Most Momentic tests are step-based: each step names a specific action (click the Sign in button, assert the dashboard loads). Step-based tests are fast, deterministic, and cacheable. Agentic testing is the opposite end of the spectrum. You give Momentic a goal, and an AI agent figures out the steps on the fly. Agentic steps are slower than step-based ones, but they thrive in situations where the exact flow isn’t predictable ahead of time.Documentation Index
Fetch the complete documentation index at: https://momentic.ai/docs/llms.txt
Use this file to discover all available pages before exploring further.
When to reach for agentic testing
- Dynamic flows where the UI changes based on feature flags, A/B tests, or user state
- High-level acceptance checks, “confirm a new user can sign up and reach the welcome screen”, without prescribing each click
- Exploratory coverage: let the agent probe areas of your app that aren’t worth a dedicated deterministic test
- End-to-end smoke tests after deploys, “complete an order”, with the agent handling whatever the current UX looks like
AI action
AI action is the primitive that powers agentic testing. It accepts a natural language goal and lets the agent drive the browser or app until the goal is complete.signup.test.yaml
Pairing with assertions
Wrap agentic steps with explicit assertions (AI check, Page check, Element check) so you always verify the outcome, not just that the agent “finished”. Agentic steps are powerful but non-deterministic, assertions keep your tests honest.Reliability tips
- Keep goals short and specific. “Sign up a new user with a fresh email” is better than “Test the onboarding flow thoroughly.”
- Provide context the agent can’t infer. If there’s an invite code, pass it in via variables.
- Add a fallback assertion right after the agentic step so failures surface with a meaningful message.
- Combine with Auto-heal and Step cache, the agent’s successful traces are cached and replayed deterministically on subsequent runs.