How to build interactive experiences with canvases

2026-07-21 · Source: The GitHub Blog · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, short

Summary

GitHub Copilot app introduces "canvases," shared, interactive surfaces designed to enhance collaboration between developers and AI agents beyond conversational prompts. These canvas extensions allow users to visualize information, interact directly with data, and automate workflows through clicks, edits, and other actions. Canvases are generated using a "/create-canvas" prompt within the Copilot app and can evolve with user input. Examples include an issue triage helper for quickly categorizing GitHub Issues, an interactive codebase diagram for visualizing project structure, a sessions worktree view to manage active and stale Copilot sessions, an agent prompt coach for improving prompt quality, and a knowledge finder to locate experts across various communication platforms. This functionality transforms AI from a purely conversational tool into a dynamic, visual workspace.

Key takeaway

For Software Engineers integrating AI agents into workflows, consider using GitHub Copilot's canvas extensions. You can move beyond purely conversational interactions. Create visual interfaces for tasks like issue triage, codebase visualization, or prompt coaching. This enables direct manipulation and real-time collaboration with agents. This approach streamlines complex tasks benefiting from visual interaction, making your agent-assisted development more efficient and intuitive. Explore the GitHub Copilot app to experiment with these interactive surfaces.

Key insights

Canvases extend AI agent interaction beyond chat, enabling visual collaboration and direct data manipulation.

Principles

• Visual interfaces enhance complex task understanding.
• Interactive surfaces improve human-agent collaboration.
• Prompt-generated UIs can evolve with workflow.

Method

To create a canvas, use "/create-canvas" in the GitHub Copilot app, then describe the desired interface and capabilities. The canvas updates in real time, responding to user interactions or agent actions.

In practice

• Triage GitHub Issues visually.
• Visualize codebase architecture dynamically.
• Improve agent prompt quality.

Topics

• GitHub Copilot
• AI Agents
• Interactive Canvases
• Visual Development
• Codebase Visualization
• Issue Management

Code references

• github/blog
• features/ai

Best for: AI Engineer, Software Engineer, Prompt Engineer

Related on AIssential

• GitHub Copilot app for Beginners: Getting started — The GitHub Copilot app integrates AI agents into a multi-session workspace for comprehensive software development workflows.
• Agents on the Canvas in tldraw — Steve Ruiz, tldraw — AI agents can evolve from single-shot generation to collaborative, orchestrated entities directly interacting with canvas environments.
• Build Interactive Agents with Generative UI — AI agents are evolving from text-only outputs to generating interactive, custom user interfaces.
• CopilotKit: Giving AI Agents a Real Interface Users Can See, Guide, and Trust — CopilotKit provides an open-source framework to connect AI agent logic with user interfaces, enabling transparency and human control.
• Cooking with Agents in VS Code — Liam Hampton, Microsoft — VS Code serves as a single, customizable hub for orchestrating diverse AI agents, optimizing developer workflows.
• 🎙️GitHub’s Mario Rodriguez on AI Coding Agents, Copilot, and the Future of Developers — AI agent quality in late 2025 enabled macro-delegation, accelerating GitHub activity and redefining developer roles.

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Editorial summary, takeaway, and curation by AIssential. Original article published by The GitHub Blog.