How does an AI voice agent improve over time?

An AI voice agent improves mainly through two channels: a business regularly expanding its knowledge base based on real call data, and the underlying language model itself getting periodic updates from the provider. Televanta's operator portal surfaces unanswered questions and low-containment call types directly, so a business always has a clear, specific list of what to improve next.

The knowledge base side is where most of the practical, day-to-day improvement actually happens, and it's mostly in the business's own hands. Every call that gets escalated or that the AI struggles with becomes a data point pointing at a specific gap.

Fixing those gaps steadily, week after week, is what drives containment rate and answer quality up over time. A telecoms provider shows what a disciplined version of this looks like, with a dedicated team reviewing the unanswered questions report every single week.

Over six months, containment climbs from an initial 50 percent to 78 percent, not because the underlying software changed dramatically, but because the knowledge base behind it got steadily better and more complete. A healthcare provider shows a slightly different improvement loop, one built around actual customer feedback rather than just call outcomes.

Post-call satisfaction surveys get reviewed alongside call transcripts, and low-scoring calls get analyzed specifically to understand what went wrong. Fixes get made based on that combined view, rather than looking at containment numbers alone without any sense of how the calls actually felt.

On the technology side, Televanta periodically updates the underlying speech recognition and language models as better versions become available. These updates happen in the background, so a business benefits from ongoing model improvements without needing to manage a technical upgrade process on its own end.

The combination of both channels, business-driven knowledge base growth and provider-driven model improvements, is what produces steady gains over time. Neither one alone gets a deployment to its best performance nearly as quickly as both working together.

It helps to set expectations properly around the pace of this improvement. The biggest early gains usually come in the first month or two, as the most common gaps in the knowledge base get filled in fairly quickly once a team starts paying attention.

Businesses that treat this as a one-time setup task rather than an ongoing habit tend to plateau earlier than they need to. Setting a regular, even brief, schedule for reviewing call data keeps the improvement loop active instead of letting it quietly stop.