Unfortunately from our experience tests don’t scale as well as code. First of all, static tests are very brittle: you rely on selectors, need wait times, and can’t really test a lot of dynamic content (think AI chats/interactions). Then it’s all the infrastructure around it: solving captchas, handling auth, handling email OTP (each of our agents has access to its own inbox) and handling video recording and screenshots.

To ensure stable results we do a lot of harness engineering, where we inject trajectories of previous tests to ensure the stability and also the split into smaller steps helps to prevent context overload and decision fatigue.

Regarding test case management, our customers have used our CLI to migrate their existing test cases from whatever system they were using before.

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Thank you! That's the goal

Unfortunately from our experience tests don’t scale as well as code. First of all static tests are very brittle, you rely on selectors, need wait times and can’t really test a lot of dynamic content (think AI chats/interactions). Then it’s all the infrastructure around it: solving captchas, handling auth, handling email OTP (each of our agents has access to its own inbox) and handling video recording and screenshots. So with the traditional testing approach you end up mocking a lot of services. I highly recommend you to give it a try!

We support both web and mobile, which is what a lot of companies prefer, just one agent for both. Also, I'm pretty sure Revyl relies only on vision models, which tend to be slower. We built the platform around a hybrid approach that combines vision and accessibility APIs, which is much faster.

Would love to hear your feedback after you try it out!

Yes! We spent quite a lot of time on this, and we are currently creating a test plan based on PR changes and sending an agent to verify it. We have some customers who are only using this feature.

Our approach is heavily focused on agents, both for executing tests and for managing the platform. We want to provide the best and simplest way to conduct agentic testing, with a strong focus on details. It looks like their platform also requires a sales call.

We use agents to navigate the app, making real-time decisions based on its state. I prefer to compare it more to a manual QA engineer than to static e2e tests. We spent a lot of time on the harness to make sure the results are reliable. This allows you to assert on dynamic content like AI-generated content. We also support validation of email flows since the agent can read its own email.

Yes, it works for any framework. We just get the built native binary and run it in the cloud.

Regarding pricing, the self serve options are currently only for lower usage. We will add more plans further down the line. Currently the most popular one is the startup plan. If you need more usage I’m happy to discuss it on a call!

Unfortunately from our experience tests don’t scale as well as code.

First of all, static tests are very brittle: you rely on selectors, need wait times, and can’t really test a lot of dynamic content (think AI chats/interactions). Then it’s all the infrastructure around it: solving captchas, handling auth, handling email OTP (each of our agents has access to its own inbox), spinning up simulators and handling video recording and screenshots.

To ensure stable results we do a lot of harness engineering, where we inject trajectories of previous tests to ensure the stability and also the split into smaller steps helps to prevent context overload and decision fatigue.

Regarding security part, the product can operate solely without any access to the codebase, you can just give us a URL or a mobile app build and we will do the testing.

Thank you!

We are thinking whether to change this.. We also have testerarmy.com/.ai