This is first post in a series about the design and implementation of Bytebot . Give us a star on our open source repo .
We’re still in the early innings of AI agents. There are hundreds of companies building wrappers around LLMs, trying to make them more useful; more tool-aware, more stateful, more capable of completing tasks across applications. But most of them are barking up the same tree: they’re building agents that work by connecting APIs and tools in structured ways.
Bytebot was born out of a fundamentally different belief: that the simplest and most universal abstraction for agent control already exists, and we’ve been using it for decades.
The Agent as Remote Worker
Here’s the core idea: give an LLM access to a keyboard, a mouse, and a screen. Nothing more.
That’s it. That’s the interface. That’s what a human remote worker uses. And it’s the only interface you need to approximate the vast majority of digital work.
Why does this work? Because nearly all software, all workflows, and all enterprise tooling has been designed (whether explicitly or implicitly) for a human sitting at a computer. If we can simulate the inputs of a human worker and read the same outputs (screen pixels), we can plug into the same workflows. No custom integrations required.
This approach isn’t just simpler - it’s more robust, more generalizable, and more future-proof.
We Tried the Other Way First
Before the current version of Bytebot, we built it as a browser agent.
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