Anatomy of a test
A Momentic test is a sequence of steps. There are many step types: some rely on AI, while others can be configured deterministically or execute code.Human in the loop and agentic
Mix preset steps, AI actions, and module calls within one test. Modules can also
combine preset steps and AI actions. This gives you the best of both worlds:
exact, reviewable control where the path matters and agentic flexibility where
the goal matters more than the path. Preset steps can still use AI to locate
elements, evaluate assertions, or extract data while keeping the sequence
explicit.
See Test structure for the YAML format, variables,
and modules. See AI action for goal-driven
flows.
Code integration
Web tests run on Playwright and mobile tests run on Appium. Use preset steps for common interactions, then drop to code when a flow needs it:- JavaScript steps run custom code in a sandboxed Node environment or the current browser page.
- Appium steps execute Appium scripts against the current device.
- Modules and variables connect reusable setup, application state, and data across the flow.
Run locally and in CI
A test provides immediate feedback during development and repeatable verification after a change leaves a developer’s machine. The same test files and CLI work in both loops:
Use
momentic run locally or in your
CI system. AI test selection
keeps pull-request feedback focused, while
AI test maintenance turns eligible failures
into recovery, diagnosis, and reviewable repairs.
CLI-based agents
Momentic exposes its AI agents through the CLI so they can be composed in CI, scripts, and custom agent loops:
Commands can emit structured output and operate on repository files or saved
results, so your orchestrator decides when they run and what happens next. See
the
momentic ai reference and
MCP server.
Self-learning system
Momentic carries useful context forward instead of treating each run as an isolated event:- The step cache reuses successful element resolutions for fast, deterministic replay.
- Memory retrieves relevant decisions from earlier runs for locator and assertion agents.
- The app graph turns run traces into product journeys and coverage, powering AI test selection and product risk analysis.
- The knowledge base adds your terminology, rules, and known flows. Your team can customize it from the dashboard.