How it works, part 1: the translation layer

A developer outlines a "translation layer" architecture that exposes existing software functionality to AI agents as discrete, structured operations rather than through human-oriented UIs. The layer sits between a product's existing APIs, database and workflows and the agent-facing interface, so the product is not rebuilt and its UI can evolve without breaking agent workflows. The writeup argues this avoids agents burning tokens parsing pixels, and teases a follow-up on permissions and approvals.

Most software already has everything an agent needs. Dashboards, forms, search, records, workflows, integrations — the functionality is all there. The problem is how it's exposed: through a UI built for human eyes. That's where a translation layer comes in. It sits between the product and the AI interfaces that want to use it. On one side, it speaks to the existing product — its APIs, its database, its workflows. On the other, it speaks a language agents understand: structured actions with defined inputs and outputs. Instead of the agent loading a page and hunting for a button, the layer describes "create an invoice" or "search for a record" as a discrete, reliable operation. Nothing gets rebuilt. The product keeps working exactly as it does today for human users. The translation layer adds an agent-usable interface on top of what already exists. That separation matters. The UI can evolve without breaking agent workflows, and agents stop burning tokens parsing pixels they were never meant to read. Tomorrow: what happens when an agent shouldn't be allowed to do everything — permissions and approvals. What part of your own product would you hand an agent first?