Writing code stopped being the bottleneck of my day a long time ago.
Now it is remembering what six agents were doing when I left for lunch.
The entire category of AI coding agent tools is solving the same problem: how to run several at once without them getting in each other's way. It is a real problem and it is largely solved.
It is just not the expensive one. Opening six AI agents takes thirty seconds. The hour that follows is what costs.
What actually happens with six AI agents open
One finished twelve minutes ago and is sitting there waiting for you to approve. Another stalled on an API call and went quiet, and quiet looks like work. A third asked a question you did not see because you were watching the fourth. And the fifth, you no longer quite remember what you asked for.
None of that is an execution failure. All six worked perfectly. What broke was your ability to keep up, and that is the bottleneck, not the machine.
The maths is simple: the gain from running six agents is capped by how many you can actually follow. If you can follow two, opening six produces the output of two plus four pieces of work nobody checked.
Why reading terminals does not scale with several AI agents
The standard way to know what an AI agent is doing is to read its output. That works with one. With six, you spend the day scanning text to rebuild in your head a state the machine already knows.
CanvasCode inverts that. Every AI agent carries one sentence about what it is working on, written by itself, plus a visible state: working, waiting for you, finished, crashed. You glance at the canvas and know where things stand, without reading anything.
How to know when an AI agent is waiting for you
The part that changes the day is the notification. When an AI agent needs you and you are in another project, the alert says which agent, in which project, and one click takes you straight to its panel with the context already on screen.
When an agent goes down because the provider's API failed, the app notices and brings it back on its own. Most outages resolve without you finding out there was one.
How to resume an AI agent without re-explaining
The worst moment of the day is coming back from an interruption. You remember the subject, not the state.
Here you say out loud that you were working on login security, and the app finds the right agent, summarises what it did, and resumes that same conversation with all the earlier context. It is not a fresh session asking you to tell the whole story again.
And closing an agent does not kill it: it dismisses it. It comes back by name, whole, with the history of what it has delivered. Each stored delivery opens the context of that moment, not just what it did but what it said about it.
Why almost no tool addresses continuity
Because parallelism is easy to demo and continuity is not. A screen with eight panels running at once is impressive in ten seconds of video. Not losing the thread at four in the afternoon on a Thursday only impresses people who have lost it.
That is where we chose to play.
I wrote the full version, with the parts that did not fit here:
https://canvascode.app/en/news/how-to-keep-track-of-multiple-ai-agents
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