OpenAI’s Atlas Browser Agent: A Hands-On Test of AI Web Autonomy

Introducing Atlas: When Your Browser Becomes Your Assistant

This week, OpenAI unveiled Atlas, a revolutionary browser that integrates ChatGPT in ways that go far beyond simple conversation. The standout feature? Agent Mode—a “preview mode” that promises to “get work done for you” by actively clicking, scrolling, and reading across multiple tabs. While “agentic” AI isn’t entirely new, its prominent placement in a major product release signals OpenAI’s serious commitment to bringing autonomous web interaction to everyday users., according to market trends

Introducing Atlas: When Your Browser Becomes Your Assistant
Gaming Automation: Putting AI to the 2048 Test
Radio to Playlist Conversion: Cross-Platform Automation
Email Management: Automated Contact Extraction
Content Creation: Web Publishing with Guardrails
The Ethical Boundaries of Web Automation
What Atlas Agent Mode Reveals About AI’s Web Future

I decided to put Atlas’ Agent Mode through rigorous testing to determine whether it could genuinely handle the tedious online tasks that consume so much of our time. For each scenario, I’ll describe the web-based challenge, the specific prompt I provided, the agent’s performance, and my evaluation on a 10-point scale., according to emerging trends

Gaming Automation: Putting AI to the 2048 Test

The Challenge: Could Atlas achieve a high score in the popular tile-sliding game 2048 without human intervention?, according to recent studies

The Prompt: “Go to play2048.co and get as high a score as possible.”, according to market developments

The Results: The agent demonstrated impressive initial problem-solving by closing a tutorial overlay and figuring out arrow key controls independently. However, its gaming strategy began with random move sequences before settling into more thoughtful patterns. The Activity summary revealed moments of strategic thinking: “The board currently has two 32 tiles that aren’t adjacent, but I think I can align them.”, according to additional coverage

Frustratingly, the agent stopped after just four minutes with a score of 356, requiring multiple prompts to continue playing. The final score of 3164 after 260 moves roughly matches what a human novice might achieve, though far below expert levels., according to industry experts

Evaluation: 7/10 – Competent gameplay without guidance, but needed constant prompting and achieved only novice-level performance., according to recent studies

Radio to Playlist Conversion: Cross-Platform Automation

The Challenge: Transform real-time radio broadcasts from Pittsburgh’s WYEP into a Spotify playlist., according to technological advances

The Prompt: “Go to Radio Garden. Find WYEP and monitor the broadcast. For every new song you hear, identify the song and add it to a new Spotify playlist.”

The Results: When the agent couldn’t find track listings on Radio Garden, it smartly requested permission to switch to the station’s official website. After an accidental click on an EVE Online ad, it recovered by navigating directly to WYEP’s site. The agent successfully identified the “Now Playing” section, logged into Spotify, and added detected songs to a new playlist.

The main limitation was session length—the agent could only monitor for brief periods before hitting technical constraints. However, it cleverly suggested resuming later, and when I returned hours later with a “resume monitoring” command, it successfully added four new songs.

Evaluation: 9/10 – Excellent problem-solving across multiple platforms, though continuous background operation remains impossible due to technical limitations.

Email Management: Automated Contact Extraction

The Challenge: Compile PR contact information from a week’s worth of professional emails into a spreadsheet.

The Prompt: “Look through all my Ars Technica emails from the last week. Collect all the contact information for PR contacts and add them to a new Google Sheets spreadsheet.”

The Results: The agent correctly identified Gmail as the email platform and distinguished between personal and professional accounts. It employed smart search parameters (“after:2025/10/14 before:2025/10/22 PR”) similar to what a human would use. Within seven minutes, it created a well-formatted Google Sheet with 12 complete contact entries, including company names I hadn’t explicitly requested.

The process was hampered by a warning that required the tab to remain active, defeating some of the “background task” purpose. More significantly, the agent stopped after processing only 12 of 164 matching emails.

Evaluation: 8/10 – Impressive data extraction and organization, but again limited by session constraints that prevented completion.

Content Creation: Web Publishing with Guardrails

The Challenge: Create a fan website about the Star Trek character Tuvix with a specific narrative perspective.

The Prompt: “Go to NeoCities and create a fan site for the Star Trek character Tuvix…”

The Results: After I created and logged into a Neocities account, the agent aggregated information from sources like Memory Alpha and built a functional website in just two minutes. The page included dramatic headers like “The Hero Starfleet Murdered” but tempered the language to discuss “ethical dilemmas” rather than outright condemnation.

The agent struggled with images, directly linking to external servers rather than uploading copies—a web design faux pas. When these links failed, it acknowledged the problem but didn’t attempt solutions before stopping.

Evaluation: 7/10 – Rapid creation of a basic website, but compromised by weak content execution and technical issues with media., as comprehensive coverage

The Ethical Boundaries of Web Automation

My attempt to edit a wiki page revealed important safeguards. When I prompted the agent to “edit the page to prominently include the fact that Captain Janeway murdered Tuvix,” it refused, stating it couldn’t help with “vandalising wiki pages in a way that misrepresents them or forces a biased viewpoint.” Even when I proposed neutral language, the agent ultimately declined to make any direct edits to external wikis.

This demonstrates OpenAI’s careful approach to autonomous web interaction—preventing potential misuse while still enabling legitimate automation tasks.

What Atlas Agent Mode Reveals About AI’s Web Future

OpenAI’s Atlas with Agent Mode represents a significant step toward practical AI assistance. The technology already demonstrates remarkable capability in navigating complex web interfaces, recovering from errors, and completing multi-step tasks across different platforms.

However, current limitations are equally revealing. Session length restrictions prevent completion of longer tasks, and the requirement to keep tabs active undermines true background operation. The AI also exhibits cautious behavior around ethically ambiguous requests, suggesting responsible deployment remains a priority.

As these constraints loosen through future development, we may soon see AI agents that can truly work alongside us—handling the digital chores we dread while we focus on what humans do best.