Many Voice AI agents that sound impressive in demos fail in production due to inconsistency, lack of control, or poor visibility into their actual performance.
Retell AI is handling 40M calls a month with Voice AI, and that volume has grown by 10M in the last 30 days alone.
We are seeing enterprises move up to 80 percent of support calls to Voice AI agents, in multiple languages, and achieve higher customer satisfaction and lower costs. They are also reducing abandon rates by up to 20 percent by deploying agents that are indistinguishable from humans.
Here’s how to make Voice AI agents both reliable and scalable.
1. Simulation Testing to validate agent performance pre-launch.
A Voice AI agent shouldn’t be released until you’ve ensured every flow works. Traditionally, this has meant calling the agent manually dozens or hundreds of times – feature testing, if you will. The process could take weeks to complete. Now, it’s possible to batch-test hundreds of conversation paths with synthetic inputs to measure success rates, latency and comprehension accuracy – a method that’s faster, far more comprehensive and automatically flags failed or ambiguous responses. The outcome is reduced deployment time – Voice AI agents can be deployed in days now instead of months. This method also prevents costly errors in production.
2. Automating Quality Assurance in production.
Until recently, companies evaluated Voice AI agent quality in production by having human agents listen to a sampling of calls – perhaps 1-3 percent. Those humans would score each call for accuracy, empathy and adherence to flow. Any problems were flagged to a human developer, who would then tweak models as needed. In this scenario, there might be a lag of days or weeks before problems were addressed, during which flawed agents would stay in production interacting with customers. To address this, companies can automate QA in production by using AI to monitor not just a sampling but all Voice AI agent interactions, detecting issues like overtalking or misinterpreting intent. AI can then also automatically retrain Voice AI agents on those edge cases, fixing problems in near real-time before other customers are impacted. AI can also be used to review past call transcripts to spot problems. The goal is to create a continuous monitoring and feedback loop where problems are spotted and corrected immediately, minimizing impact on Voice AI agent performance.
3. Set up real-time alerts for problem calls.
If you’re using AI to monitor calls, it should be able to send real-time alerts to a manager when there’s a problem – for instance, a frustrated customer. This enables a human agent or manager to immediately take over the call to find a resolution.
4. Create an Analytics Dashboard for visibility into performance and real-time alerts.
It’s crucial to track key metrics for Voice AI agents, including resolution rates, average handle time, interruption patterns, sentiment trends, and success metrics per campaign or agent version, and to do that over time. The data will help you spot performance trends that could affect operational decisions – for instance, when to retrain or escalate an issue. Empowering teams to understand what’s happening across millions of calls enables data-driven iteration and helps you measure return on your Voice AI investment.
Moving from demo or pilot into production – and then scaling your Voice AI agent team to keep up with growth – requires rigorous testing, monitoring and insight. Enterprises deploying Voice AI must take all four steps to succeed with this transformative technology.
