Using GitHub Copilot for AI-assisted development

Agent HQ: The next chapter for GitHub ✨ Today at Universe, we're announcing Agent HQ with Anthropic, OpenAI, Google, xAI, and Cognition! After nearly twenty years of GitHub being the home for developers, it’s now the home for developers and coding agents. One open way to direct, observe, and manage work across GitHub, Visual Studio Code, CLI, and mobile clients. The way people build software is changing fast. GitHub already serves 180 million developers, 90% of Fortune 500s, and more than 10 million students and teachers. A new developer joins every second and 80% of them use Copilot in their first week. Yet even with all this growth, incredible power remains fragmented across too many tools and interfaces. At GitHub, we’ve always worked to bring that power together by making Git accessible, making code review systematic with pull requests, and making deployment automatic with Actions. Agent HQ is the next shift. It’s an open ecosystem so developers can access any agent, any way they work on the primitives they already know - Git, pull requests, issues. All inside your Copilot subscription. Welcome home, agents! https://lnkd.in/gEpqrrzV 1️⃣Mission Control 🛸Branch controls to decide when CI and checks run on agent‑created code 🛸Agent identity & access so you can see which agent did what, with policy enforcement 🛸One‑click merge conflict resolution, better file navigation, and richer code comments 🛸New integrations for Slack and Linear (alongside Jira, Teams, Azure Boards, Raycast) 2️⃣New customization in VS Code 🛸Plan Mode: Co‑create a step‑by‑step plan with Copilot before any code is written. Catch gaps early, approve, and then implement locally or via a cloud agent. 🛸Custom agents with AGENTS.md: Check in guardrails like “use this logger” or “prefer table‑driven tests,” and your agent follows them every time. 🛸GitHub MCP Registry (in VS Code): Discover and enable MCP services (Stripe, Figma, Sentry, and more) so your agent can tap the same tools your team already uses. 3️⃣Confidence & control for teams 🛸GitHub Code Quality (preview): Org‑wide visibility, governance, and reporting to improve maintainability, reliability, and test coverage. 🛸Code review inside the coding agent workflow: Agents run a first‑line review so issues surface before humans ever see the PR. 🛸Copilot metrics dashboard: Track adoption and impact, with the critical usage signals leaders need. 🛸Control plane: Set security policies, enable audit logging, manage model/data access, and decide which agents are allowed, without slowing developers down.

#GitHub Universe is underway and wow! - there’s a lot of goodies in store for the more than 180 million developers that call GitHub home. From Mission Control, where can choose from a fleet of agents, assign them work in parallel, and track their progress from any device to Plan Mode in VSCode, which helps you to build a step-by-step approach for your task, it's a bonanza to explore. If you didn't catch the the keynote, you can check out the big announcements here: https://aka.ms/AAycw0s

Today at Github Universe, we’re announcing Agent HQ with Anthropic, OpenAI, Google, xAI, and Cognition! After nearly twenty years of GitHub being the home for developers, it’s now the home for developers and coding agents. One open way to direct, observe, and manage work across GitHub, Visual Studio Code, CLI, and mobile clients.

Big news from GitHub Universe today — #GitHub just announced Agent HQ 🎉 It’s a command and control centre for your AI coding agents — a place to manage, compare, and collaborate with multiple agents (#OpenAI, #Anthropic, #Google, #xAI, #Cognition) right inside GitHub and VS Code. Super cool step toward a more open, flexible AI dev workflow.

It's showing that we are as an engineers should look carefully how AI Agents improve and speed up our work. What I believe will be a next big thing an integration with Platform Engineering, DORA metrics and other terms which we know from engineering. This creates an autonomous Agentic Platform. There will be always space for human but we will be focusing on most complex tasks and we shift left our work in typical SDLC process.

GitHub Universe just wrapped—and what a time to be a developer! Application development is officially at the frontier of AI innovation. No more “I don’t have time to build this”—and gone are the days of asking for permission to explore an idea. I loved this quote from the keynote: “Groundbreaking innovations don’t start with a grand plan—they start with a spark of an idea and the courage to act.” With GitHub Copilot, you dream it, you ship it! 🔥 Big Announcements from Universe: ✨ Agent HQ – The next evolution of GitHub’s open platform. Developers can now orchestrate all their agents to perform tasks across multiple surfaces—GitHub, VS Code, CLI, and mobile—using the primitives they already know. To bring this vision to life, GitHub is partnering with Anthropic, OpenAI, Google, Cognition, xAI, and more. ✅ Mission Control – A unified command center to steer multiple agents across GitHub, VS Code, mobile, and CLI. ✅ Plan Mode in VS Code – Align developers and agents on context and goals before execution. ✅ Custom Agents – Create agents in VS Code with clear rules and guardrails to shape behavior. ✅ Expanded Integrations – GitHub now connects with Teams, Slack, Linear, Jira, Raycast, and Azure Boards. ✅ Code Quality & Copilot Metrics Dashboard – Visibility, reliability, and control as AI adoption accelerates. ✅ Control Plane – Enterprise-grade policy, security, and audit logging for AI agents and models. 💡 The future of software development is agentic, collaborative, and AI-powered. GitHub + Visual Studio is the ultimate platform to accelerate from idea to impact. If you’re still writing every line of code yourself because “it’s a good idea,” that’s like insisting on writing assembly code after the compiler was invented. The game has changed!

