How canvases make agentic workflows visible, steerable, and cost-efficient
figure — when I was in college, I joined the beta for one of the first versions of AI inline completions in VS Code. It felt like a game changer.
Since then, GenAI has fundamentally changed software development: hybrid teams where agents and humans work in tandem, with the developer at the center as visionary and orchestrator. We are living in that transition right now. As a natural byproduct of how fast innovation in GenAI has moved, we now have tools to help us plan, build, review, and ship code. But in the current state, many workflows still feel disjointed. Context gets lost across threads and surfaces, and too much time gets spent reviewing agent-generated work. Agents can produce changes faster than any human can review them, and most developer tools were not originally designed for multi-agent orchestration. It becomes easy to lose track of what ran, what changed, what was validated, and what still needs human judgment. The GitHub Copilot app is a major step toward addressing this. One feature in particular that I’ve learned to love and use almost every day is canvases. Canvases let developers and agents interact on a durable, shared surface. Instead of treating chat as the only place where work happens, canvases make work visible, steerable, and approvable as it unfolds. Chat is great for intent, but weak for durable execution I still believe chat is one of the best interfaces we have for intent. It’s where you can think, refine, and direct. It’s fast and flexible, especially when the problem is still ambiguous. But once an agent starts doing real work, chat becomes a long scroll of instructions, logs, pivots, and corrections.
