One of the most common questions we get from enterprise customers is: "What happens when a website changes?" It's a fair question. Traditional automation tools break constantly—a small CSS change can bring entire workflows to a halt.
Our answer is what we call "codified learning." Every interaction our agents have with a website contributes to a growing understanding of how that site works. When something changes, the system adapts automatically, drawing on everything it's learned before.
Here's how it works:
First, we separate the "what" from the "how." When you tell a TinyFish agent to "add this item to cart," you're expressing intent, not implementation. The agent figures out the specific clicks and keystrokes required to achieve that goal on any given website.
Second, we build site models. As agents interact with websites, they develop sophisticated models of how those sites are structured. These models capture not just the current state, but the patterns of how sites typically change.
Third, we enable continuous improvement. Every successful (and unsuccessful) interaction feeds back into our systems. Agents literally get smarter over time, learning from each other's experiences across our entire customer base.
The result is reliability that actually improves as scale increases. Our largest enterprise customers run millions of agent interactions per day across thousands of websites. The system learns from every single one.
This is the opposite of traditional automation, where scale increases fragility. With TinyFish, scale increases robustness.
We call it codified learning because the insights aren't trapped in a single agent's memory—they're systematically captured, validated, and shared across the entire platform.
That's how you build enterprise-grade AI agents.