CovalSimulation and evaluation platform for testing AI voice and chat agents at scale

Coval is a testing and evaluation platform aimed at teams building conversational AI agents, particularly those operating across voice and chat modalities. It addresses a recurring problem in agent development: traditional unit tests and manual spot-checks don't capture the non-deterministic, multi-turn nature of agentic systems, making it hard to know whether a change improves or regresses real-world behavior. The platform's core idea is simulation. Rather than relying solely on static test cases, Coval generates simulated user interactions that exercise an agent across many scenarios and conversational paths. These simulated runs can then be scored against defined metrics and expectations, allowing teams to measure reliability before shipping changes and to catch regressions as agents and prompts evolve. Coval positions itself for both voice and text agents, which is notable because voice introduces additional layers — speech-to-text, latency, and turn-taking — that affect agent quality beyond the underlying language model. The company has drawn comparisons to the way autonomous-vehicle teams use large-scale simulation to validate behavior before deployment, applying a similar testing philosophy to AI agents. In a typical workflow, a team connects their agent, defines scenarios and evaluation criteria, runs simulations, and reviews results across runs to track performance over time. This supports use in development as well as ongoing monitoring and regression testing as part of a CI-style process. As a relatively young product in an evolving category, details of its pricing, integrations, and exact metric coverage are best confirmed directly, and teams should evaluate how well its simulated scenarios reflect their own production traffic. Its main differentiation from general LLM-evaluation tools is the emphasis on multi-turn, multi-modal agent simulation rather than single-prompt scoring.