Intelligence Engine Applications & Developer Ecosystems
Synthetic User Testing with LLMs
LLM agents can simulate users walking through flows, clicking, and reporting friction before real users do. Synthetic testing finds obvious breakage fast and cheaply, but it is not a substitute for real user research: agents do not feel confusion, and they follow scripts. Use it to catch regressions and expand coverage, then validate with humans.
Points clés
- Define personas and goals as prompts, not just click scripts.
- Run agents against real or staging environments, not mocks only.
- Capture screenshots, logs, and transcripts for each run.
- Compare results across model versions to detect drift.
- Treat synthetic findings as hypotheses to confirm with humans.
- Cover happy paths, error paths, and accessibility.
- Budget for false positives; not every agent complaint is real.
- Keep tests deterministic enough to track regressions over time.
Questions fréquentes
Can synthetic testing replace user research?
No, it complements it by catching breakage and expanding coverage cheaply.
Which tools work best?
Browser automation plus an LLM agent; Playwright and similar frameworks handle the driving.
How do I reduce false positives?
Calibrate with known-good flows and require reproducible failures.
Is it worth the cost?
For regression coverage on critical flows, yes; for discovery, pair with real users.
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