A series on how one language shaped artificial intelligence, and how intelligence is now reshaping it.
Stop Filling In the Form
Kenton Varda spent his career on the problem of how structured data moves between programs.
At Google he was the primary author of Protocol Buffers version 2, the version the company open sourced and the one the industry adopted. Later he built Cap’n Proto, a serialization format and capability-based remote procedure call system, designed around his conclusions about what the first system had got wrong. Then he became the architect of Cloudflare Workers. If there is a person whose professional life has been the question of how one program tells another program what it wants, in a form both can agree on, it is him.
His thinking over that career did not change so much as continue. Protocol Buffers is a schema system: you declare the shape of your data, a compiler generates code, and both ends agree in advance. Cap’n Proto kept the schema and moved away from rigidity elsewhere, treating fine-grained validation at the protocol level as something that constrains a system’s ability to evolve rather than as a safety feature. A person who has concluded that protocols should carry less ceremony, not more, is a person predisposed to notice when a new consumer arrives who does not need the ceremony at all.
A tool-calling protocol is a serialization format. The model forms an intention, the runtime has to receive it, and something in between must carry it in a form both ends agree on. That is the problem Varda spent his career on, and in the early 2020s it arrived in a new domain, with a new kind of consumer at one end of the wire.
In September 2025, with Sunil Pai, he shipped something called Code Mode, built on the premise that for AI agents this entire tradition is the wrong approach. Do not hand the model a schema to fill in, meaning a declared shape with named fields waiting for values. Convert the tools into an API, hand the model the API, and let it write code.
His reasoning was about what models have actually seen. The training corpus contains an enormous quantity of real code written by people solving real problems. It contains a small quantity of tool-call examples, most of them contrived. Varda put it in an analogy nobody has improved on. Asking a model to master tool calling is like sending Shakespeare on a crash course in a foreign language and then asking for a play. The result would be good, because it is Shakespeare. It would not be his best work.
How the industry arrived there, what it means that code became the interface through which machines act on the world, and one fact that complicates everything argued so far.
