Simulate difficult calls and monitor production voice-agent performance
Coval is a testing and monitoring platform for voice AI agents. It helps teams evaluate conversations before release and inspect performance after deployment. The product combines simulated calls, automated evaluation and human quality review, so a team can look beyond whether an agent answered a single demonstration question successfully.
Simulations can cover accents, background noise, interruptions, policy requirements and tool failures. These conditions are useful for exposing problems that a clean scripted exchange may miss. Teams can compare versions or providers and use regression testing to see whether a change improves one behavior while weakening another. Evaluation criteria should reflect the actual job of the agent, including appropriate escalation and the handling of incomplete information.
Production monitoring adds evidence from real calls. Human QA workflows and overrides provide a way to review automated judgments rather than treating every score as definitive. The platform also advertises API, CLI, MCP and webhook access for integrating tests into an existing development process. Access and retention should be checked against the plan and the organization's handling requirements for call data.
The published tiers include simulation-minute and monitored-call allowances, with additional usage charged separately. Starter, Growth and Enterprise also differ in retention and other capabilities. The trial requires a credit card, so its conditions should be reviewed before activation. A practical pilot uses representative scenarios and compares automated findings with human judgments. The retrieved homepage and pricing page used inconsistent wording about SOC 2 status; current security documentation should therefore be requested rather than assuming certification from this listing. Coval supports systematic evaluation, but the chosen metrics and the team's response to failures remain essential.
