Functionize sells a platform. agent-qa is the QA agent that learns inside your workflow.

Yes, and the reason is structural rather than a feature count. Functionize is an enterprise AI testing platform where authoring, execution, and AI adaptation happen in the vendor's cloud, which means the asset you are building lives on their side of the line. agent-qa is a source-available QA agent with no paid tier, governed by FSL-1.1-ALv2: tests are plain-English YAML in your repository, runs execute on your laptop, in your CI, or from your coding agent, and every run writes back into memory committed beside the tests. The suite gets better at your app whether or not you renew anything.

Functionize is priced on enterprise platform contracts, so the bill tracks how much you test. agent-qa has no paid tier, no seats and no platform fee; FSL-1.1-ALv2 governs permitted use. You pay for the model tokens and infrastructure you already control, on the provider you choose, and the validated action cache takes roughly 60% of the tokens off a matched rerun. Adding coverage does not add a line item.

You are re-describing intent, not porting code, which is why this is far smaller than a normal test migration. Inventory the journeys your Functionize suites cover and restate each as an agent-qa plain-English test; the AI adaptation you relied on comes along, but inspectable this time. Run npx agent-qa init, write each critical flow as a plain-English YAML test, and let the runtime work out the selectors and the recovery. Most teams move a smoke suite in an afternoon, and there is nothing to un-pick later because the output is files in your own repository.

Yes, and from the same file. agent-qa runs end-to-end tests on web, Android and iOS with one natural-language format, one memory store and one evidence model, so a flow written once survives being pointed at another surface. agent-qa's mobile support uses the same YAML contract and CLI as web, so one skill set covers both surfaces.

Both adapt when the UI changes. The difference is transparency and ownership: agent-qa re-plans from natural-language intent, records what changed in file-backed memory, and caches the corrected plan, all in artifacts your team can audit rather than a platform's internal state.