The GitHub Blog post argues chat works as a universal fallback because builders cannot predict what people will try, but once you know the task you want done, a purpose-built interface is usually the better fit.
In the GitHub Copilot app, a 'canvas' is a small full-stack app that runs inside the app; its server side communicates both ways with the Copilot AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → and can call third-party APIs or execute code on your machine.
The cost argument: having the agent build a tool once, so later interactions cost nothing, usually beats treating the agent itself as the tool for repetitive jobs like staging and committing, which the author calls a waste of tokenThe chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.Full definition →.
Examples include a Winget package manager UI with no AI in it and a SQLite canvas. The author says simple canvases can be one-shot, while an automated research-to-finalize workflow canvas took most of a day to get right.
Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
Why it matters
Makes the case for designing purpose-built, non-chat interfaces around specific AI-assisted coding tasks instead of defaulting everything to a chat window.
