Hello Devs 👋
Over the last few months, AI agents have been evolving very quickly. We are no longer building simple chatbots that answer questions. Today, agents browse websites, execute workflows, monitor dashboards, interact with SaaS platforms, and perform actions that previously required manual work.
Recently, , which immediately caught my attention. Products often receive attention during launch periods, but reaching the number one spot usually means people see a real problem being solved rather than just another feature release.
? Is it simply another browser automation tool, or is there something different about it?
Instead of reading documentation and feature lists, I decided to test it myself and understand what problem it is trying to solve.
In this article I will walk through what ?
The output lists available browser profiles that BrowserAct can use during execution. These profiles become useful when creating isolated browser environments, preserving state across sessions, or reusing existing login contexts.
Next, I created a browser using my existing Chrome profile.
browser create \
-- type chrome \
-- source-profile \
local_profile_182885126174998716 \
-- name "browseract" \
-- desc "testing-browseract"
Output:
# Example Domain
This domain is for use in documentation examples without needing permission.
[Learn more](https://iana.org/domains/example)
The result was returned as clean markdown without writing selectors, parsing HTML, or creating custom scraping logic.
Next, I wanted to see how BrowserAct handled an interactive browser session.
Second Test: Opening a Browser Session
I opened a browser session using the browser profile I created earlier.
browser-act \
--session first-test \
browser open \
chrome_local_102863481715294440 \
https://github.com
This allows interactions to happen using element references rather than manually creating CSS selectors.
To test this further, I clicked search button:
browser-act \
--session first-test \
click 6
Although starring a GitHub repository is a simple action, it demonstrates an important capability. The agent was able to open a real website, maintain browser state, interact with UI elements, and complete an action inside an authenticated environment.
This feels much closer to real-world AI workflows than isolated browser automation scripts.
Fourth Test: Human Handoff with an AI Agent
One feature that interested me most was human handoff.
Many automation workflows eventually reach a point where the process cannot continue without human participation. Common examples include OTP verification, QR login, enterprise SSO approval, and security confirmations.
To test this behavior, I used Codex together with BrowserAct and created a workflow that required OTP verification.
My objective was straightforward:
- Let the agent open the login page
- Enter the email automatically
- Pause when OTP is required
- Preserve browser state
- Resume execution after human input
I provided the following prompt to the agent:
Use BrowserAct for this workflow.
Open: https://practice.expandtesting.com/otp-login
Actions:
1. Launch BrowserAct
2. Open the website
3. Continue the login workflow
4. If human interaction becomes necessary for OTP, preserve browser state and use BrowserAct's collaboration capability
5. Resume execution after collaboration completes
When the workflow reached the authentication stage, BrowserAct detected that human interaction was required and generated a collaboration link.
The interesting part was that the browser state remained active throughout the interruption. The agent paused execution, handed control to a human when necessary, and then continued from the exact same session once the required action had been completed.
For production workflows involving authentication, verification, or approval steps, this feels significantly more practical than forcing complete automation.
Fifth Test: Running Multiple Browser Sessions
Another scenario I wanted to test was running multiple independent browser sessions.
Long-running AI systems rarely perform a single task. They may monitor dashboards, analyze feedback, review customer activity, and collect information simultaneously.
I created several browser sessions locally.
browser-act --session reviews browser open chrome_local_102863481715294440 https://reddit.com
browser-act --session ops browser open chrome_local_102863481715294440 https://status.openai.com
browser-act --session community browser open chrome_local_102863481715294440 https://dev.to
I then listed active sessions:
browser-act session list
The output showed multiple active sessions operating independently.
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