Completed browser-agent experiment
The Map of
Browser Agent Testing
A short route from “the driver finished” to “the product is actually correct.”
Use adaptive tools for exploration, then verify their outcome independently. No overall replacement has qualified yet.
Browse all 54 tools · Read the eight experiment records
Source: the runner, adapters, experiment records, and replay instructions are public on GitHub.
Clone
Give the agent the runner
Keep the shared runner beside your application. Do not copy it into the product.
gh repo clone sarthakagrawal927/agent-testing
cd agent-testing
npm test
Prove it
Run the built-in smoke benchmark
The fixture demonstrates warm and cold runs, bounded phases, sanitized receipts, and a separate correctness oracle.
node bin/agent-testing.mjs validate \
--manifest fixtures/good.manifest.json
node bin/agent-testing.mjs run \
--manifest fixtures/good.manifest.json \
--out artifacts/quickstart
node bin/agent-testing.mjs summarize \
--receipt artifacts/quickstart/receipt.json
Adapt
Add one real product journey
Create adapters/<your-product>/ with a strict manifest and bounded commands for reset, readiness, workflow, and verification.
adapters/your-product/
├── README.md
├── search.playwright.manifest.json
├── run-search.mjs
└── verify-search.mjs
Verify displayed values, persisted backend state, reload or relaunch behavior, and selected screenshots outside the driver.
Compare
Use the tool that fits the job
| Job | Use now | Boundary |
|---|---|---|
| Repeat web changes | Playwright | Saved workflows remain the qualified baseline. |
| Explore web behavior | Jev + verifier | Search worked 5/5; password login is unsupported. |
| Replay a semantic action | Stagehand cache | Promising 4/4 screening result; not a 2× win. |
| Repeat iOS changes | Maestro | AXe was faster in login screening but not reliable enough overall. |
These are Vaultwealth screening results, not universal rankings. Sample sizes are preliminary and prepared workflows are not equivalent to unfamiliar exploration tasks.
Adopt
Raise the bar only after screening
Start with five warm runs. Promote promising candidates to 20 warm and three cold runs per journey.
At least 2× faster, or 50% less model usage without slower execution—plus no missed seeded defects and no observed reliability regression.