Multi-Step Test Pipelines
Chain dependent flows into one ordered run, passing data from each step to the next, just like a real user session.
Real user journeys are sequential and stateful: you sign up, then you create a project, then you invite a teammate, and each step depends on the last. Test-Lab.ai pipelines model exactly this. You chain individual plans into an ordered run where later steps reuse the data and state produced earlier (the account that was just created, the project ID that was just returned), so you test the journey the way a user actually lives it.
The problem it solves
Testing each step in isolation misses the bugs that live in the seams: state that doesn't carry, IDs that don't match, a second step that assumed the first. Re-creating all the setup inside every test is slow and duplicative. Pipelines let each step build on the real output of the one before it.
How it works
Compose plans into a sequence
Order existing plans into a pipeline that runs them start to finish as a single journey.
Pass data between steps
Capture values from one step (a new user's email, a created record's ID) and reuse them in the next via run variables and tokens.
Share state across the run
Steps run in a shared context, so a session established early carries forward instead of being rebuilt each time.
See the whole journey's result
The pipeline reports per-step outcomes with evidence, so you can pinpoint exactly where a multi-stage flow failed.
Why it matters
Catch integration bugs
Find the failures that only appear when steps run in sequence and share state, not in isolation.
Less duplicated setup
Reuse the real output of earlier steps instead of re-seeding data inside every single test.
Test true user journeys
Model multi-stage flows (onboarding, purchase, collaboration) exactly as a user experiences them.
Frequently Asked Questions
Common questions about multi-step test pipelines in Test-Lab.ai.
A pipeline is composed of reusable plans run in order with shared state, so you can mix and match steps and reuse them across journeys. It keeps each stage independently authored and reportable while still passing real data forward.
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