Aaron Winston details how developers can use GitHub Copilot’s latest features—including mission control, agent mode, CLI, and automated reviews—to streamline every phase of the software workflow.

GitHub Copilot: Modern AI Coding Workflows, Mission Control, and Best Practices

Author: Aaron Winston

This guide explores how GitHub Copilot has evolved from a code autocomplete tool to a comprehensive AI coding assistant, introducing new workflows and productivity gains for developers.

What’s New in GitHub Copilot?

  • Mission Control: Centralized interface for running multi-step tasks like generating tests, opening pull requests, and handling refactoring jobs from within VS Code or GitHub.
  • Agent Mode: Define what you want to achieve; Copilot plans the approach, seeks your feedback, tests its solutions, and refines work in real time.
  • Copilot CLI: AI in the terminal—automate, explore, and edit your repo with intelligent commands.
  • Coding Agent: Offload repetitive tasks (scaffolding, refactors, tests, documentation) to Copilot for draft PR review.
  • Code Review: Automated pull request analysis for risky diffs, missing tests, and potential bugs, all integrated within your GitHub workflow.

Key Feature Walkthroughs & Prompt Patterns

1. Mission Control and Agent Mode in VS Code

  • Setup: Install the Copilot extension, enable agent mode in settings, open mission control.

  • Example Prompt:

    # Add caching to userSessionService to reduce DB hits

    In mission control: “Add a Redis caching layer to userSessionService, generate hit/miss tests, and open a draft PR.”

  • Workflow: Copilot will generate a new file, apply code changes, write tests, and open a draft PR, summarizing changes.

2. Copilot CLI for Terminal Automation

  • Install:

    npm install -g @github/copilot-cli
copilot /login

  • Sample Commands:

    • copilot explain . — Summarize repo, dependencies, coverage, and issues.
    • copilot fix tests — Detect and propose fixes for failing tests.
    • copilot setup project — Project initialization automation.
    • copilot edit src/**/*.py — Batch edits across Python files.

3. Automated Code Review

  • Enable: In repo settings, activate Copilot code review.
  • Capabilities:
    • Highlights missing test coverage
    • Flags potential bugs or edge cases
    • Surfaces possible security vulnerabilities

  • Prompt Example in Pull Request Chat:

    Summarize the potential risks in this diff and suggest missing test coverage.

4. Using Copilot Coding Agent for Features

  • Example Issue:
    • Feature: CSV import for user sessions
    • Parses, validates, batches, appends, with tests, docs, and API endpoint
  • Assignment: Assign issue to Copilot agent—it clones the repo, implements the feature, and opens a draft PR for review.

5. Best Practices

  • Always review Copilot output before merging.
  • Write clear prompts: explain why and how, not just what.
  • Prefer incremental changes over large rewrites.
  • Keep developers involved in security and architecture decisions.
  • Log prompts and results to refine your workflow.
  • Start by automating non-critical tasks such as tests or boilerplate.

6. Why This Matters

  • AI-assisted development is now mainstream, not experimental.
  • Typed languages (TypeScript, Python) pair naturally with Copilot.
  • By centralizing workflows (mission control), Copilot reduces context switching for developers.

Next Steps

Start by trying one of Copilot’s new modes—mission control, agent mode, CLI, or review features—on a single module or workflow in your project. Track time saved, review quality, and iterate your approach.

Links for more:

With Copilot, you can offload the routine and focus more on solving bigger engineering problems.

This post appeared first on “The GitHub Blog”. Read the entire article here