Note: this is a lightly edited version of an internal work post I wrote in April 2025 before Copilot Coding Agent was released to the public.

I’ve been playing with Copilot Coding Agent a bit lately, and it’s been really interesting for me to realize that my thoughts haven’t really been about AI so much as how strongly the output reminds me of incoming contributors to open source software, and all those non-code maintainer skills coming in handy and what I can work on sharpening again. Similar things apply to earlier-in-profession developers too, and with new hires joining our teams next week, this feels especially good to be thinking about right now.

The thing is, when you combine very eager volunteers with low context, you’ll tend to get additive solutions. And that makes sense! They’re thinking about how to make something work, not “what are all the things that happened leading up to this that caused this that maybe need re-examining” – root causing, if you will. But just because it makes sense and maybe is even expected doesn’t mean that we can’t help steer contributors, human or AI, in better directions through the way we describe the problem space in a well-written issue. Practices of keeping good hygiene between discussing problem definitions (issues) and feedback on solution proposals (PRs) also seem more important than ever – there aren’t always solution proposals to be had for a given problem, and sometimes there are multiple to think through.

So what are some things we can all think about as we grow ourselves and support the journey of others? Here’s what I’m thinking about, and I’d love to hear and talk about yours in the discussion!

• Keep issues focused on the problem definition and context – centering them around a specific solution tends to narrow our thinking when it comes to the solution. Of course, sometimes it’s a straightforward bug that’s the problem, so don’t forget nuance either.
• Clearly written reproduction steps: one of the things I encourage giving visible code-equivalent credit for in OSS, because it’s hard to describe reproduction steps in a way that is broadly understandable and doesn’t overly rely on screenshots. Oh and hey, if you do use screenshots, remember to include alt text – it’s good for both humans and the machine.
• Including references and links for easy access from one place – building context from code archaeology is hard! This doesn’t have to happen when an issue is first written – it can be added later as you dig around and find things.
• Ensuring non-durable artifacts or content behind auth like Slack conversations are copied over and/or summarized, especially when decisions are made.
• Raising each other up and remembering to include all these skills as a part of how we think about impact and excellence at work and beyond.
• Being willing to try to explain again in a different way to steer contributors onto a given path.

The machine is imperfect just as we are, and with AI it’s even clearer how it reflects our own imperfect communication back at us. I’m looking forward to cross-repo support someday with Copilot Coding Agent, because I can see the promise in using the tool to reduce the friction getting started with resolving audit-discovered issues. So while that’s not here just yet, we can still get a start on refining the way those types of issues are written to help get to a reasonable solution faster, whether done by human or Copilot.