Three approaches to QA
Scripted test automation
People write test code, often with frameworks for browsers, APIs or units, that performs fixed steps and checks fixed expectations. It runs in CI on every change. It is the backbone of most mature engineering teams, and for good reason.
AI-assisted testing
AI helps people create or maintain tests. Typical examples are generating a test from a recorded session, suggesting test cases from a user story, or repairing a locator when a button moves. A person is still in charge of what gets tested and reviews the output.
Agentic QA
AI agents decide what to test, run the checks and judge the results on their own, based on context such as requirements, design systems and business rules. People set goals and limits and make the final call. We cover how this works in our guide to agentic QA testing.
Scripted vs AI-assisted vs agentic QA
| Aspect | Scripted automation | AI-assisted testing | Agentic QA |
|---|---|---|---|
| Who decides what to test | Engineers, in advance | People, with AI suggestions | Agents, per change, within set limits |
| What people write | Test code | Prompts, reviews, edits | Context: requirements, design, rules |
| Speed per run | Very fast | Fast once tests exist | Slower; agents explore and reason |
| Repeatability | Highest; same steps every time | High for generated scripts | Needs verification to be trustworthy |
| When the UI changes | Scripts may break and need fixes | AI can help repair | Checks are re-planned from context |
| Brand-new features | Uncovered until tests are written | Faster to write tests | Checked against requirements |
| Unclear requirements | Encoded as whatever the author assumed | Depends on the reviewer | Can be flagged as needs input |
| Main risk | Maintenance cost, coverage gaps | Over-trusting generated tests | Unverified AI claims |
| Best for | Critical, stable flows | Teams scaling an existing suite | Broad coverage without script upkeep |
Want to see which mix of scripted, AI-assisted and agentic QA fits your product? Start with a free 45-day trial, no card.
Work email only. We keep your email, team size, plan choice and the page you joined from, only to contact you about the ShipperAG pilot. No spam.
When scripted test automation is the right choice
If a flow is critical, stable and must behave identically every time, a script is hard to beat. Payment calculations, authentication and core APIs are good examples. Scripts are fast, cheap per run, deterministic and easy to reason about when they fail.
The costs show up elsewhere: someone has to write every test, keep it working as the product changes, and notice what nobody wrote a test for. Suites also tend to encode what the code did when the test was written, which is not always what the product was supposed to do.
When AI-assisted testing is enough
If you already have a healthy suite and a team that owns it, AI assistance can make that team faster: drafting tests, suggesting edge cases and reducing locator churn. The main watch-out is trust. A generated test that passes may be checking the wrong thing, so review stays essential.
When agentic QA helps most
- Change is outpacing your tests. Frequent releases, whether the code is written by hand or with AI coding tools, add more change than most suites keep up with.
- Short-lived features and sites. A full suite rarely pays back on something that changes shape every few weeks.
- Wide quality areas. Accessibility, visual design, security and business rules on every release, not only at audit time.
- You need evidence, not just green ticks. A report of what was verified, what failed and what was not covered.
Its weak spots are real. Agentic runs are slower and less predictable than scripts, and an unverified AI claim is worthless. That is why ShipperAG never treats a model's opinion as proof and uses an independent verifier to re-run every finding.
Using them together
For most teams, the practical answer is a blend:
- Keep scripted tests for your most critical, stable flows.
- Use AI assistance to maintain and extend that suite if it helps your team.
- Add agentic QA for broad coverage of every change, new features and areas nobody has scripted.
Regressions are where the blend pays off first: a suite proves that what someone wrote down still works, while AI regression testing chooses its scope from the change itself and reaches the areas nobody scripted.
ShipperAG is designed to run in your own CI next to your existing tests, and its AI QA agents can report where your suite already covers a flow and where it does not.
Questions to ask any QA tool, including ours
- When it says "passed", what check actually ran, and can I see the evidence?
- Are findings reproduced before they are reported?
- Does it tell me what it did not cover?
- What does it do when requirements are unclear or contradictory?
- Can the thing that fixes code also approve its own fix?
- Where does my code run, and where do my secrets go?
If you want the plain-language version of the autonomy trade-off, read automated vs autonomous QA testing. For the same decision framed around one framework, see agentic QA vs Playwright.