Key takeaways

Autonomous AI browser agents generate a new category of web traffic that operates with sparse, highly economical behavioral telemetry.

Traditional bot detection models fail to evaluate these agents because more than 98% of their requests contain little to no mouse movement.

Akamai developed a proof-of-concept Masked Autoencoder Transformer model that treats mouse telemetry like language tokens to analyze short event sequences.

A lightweight classifier trained on transformer embeddings successfully distinguishes agentic automation from sparse human sessions, achieving high accuracy despite extreme class imbalance.

A new kind of visitor is browsing your website — and it doesn't move the mouse the way you'd expect.…