Yes and no: can AI build a web browser? A weeklong Cursor experiment used hundreds of coordinated AI agents to generate millions of lines of code and a rough, working prototype. It renders simple pages and keeps going without human babysitting, but it lacks the features, safety, and polish that real users expect from Chrome-class browsers. The project showed what happens when you give modern agent models long, uninterrupted runs and a clear structure. It also showed the limits. Building a browser is not only about parsing HTML. It is about security, stability, and thousands of edge cases that take years to get right.

Inside the experiment: from idea to prototype

A week of autonomous agents

Cursor’s CEO, Michael Truell, and a small team ran hundreds of GPT‑5.2 agents for a full week. The agents produced more than three million lines of code across thousands of files. The goal was ambitious: build a new browser, including a rendering engine and core systems, starting from zero. The team used OpenAI’s GPT‑5.2 model because it was built for long, autonomous tasks. According to the team, the model stayed on topic, completed multi-step work, and kept making progress without stopping for human fixes. In side-by-side efforts, GPT‑5.2 also held focus better than Claude for long, complex tasks.

Why hierarchy mattered

At first, the agents stalled. When all agents had equal status and no leader, they hesitated, debated, or made tiny, safe edits. The fix was a simple hierarchy with clear roles:

Planners defined what to build and broke work into tasks.

Workers wrote code and implemented features.

Judges reviewed progress and decided whether to continue or adjust.

This structure stopped the drift. It let the swarm move forward in tight cycles. The system did not pause after errors; it debugged and tried again. That is rare in real projects, where humans often need to step in to unblock issues.

Can AI build a web browser today?

The short answer: partly. The agents produced a minimal browser that renders basic pages quickly and mostly correctly. The codebase includes an HTML parser, CSS cascade and layout code, text shaping, painting, and even a simple JavaScript virtual machine. That is real progress, not a demo with smoke and mirrors. But the team is clear: “It kind of works.” Modern browsers are enormous. Chromium alone has more than 35 million lines of code. The AI’s code is not even close to 10% of that. The difference is not just size. It is years of bug fixes, standards updates, performance tuning, security patches, and crash handling under heavy load. The experiment suggests the answer to “can AI build a web browser” is a cautious “yes, in prototype form.” It can get you a base that displays pages. It cannot replace mature engines like Chromium or WebKit.

What the prototype actually does

Core pieces that landed

The public code shows core building blocks:

HTML tokenizing and parsing.

CSS cascade, layout calculations, and box model logic.

Text shaping and painting to the screen.

A custom JavaScript virtual machine with basic execution support.

These parts are enough to render simple pages. They also show that the agents did not only stitch together libraries. They wrote a large amount of glue code and subsystem logic.

Why this matters

Rendering with acceptable speed and correctness is a milestone. It means agents can carry a long, multi-module software effort over the line without a human writing every step. It also means teams can start to rely on agents for dirty, repetitive, and detailed tasks that slow humans down.

Where the hard problems still live

Rendering a simple page is the easy part. A modern browser has to be safe, stable, fast, and compatible with the messy real web. Many of those pieces are missing or incomplete:

Security: isolation, sandboxing, memory safety, and site boundaries.

Compatibility: thousands of web standards and quirks across versions.

JavaScript features: complete engines, JITs, garbage collectors, and the DOM.

Extensions and integrations: password managers, sync, and policies.

Performance: multiprocess models, GPU acceleration, and power use.

Stability: crash recovery, watchdogs, and failure-handling at scale.

Accessibility: screen readers, keyboard navigation, and ARIA support.

Real users judge browsers on these things. The prototype does not offer them yet. That is why calling it “production ready” would be misleading.

The maintenance wall

Writing code is one cost. Owning code is the bigger one. The agents can sprint for a week and ship millions of lines. But then the long tail begins:

Fix hard bugs with subtle side effects.

Reduce duplication and dead code.

Add tests and coverage for tricky edge cases.

Keep up with evolving web standards.

Review and explain decisions for future maintainers.

