better because of that separation. We run on Chatbase at the department level and have for a while now. Curious how other CX teams are handling knowledge base updates as products and policies change, do you have a formal refresh process or is it reactive? submitted by /u/Many-Personality-157
Originally posted by u/Many-Personality-157 on r/ArtificialInteligence