Coval

Coval is a strong tool that makes creating AI agents simple and trustworthy. It assists engineers by making the testing process automatic. This speeds up work and ensures AI solutions perform well in real life. Inspired by self-driving cars, Coval brings advanced testing methods to AI development.

With Coval, developers can make their own measurements to check AI agents. They can run thousands of real-life situations from just a few tests. This helps find the main causes of issues and track how AI agents work together. The tool allows quick checks using customer talks or simple language user wishes. This makes it easy to see how systems get better over time.

One of the best parts of Coval is its complete checking system. It works for both text and voice AI, giving detailed scores and performance checks. This makes it easy to understand how well an AI agent is doing and where it needs to get better.

Coval also makes the joining and launching process simple. Its automatic tools fix the problems of traditional manual testing. This ensures that AI agents are steady and reliable in real-life use. This is very helpful for developers who want to keep high standards without the trouble of manual testing.

The platform was started by Brooke Hopkins, who has experience from leading the evaluation job team at Waymo. Inspired by the change to automatic testing in self-driving cars, Coval aims to bring similar improvements to AI agent development. The goal is to help engineers create AI experiences that really work for users.

Coval, based in San Francisco, has raised 500K in a Seed round led by Y Combinator. The company is ranked 9th among its active competitors and is set to be a leader in the AI agent simulation and checking space. Users have praised Coval for its ability to make the development process simple and ensure the trustworthiness of AI solutions.