how does the AI agent work?

We deploy AI to simulate user behavior to automatically generate end-to-end tests. It's followed up by deterministic systems that generate and run the tests.
See the Agent in action Book a demo

Octomind AI agent in a nutshell

Octomind AI agent mimics human users (i.e., clicks input fields, signs up for newsletter) to navigate apps, interprets app intent, and identifies all relevant user flows. This is how it runs when you sign up:

  1. The agent looks at your site.
  2. It looks for cookies and a 'required' login first. 'Required' is the type of login you need to get further in the app.
  3. When a 'required' login flow is found, we will ask you for your login test credentials.
  4. The agent will put these 2 tests as pre-requisites (dependencies) to all new tests.
  5. Next, the agent automatically generates up to 3 meaningful tests.
  6. If successful, it validates the tests and files them as 'active'.
  7. We will run all active cases.
  8. You can have the agent generate more tests sequential to the active test (up to 3 on each run) they were launched from.
  9. You can prompt the AI agent to generate a specific test case.
  10. You can run the AI agent to re-generate a broken test.

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Find a detailed description of the AI agent flows in our documentation.

automate test generation & execution
with your own AI

Install Octomind MCP server and connect your AI agent. Then:


  • Plug in your data source (Jira, Slack, docs ) to your agent
  • Let it analyze real project data
  • Auto-generate and launch Octomind test cases - no manual input needed

See the most common testing use cases for an MCP integration.

AI agent knows what to test

Our AI agent traverses the publicly accessible code in the DOM and uses the vision capability of the underlying multimodal LLM to add visual context when code insight is insufficient.  It tries to understand the purpose of your site to generate relevant end-to-end tests.
a screenshot of octomind AI agent generating test steps

AI agent generates tests, step by step

Our AI mimics user behavior to interact with apps as a human would to reach the goal of a user flow. These interactions are represented in test steps.

We record and store each test case's interaction chain and generate the corresponding  Playwright code deterministically on the fly immediately prior to test execution.
AI agent auto-fixes broken tests
beta version

When a test breaks due to behavioral changes in your app, you can have the Octomind AI Agent automatically fix the failing test case.

We pinpoint failing interactions and deploy the AI Agent to detect new desired interaction that achieves the test case's goals.
fight flaky tests with AI
work in progress  

We already deploy a variety of techniques to fight test flakiness. Yet it's not enough.

That's why we are investigating the best ways to implement AI-based analysis of unexpected site behavior, like temporary pop-ups or toasts, and further improve testing performance.

blogtopus: on AI, agents and app testing

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My MCP vision for Octomind

Last week’s open-sourcing of our MCP implementation is our contribution to the ecosystem. This week we are launching on Product Hunt. Let’s turn testing from afterthought into a first-class citizen together.
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AI doesn’t belong to test runtime

Not all AI in e2e testing is created equal. To deploy AI to testing successfully, you have to address concerns of brittleness, interpretability, and clunkiness. Mitigation strategies depend on the testing stage.
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Test automation was never just the problem of creating scripts, but a challenge of proper setup and context. Autonomous testing tools should look at test automation as a whole and go beyond simple scripting to be useful.
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Explore state-of-the-art agent powered testing.  Request a personalized demo, if you need to talk to our devs. Or just start for free, today.
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