Day 30 of 30 Software/Web Dev Tools in 30 Days Tool: GitHub Copilot Category: AI-Coding Assistant Wow, I can’t believe today is the final day. I honestly can’t believe it, Day 30 of 30 Software Tools in 30 Days. It feels so unreal but amazing at the same time. I don’t know if you’ve learned as much as I have from this series, but I’ve learned a lot. Even though I’ve been sharing these tools here, I’ve also been exploring them myself, testing, researching, and discovering new things every single day. This journey has opened my eyes to so many tools I had no idea about before, and I’m genuinely happy I started it. So today, we’re wrapping it all up with something special, a tool that has truly changed how developers write code. Github Copilot. GitHub Copilot is like your smart coding partner. It helps developers write code faster and better by suggesting whole lines or even entire functions as you type. Powered by AI, Copilot understands your code context and gives real-time suggestions that save time and reduce errors. It’s like having an assistant that reads your mind (in code). You can use it in editors like VS Code, Neovim, JetBrains, and more, and it supports many languages, from Python and JavaScript to C++ and Go. If you’re a developer who wants to code smarter, not harder, GitHub Copilot is that tool. And that’s it, 30 days, 30 tools. It’s been an exciting ride, and I’m so proud of how far this series has come. Thanks for following along, and cheers to more learning, more building, and more tech adventures ahead. Don't forget to follow for more tech tip and coding tips.

The latest GitHub Blog post discusses the development of custom models that enhance the completions experience in GitHub Copilot. I found it interesting that these advancements aim to significantly improve coding efficiency and accuracy, making tools like Copilot even more indispensable for developers. What are your thoughts on the impact of AI-driven tools in our coding practices?

Started with GitHub Copilot: 1. Set up GitHub Copilot: Install the Extension: If you are using an IDE like VS Code, navigate to the Extensions Marketplace, search for "GitHub Copilot," and click "Install." This will typically install both GitHub Copilot and GitHub Copilot Chat. 2. Basic Code Completion: Create a File: Open or create a new file in your IDE. Start Typing: Begin typing code. GitHub Copilot will provide inline suggestions in "ghost text" as you type. Accept Suggestions: Press Tab to accept the suggested code. 3. Using GitHub Copilot Chat (Agent Mode for Autonomous Coding): Open Chat: Open the Chat view (often by pressing Ctrl+Alt+I or selecting the chat icon in the IDE's title bar). Select Agent Mode: In the chat mode dropdown, select "Agent" to enable autonomous coding. Provide a Prompt: Describe the task you want Copilot to perform (e.g., "Create a basic Node.js web app for sharing recipes"). The AI will analyze your request and generate the necessary files and code. Review and Accept: Review the generated code and select "Keep" to accept the changes. 4. Using GitHub Copilot Chat (Inline Chat for Specific Tasks): Select Code: Highlight the code you want to modify or get help with in your editor. Open Inline Chat: Press Ctrl+I to open the inline chat. Ask a Question/Provide a Prompt: Ask Copilot to explain the code, refactor it, add documentation, or make other modifications directly within your file. 5. Customization and Best Practices: Context is Key: Provide clear and concise prompts, and ensure relevant files are open in your IDE to give Copilot sufficient context. Experiment with Models: In Copilot Chat, you can often switch between different language models (e.g., GPT-4, Claude) to see which performs best for your specific task. Refine Prompts: If Copilot's initial suggestions are not what you need, refine your prompts to guide it towards better outputs. Sign In and Authorize: Hover over the Copilot icon in the Status Bar (or click on it) and select "Set up Copilot" or "Login to GitHub." You will be prompted to sign in with your GitHub account and authorize the Copilot plugin. If you don't have a Copilot subscription, you may be signed up for a free trial. 

At work we have been going through a concerted process of experimenting with LLM-supported development, asking ourselves how these tools can help us and how they can't. I have two stories from this week, both assisted by GitHub Copilot. In the first, I built an internal GitHub dashboard to monitor various attributes of our Git repositories with help from Copilot to guide me through making calls to Octokit, which I wasn't familiar with. This worked quite well but once I got it onto staging, I hit two problems: 1. Copilot wasn't particularly concerned about memory efficiency and had taken a silly route to answering "how old is the newest commit" by loading all commits and then taking the first one. In a big repository, this blew memory usage over 1GB and killed it. Turns out there were much simpler ways to do it. 2. Copilot also didn't much care to be efficient with API rate limits and decided to use a search endpoint that was limited to 30 calls per minute to count the number of open PRs. There were other APIs that could have been used that are accounted for under the general rate limits of 5k/hour. So I suppose this experience was OK, but it was clear that the AI would take any route to correctness and wasn't weighting other factors quite as strongly. Quite an interesting experience. The second task was to add a client-side type-ahead filtering box to a page that had a table on it. I had a well-elaborated GitHub issue for this and I prompted Copilot to "Analyse the requirements in Issue #xxxx and implement those requirements in this codebase". Copilot rumbled away for a bit and finished - and it was almost perfect. The field wasn't in the best place on the page, but it worked correctly the first time and it was easily moved. I wrote in my notes "There's something of the Sorcerer's Apprentice about this". ....and I was not wrong. One hour of prompting and refinement had gotten me to a working PR with tests ... which failed. What followed was TWO hours of watching Copilot spiral into madness trying to correctly test these React components to a high degree of confidence. To be fair, that's mostly what happens to me when I try to work with React too. In the end we got to working but it was a bit of a journey. I read a quote once along the lines of "I don't want the AI to make music and art while I do the dishes. I want the AI to do the dishes while I make the music." Today felt a bit like I got to do all the dull and annoying parts of programming after Copilot got to do the fun bit of making the computer do things. I felt like I spent two hours watching the computer spin and learned nothing useful in the process. My previous approach to working with LLMs was to ask ChatGPT questions about the technology I was using like it was the patient teacher I never had. That approach was much better for my own learning and development.