Human engineers usually carry this load. It is unclear how well agents will manage the slow, boring, quality grind over months and years. One developer who browsed the repo said it was hard to find core pieces like the DOM or the JavaScript engine layout. That points to discoverability and architecture issues that hurt long-term maintenance.

Human guidance still sets the path

The agents did not invent a browser plan on their own. Humans created the goal, the phases, the roles, and the acceptance checks. Humans also curated blockers and reset loops when needed. This is not a negative. It is how teams work. But it does shape how we answer the bigger question: can AI build a web browser without human guidance? Not yet. It is also worth noting the data question. Browsers are widely documented. Even if no code was copied, models trained on public text will echo decades of browser design and standards. That is helpful for speed, but it blurs the line between new creation and learned reconstruction.

Agent orchestration: lessons teams can use now

Simple rules beat loose swarms

The hierarchy of planners, workers, and judges is a clear win. It reduced indecision and safe, trivial edits. If you plan to use agents on big codebases, start with roles, not with a flat swarm.

Progress gates keep focus

Judges that ask, “Is this good enough to continue?” create momentum. They also stop infinite, low-value looping. Add numeric gates, too:

Unit test pass rates.

Static analysis errors and warnings.

Performance budgets per module.

Crash or error thresholds under stress tests.

Long runs need guardrails

Agents that run for days need clear scopes and hard stops:

Define a crisp “definition of done” per milestone.

Cap file count and code size per week.

Force checkpoints for architecture and naming.

Require traceable commit messages and summaries.

These rules make large outputs easier to review and keep. They also reduce the cleanup cost later.

How to judge claims like this

Not every flashy demo is equal. Use a simple checklist before you trust results:

Is the repo public, buildable, and runnable on a clean machine?

Are there tests, sample pages, and reproducible benchmarks?

Can you trace who (agent vs. human) wrote key parts?

Is there documentation for the architecture and module layout?

Do claims compare against known baselines (e.g., simple WebKit tests)?

When you ask “can AI build a web browser” in a practical sense, these checks turn hype into proof.

What this means for engineering leaders

You can treat agents as force multipliers for structured work. They can:

Create scaffolding for new modules at speed.

Draft parsers, serializers, and glue code.

Write tests from specs and logs.

Run targeted refactors with guardrails.

But you should keep humans in the loop for:

Architecture and system boundaries.

Security models and threat reviews.

API design and long-term maintainability.

Performance budgets and production readiness.

Plan budgets for the slow parts: documentation, refactoring, and test coverage. Those are the parts that make software last.

What’s next for multi-agent coding

Cursor plans to bring this orchestration model into its main product. Expect more tools that let you define roles, goals, and gates, then run agents for long stretches. Expect better dashboards for progress, regressions, and test results. Also expect tighter integrations with version control, CI, and issue trackers. Will this replace expert teams? No. It will likely change workflows. Engineers will review, nudge, and design. Agents will explore, implement, and grind through details. The best results will come from clear goals, strict guardrails, and honest metrics.

Reality check: ambition versus value

A week of agents produced a big, interesting codebase. It proved that long autonomous work is possible and useful. It also proved that the last mile to “production browser” is massive. Many teams will get more value by aiming agents at narrower, boring tasks with high payoff:

Porting modules across languages or frameworks.

Building internal renderers or viewers with limited scope.

Generating test suites from logs and bug reports.

Automating documentation and dependency audits.

These wins are practical, lower risk, and easier to maintain.

Bottom line

The experiment is a milestone and a caution. It shows speed, scale, and self‑recovery that would have been unthinkable a few years ago. It also shows how much of a modern browser sits outside basic rendering. Security, stability, performance, and compatibility are still human-led marathons. So, to the question can AI build a web browser, the best answer today is: it can build a credible prototype that renders simple sites, and it can keep working without constant human rescue. Turning that prototype into a secure, fast, and reliable browser that people trust is still a long road—and that road will need skilled humans guiding capable agents every step of the way.

(Source: https://www.finalroundai.com/blog/cursor-ceo-browser-made-using-ai)

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