What is AI-native testing?

Momentic uses AI agents to explore an app, author tests, select tests for a code change, execute steps, and investigate failures. You can start an exploratory session with Mo or keep repeatable tests as YAML files in your repository. Repository tests connect authoring, execution, and maintenance through files you review in Git. Run data provides context for later agents and records the product journeys your suite covers.

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Explore with Mo

Give Mo a running web URL or an uploaded mobile build and a brief. Its hosted agents plan test cases, execute them, and reproduce suspected bugs. The session report contains cases, verdicts, reproduced bugs, and coverage gaps. Use Mo to check a preview build or a product area before you have a test suite. It does not write YAML test files. After a session, use a coding agent to turn cases you want to check in CI into repository tests. See the Mo quickstart and accepted bug follow-up.

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Use AI throughout the lifecycle

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Author with product context

Install the coding agent skills so your coding agent knows how to add and edit tests. Connect the MCP server so the agent can combine repository context with a live browser or mobile session, execute steps, and inspect artifacts while it works. Building with AI covers both in two commands. With the momentic-spec skill, the agent captures the intended behavior of a change as tests before it writes the product code. This puts test development in the same loop as product development, so coverage changes with the product.

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Run

Run tests locally, in coding-agent sandboxes, in CI, or on a schedule. Use the full suite for scheduled runs and bug bashes. For pull requests, AI test selection uses the code diff, local code index, and optionally the App graph to choose a representative set of web tests. This shortens feedback without manually maintained dependency rules. During execution, Momentic uses semantic element resolution and transient recovery to tolerate routine UI variation. Deterministic replay and step caching keep successful paths fast, while AI resolves described elements and evaluates AI checks. See Performance for latency benchmarks, parallelism, and sharding.

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Maintain

When a test fails, Momentic starts with the least invasive response and escalates only when needed. It can re-resolve a locator, recover from a transient condition, classify the failure, propose and verify a permanent repair, or quarantine a persistent flake. This keeps maintenance in the development loop and leaves durable changes reviewable in Git.

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Turn runs into reusable insights

Each run adds information that Momentic uses to make future AI decisions more consistent and model how tests exercise the product. These systems provide reusable context about test execution: coverage across the product, journeys a change puts at risk, and information the next agent needs before acting. See How Momentic works for how preset steps, AI actions, custom code, and CLI agents fit together.