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The Spatial Harness: Bringing Agents to the Canvas — Max Drake, tldraw

Source
youtube.com
Author
AI Engineer
Date

Context

An working on a drawing canvas needs both a view of the picture and a way to act on its objects. The tldraw talk describes supplying a screenshot alongside the canvas's structured JSON, then extending the agent with its own task list and control over its viewport. Moving that viewport lets it look elsewhere on the canvas as work continues. The design addresses a concrete interface problem: useful spatial work requires access to the visual arrangement, the underlying objects and the area the agent is currently inspecting.

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.
  • multi-agent — Using several AI agents on one problem — splitting work in parallel, checking each other, or filling different roles like planner and reviewer.
Chapters
  • 0:00A demo kicked off before the talk begins
  • 1:08tldraw, the app and the SDK behind other canvases
  • 4:14Why coding agents work and canvas agents struggle
  • 5:08Teaching a model to read and act on the canvas
  • 7:01The agent starter kit goes agentic
  • 8:26Fairies, multiplayer agents you can see working
  • 9:49A group chat of agents with an orchestrator
  • 12:06One node per coding agent, in a dependency graph
  • 15:48Code mode, a window manager, and Pong
  • 18:05The canvas as a place to work with agents
Read the source www.youtube.com
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