What is AI-native testing?

Most test automation uses AI for one isolated task, such as generating a test or healing a locator. AI-native testing uses agents throughout the development lifecycle: authoring coverage, choosing and executing tests, maintaining the suite, and learning from every run. Momentic connects those stages through tests that remain readable, reviewable YAML files in your repository. AI reduces the work at each stage without taking away control. The result is faster development, more reliable coverage, and run data that can explain product risk instead of only reporting pass or fail.

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Use AI throughout the lifecycle

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Author with product context

Install the Momentic skill 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. For web projects, the Explore agent can bootstrap coverage for the whole application or identify and cover journeys affected by a code change. This moves test development into the same loop as product development instead of leaving coverage as a separate, manual follow-up.

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Run intelligently

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 and app graph to choose a representative set of web tests, shortening feedback without relying on manually maintained dependency rules. During execution, Momentic uses semantic element resolution and transient recovery to absorb routine UI variation. AI is involved where judgment helps; successful paths remain fast through deterministic replay and step caching. See Performance for latency benchmarks, parallelism, and sharding.

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Maintain automatically

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

Every run produces more than a result. Momentic reuses what it learns to make future AI decisions more consistent and to build a model of how tests exercise the product. Together, these systems turn test execution into reusable context: where the product is covered, which journeys a change puts at risk, and what the next agent should know before it acts. See How Momentic works for how preset steps, AI actions, custom code, and CLI agents fit together.