Let's Learn GitHub Copilot App
Summary
GitHub Copilot App shifts developers from coding to orchestration: managing parallel AI agent sessions that autonomously handle code reviews, CI fixes, and issue resolution. The new bottleneck isn't writing code anymore—it's supervising agents that write code at scale.
Key Takeaways
- Parallel agent sessions run in isolation, allowing developers to explore multiple solutions to GitHub issues simultaneously without cross-contamination of changes.
- Agent Merge feature automates code review remediation by monitoring PRs, auto-addressing valid Copilot review comments, and resolving CI failures before merge.
- Plan Mode enables refinement of agent approach through iterative questioning with the model before execution, reducing wasted compute on wrong directions.
- Developer role evolution: from hands-on coding to strategic decision-making on what to build and how to build it, delegating execution to agents.
- Four-phase SDLC (planning, building, reviewing, shipping) now integrates AI agents across all stages rather than siloed tools, reducing context loss.
Related topics
Transcript Excerpt
My day starts in the GitHub copilot app, where I ask for a list of top issues and feedback we've actually gotten, and it looks like the app has surfaced 3 or 4 issues that we might want to take a look at. Coding agents are writing much of my code, and my new bottleneck is managing all of these coding agents. The app enables me to manage a team of agents, all while having deep integration with the GitHub platform. Now I can take each of these issues and go fire off parallel sessions to explore what might be going on. Each of these sessions is running in its own isolation. The changes in one agent session won't impact the changes in another session. I spent a lot of time in plan mode with the model on the right approach. I can refine this plan by asking the model some more questions. I can r…