So far, the agent has been used by about 400 GitHub employees in more than 300 our our repositories, and we've merged almost 1,000 pull requests contributed by Copilot.

In the repo where we're building the agent, the agent itself is actually the #5 contributor - so we really are using Copilot coding agent to build Copilot coding agent ;)

(Source: I'm the product lead at GitHub for Copilot coding agent.)

Have they tried dogfooding their dogshit little tool called Teams in the last few years? Cause if that's what their "famed" dogfooding gets us, I'm terrified to see what lays in wait with copilot.

In any case, I think this is the best use case for AI in programming—as a force multiplier for the developer. It’s for the best benefit of both AI and humanity for AI to avoid diminishing the creativity, agency and critical thinking skills of its human operators. AI should be task oriented, but high level decision-making and planning should always be a human task.

So I think our use of AI for programming should remain heavily human-driven for the long term. Ultimately, its use should involve enriching humans’ capabilities over churning out features for profit, though there are obvious limits to that.

[0] https://www.cnbc.com/2025/04/29/satya-nadella-says-as-much-a...

They just cut down their workforce, letting some of their AI people go. So, I assume there isn't that much success.

https://techcrunch.com/2025/04/29/microsoft-ceo-says-up-to-3...

This was true up around 15 years ago. Hasn't been the case since.

This is a feature, not a bug. LLMs are going to be the next "OMG my AWS bill" phenomenon.

Consider using Aider, and aggressively managing the context (via /add, /drop and /clear).

https://aider.chat/

In a brownfield code base, I can often provide it reference files to pattern match against. So much easier to get great results when it can anchor itself in the rest of your code base.

For example it (Gemini 2.5) really struggles with newer ecosystem like Fastapi when wiring libraries like SQLAlchemy, Pytest, Python-playwright, etc., together.

I find more value in bootstrapping myself, and then using it to help with boiler plate once an effective safety harness is in place.

Initial cost was around $20 USD, which later grew to (mostly polishing) $40 with some manual work.

I've intentionally picked up simple stack: html+js+php.

A couple of things:

* I'd say I'm happy about the result from product's perspective * Codebase could be better, but I could not care less about in this case * By default, AI does not care about security unless I specifically tell it * Claude insisted on using old libs. When I've specifically told it to use the latest and greatest, it upgraded them but left code that works just with an old version. Also it mixed latest DaisyUI with some old version of tailwindcss :)

On one hand it was super easy and fun to do, on the other hand if I was a junior engineer, I bet it would have cost more.

I wonder if the next phase would be the rise of (AI-driven?) "linters" that check that the implementation matches the architecture definition.

That doesn’t matter anymore when you’re vibe coding it. No human is going to look at it anyway.

It can all be if/else on one line in one file. If it works and if the LLMs can work at, iterate and implement new business requirements, while keeping performance and security - code structure, quality and readability don’t matter one bit.

Customers don’t care about code quality and the only reason businesses used to care is to make it less money consuming to build and ship new things, so they can make more money.

That said, months ago I did experience the kind of slow agent edit times you mentioned. I don't know where the bottleneck was, but it hasn't come back.

I'm on library WiFi right now, "vibe coding" (as much as I dislike that term) a new tool for my customers using Copilot, and it's snappy.

A wall of noise that tells you nothing of any substance but with an authoritative tone as if what it's doing is objective and truthful - Immediately followed by:

- The 8 actual lines of code (discounting the tests & boilerplate) it wrote to actually fix the issue is being questioned by the person reviewing the code, it seems he's not convinced this is actually fixing what it should be fixing.

- Not running the "comprehensive" regression tests at all

- When they do run, they fail

- When they get "fixed" oh-so confidently, they still fail. Fifty-nine failing checks. Some of these tests take upward of an hour to run.

So the reviewer here has to read all the generated slop in the PR description and try to grok what the PR is about, read through the changes himself anyway (thankfully it's only a ~50 line diff in this situation, but imagine if this was a large refactor of some sort with a dozen files changed), and then drag it by the hand multiple times to try fix issues it itself is causing. All the while you have to tag the AI as if it's another colleague and talk to it as if it's not just going to spit out whatever inane bullshit it thinks you want to hear based on the question asked. Test failed? Well, tests fixed! (no, they weren't)

And we're supposed to be excited about having this crap thrust on us, with clueless managers being sold on this being a replacement for an actual dev? We're being told this is what peak efficiency looks like?

https://docs.github.com/en/copilot/managing-copilot/managing...

On an unrelated note, it also suggested I use the "Strobe" protocol for encryption and sent me to https://strobe.cool which is ironic considering that page is all about making one hallucinate.

Would you be able to drop me an email? My address is my HN login @github.com.

(I work on the product team for Copilot coding agent.)

But also as a slightly deeper observation - agentic coding tools really do benefit significantly from good test coverage. Tests are a way to “box in” the agent and allow it to check its work regularly. While they aren’t necessary for these tools to work, they can enable coding agents to accomplish a lot more on your behalf.

(I work on Copilot coding agent)

I’d love for this to blow past cursor. Will definitely tune in to see it.

In coding agent, we encourage the agent to be very thorough in its work, and to take time to think deeply about the problem. It builds and tests code regularly to ensure it understands the impact of changes as it makes them, and stops and thinks regularly before taking action.

These choices would feel too “slow” in a synchronous IDE based experience, but feel natural in a “assign to a peer collaborator” UX. We lean into this to provide as rich of a problem solving agentic experience as possible.

(I’m working on Copilot coding agent)

They definitely use it for full-time SWEs

Source: I work there

Copilot Workspace could take a task, implement it and create a PR - but it had a linear, highly structured flow, and wasn't deeply integrated into the GitHub tools that developers already use like issues and PRs.

With Copilot coding agent, we're taking all of the great work on Copilot Workspace, and all the learnings and feedback from that project, and integrating it more deeply into GitHub and really leveraging the capabilities of 2025's models, which allow the agent to be more fluid, asynchronous and autonomous.

(Source: I'm the product lead for Copilot coding agent.)

That seemed to drop off the Github changelog after February. I’m wondering if that team got reallocated to the copilot agent.

Edit: From the TFA: Using the agent consumes GitHub Actions minutes and Copilot premium requests, starting from entitlements included with your plan.

[0] https://docs.github.com/en/copilot/managing-copilot/monitori...

(Source: I'm on the product team for Copilot coding agent.)

(Source: I'm on the product team for Copilot coding agent.)

Sure! How can I help you?

https://developers.google.com/gemini-code-assist/docs/review...

Definitely not Google Code, but better than Cloud Source Repositories.

But the upgraded Copilot was just in response to Cursor and Winsurf.

We'll see.

[0] https://news.ycombinator.com/item?id=43904611

Copilot literally can't push directly to the default branch - we don't give it the ability to do that - precisely because we believe that all AI-generated code (just like human generated code) should be carefully reviewed before it goes to production.

(Source: I'm the product lead for Copilot coding agent.)

To me, this reads like it'll be a good junior and open up a PR with its changes, letting you (the issue author) review and merge. Of course, you can just hit "merge" without looking at the changes, but then it's kinda on you when unreviewed stuff ends up in main.