The idea

A developer tool may offer chat, an IDE assistant, a command-line interface, an app, and agent workflows. These are different places and ways to work, not merely different buttons for the same task. GitHub documents Copilot across GitHub.com, IDEs, the CLI, the Copilot app, and cloud-agent workflows. GitHub Docs

When a tool presents many choices, decide in two layers.

First choose the working mode. A chat is useful for asking questions, exploring an unfamiliar codebase, or discussing options. A coding agent is a convenient informal name for a workflow that can pursue a bounded, multi-step task. Depending on the product and mode, it may edit files, use tools, run commands, or react to intermediate results. These capabilities are product-specific, so check the interface you are using.

Then consider the model: the AI component producing or guiding the response. Models can emphasize different combinations of speed, cost, accuracy, reasoning ability, context capacity, and multimodal capability. GitHub Docs That is a separate decision from whether you want a conversation or a more independent workflow.

Plain English: Pick how much work you want the tool to attempt before you pick which AI engine should attempt it.

A useful question is: “What must happen between my request and a reviewable result?” If the answer is “explain this,” start with chat. If it is “make several related changes and run checks,” an agent workflow may fit. The amount of independent action allowed is called autonomy. More autonomy can reduce back-and-forth, but it also creates more work to inspect.

A small example

Example: You need to add validation for invalid email addresses in a TypeScript repository.

Start in chat: “Which files currently validate email addresses, and which tests cover them?” This is exploration. You are asking for a map of the problem, not requesting a change.

Next, if the scope is clear, use an interactive agent session: “Add validation for invalid email addresses. Change only the affected files. Add tests. Show me the diff before I apply anything.” The task has a boundary, expected artifacts, and a review point.

Now decide whether to select a model yourself. A small, routine request may favor speed or cost. A task with many dependencies or difficult trade-offs may justify a model oriented toward more complex reasoning. The context window is the amount of information a model can consider in one request; it can matter when the task needs many files or long requirements. Model choice affects trade-offs, not a guarantee of correctness. GitHub Docs

Some supported Copilot surfaces offer auto model selection. GitHub says it can route tasks using task complexity, system availability, and an efficiency, balance, or intelligence preference. GitHub Docs Auto chooses within what is available. You still choose the working mode and the review boundary.

Try it

Take one small backlog item. Spend five minutes writing three lines:

  1. Outcome: What reviewable result do you need?
  2. Mode: Would chat, an interactive agent, or a more autonomous agent best fit? State why.
  3. Review: What will you inspect before accepting the work: a diff, tests, commands, or all three?

For a risky or unfamiliar change, begin with lower autonomy. In the Copilot app, GitHub documents Interactive, Plan, and Autopilot session modes separately from model choice. GitHub Docs A plan can make the proposed path visible before implementation.

Watch out

“Chat,” “coding agent,” and “model” are useful teaching labels, not universal product categories. Names, available modes, and permissions vary by provider and surface. Model availability can also depend on your plan, the surface, organization policy, and current availability. GitHub Docs

Do not treat a stronger model or auto selection as approval. Inspect the diff. Run relevant tests. Consider security effects and commands that might change project or system state. A good default is simple: use chat to understand, use an agent for a clearly bounded task, and keep humans responsible for acceptance.