Copilot Fusion puts the focus back on what you want to build

More help choosing how the work gets done

On October 28, GitHub introduced Copilot Fusion at Universe 2026. GitHub says Fusion looks at a coding task and chooses one or more AI systems to handle it. That work can include planning, writing code, finding problems, and using tools.

GitHub also announced Copilot projects, which let people manage several AI work sessions across different code projects in its Copilot app. It described continuing the same work in that app, the VS Code coding app, a browser, or the command line, where developers type instructions to run tools.

These are product announcements. Trying them on your own work is still how you find out whether they help. Our view is that AI coding tools may increasingly let you focus on the result you want while they choose more of the steps.

Read the announcement: GitHub Universe 2026: Evolving every layer of the stack.

Bring your knowledge of the work

Building by describing an idea to AI is often called vibe coding. If you are learning that way, start with a problem you can explain. 'Help the team see which customer requests are waiting' gives you a clearer starting point than 'build a modern dashboard'.

Write down who will use the tool and what they need to do. Explain what should happen when information is missing, what the tool may change, and which actions need your approval.

You can ask the AI to explain a decision in familiar language, show other options, and point out what still needs checking. If an answer is hard to follow, ask for a clearer version. Your understanding of the business belongs in the conversation.

Try one change you can check

Choose a change small enough to review in one sitting. Before the AI starts, write what a person will do and the result they should see. Save the starting version so you can compare it and undo the experiment.

Then try the result as a user. Look beyond the screenshot. Check what happens when everything goes well, when there is no information, and when an action fails. This gives you a practical way to judge the build.

1. Describe one repeated problem and what should become easier.
2. Ask for a short plan before the tool makes changes.
3. Try the normal path, an empty result, and a failed action.
4. Record what worked and the next change to try.

Keep learning as the tools change

We expect some coding tools to handle more of the AI selection for you. That is our reading of this announcement, rather than a promise about the market. Setting clear limits, reading changes, and checking results remain useful skills to practise.

Progress might mean a customer gets an answer sooner or your team stops retyping the same information. Keep that outcome close. Each careful experiment can help you choose your next step, whether you want to build your own skills or work with someone on the finished tool.