GitHub Copilot CLI ACP Server Support Boosts Coding Agent Integration

My team-mate has been digging into GitHub Copilot CLI’s new ACP server support, and explains it is a genuinely interesting step toward making coding agents easier to integrate anywhere. ACP stands for Agent Client Protocol, a standard way for a client (an editor, IDE, terminal UI, or even a pipeline runner) to talk to a coding agent (like Copilot CLI). Instead of every tool inventing its own bespoke integration, ACP provides a shared contract for how requests, responses, and agent actions flow. A quick note: ACP support in GitHub Copilot CLI is currently in public preview, so details may evolve. https://msft.it/6049QdMMU Why this matters: IDE integrations: Bring Copilot style agent workflows into editors or internal dev environments that do not have first class Copilot plugins. CI/CD pipelines: Orchestrate agentic coding tasks in automated workflows, like generating patches, refactoring, or assisting with test updates as part of builds. Custom frontends: Build purpose specific interfaces for your team’s workflow, like a lightweight internal “coding assistant console.” Multi-agent systems: Coordinate Copilot alongside other AI agents with a standard protocol, rather than stitching together fragile adapters. The bigger takeaway for me is that standard protocols are what turn “cool demos” into ecosystems. If you have ever built a custom IDE integration or tried to automate code changes in a pipeline, you know how much time disappears into glue code. ACP aims to reduce that friction. If you are experimenting with agentic workflows, ACP server support in Copilot CLI is worth a look, especially if you want Copilot assistance outside the usual editor plugin path. #GitHubCopilot #DeveloperTools #AIEngineering #DevEx #Automation #CICD #IDEs

My team-mate has been digging into GitHub Copilot CLI’s new ACP server support, and explains it is a genuinely interesting step toward making coding agents easier to integrate anywhere. ACP stands for Agent Client Protocol, a standard way for a client (an editor, IDE, terminal UI, or even a pipeline runner) to talk to a coding agent (like Copilot CLI). Instead of every tool inventing its own bespoke integration, ACP provides a shared contract for how requests, responses, and agent actions flow. A quick note: ACP support in GitHub Copilot CLI is currently in public preview, so details may evolve. https://msft.it/6046QwNnp Why this matters: IDE integrations: Bring Copilot style agent workflows into editors or internal dev environments that do not have first class Copilot plugins. CI/CD pipelines: Orchestrate agentic coding tasks in automated workflows, like generating patches, refactoring, or assisting with test updates as part of builds. Custom frontends: Build purpose specific interfaces for your team’s workflow, like a lightweight internal “coding assistant console.” Multi-agent systems: Coordinate Copilot alongside other AI agents with a standard protocol, rather than stitching together fragile adapters. The bigger takeaway for me is that standard protocols are what turn “cool demos” into ecosystems. If you have ever built a custom IDE integration or tried to automate code changes in a pipeline, you know how much time disappears into glue code. ACP aims to reduce that friction. If you are experimenting with agentic workflows, ACP server support in Copilot CLI is worth a look, especially if you want Copilot assistance outside the usual editor plugin path. #GitHubCopilot #DeveloperTools #AIEngineering #DevEx #Automation #CICD #IDEs

My team-mate has been digging into GitHub Copilot CLI’s new ACP server support, and explains it is a genuinely interesting step toward making coding agents easier to integrate anywhere. ACP stands for Agent Client Protocol, a standard way for a client (an editor, IDE, terminal UI, or even a pipeline runner) to talk to a coding agent (like Copilot CLI). Instead of every tool inventing its own bespoke integration, ACP provides a shared contract for how requests, responses, and agent actions flow. A quick note: ACP support in GitHub Copilot CLI is currently in public preview, so details may evolve. https://msft.it/6049Q5NGc Why this matters: IDE integrations: Bring Copilot style agent workflows into editors or internal dev environments that do not have first class Copilot plugins. CI/CD pipelines: Orchestrate agentic coding tasks in automated workflows, like generating patches, refactoring, or assisting with test updates as part of builds. Custom frontends: Build purpose specific interfaces for your team’s workflow, like a lightweight internal “coding assistant console.” Multi-agent systems: Coordinate Copilot alongside other AI agents with a standard protocol, rather than stitching together fragile adapters. The bigger takeaway for me is that standard protocols are what turn “cool demos” into ecosystems. If you have ever built a custom IDE integration or tried to automate code changes in a pipeline, you know how much time disappears into glue code. ACP aims to reduce that friction. If you are experimenting with agentic workflows, ACP server support in Copilot CLI is worth a look, especially if you want Copilot assistance outside the usual editor plugin path. #GitHubCopilot #DeveloperTools #AIEngineering #DevEx #Automation #CICD #IDEs

My team-mate has been digging into GitHub Copilot CLI’s new ACP server support, and explains it is a genuinely interesting step toward making coding agents easier to integrate anywhere. ACP stands for Agent Client Protocol, a standard way for a client (an editor, IDE, terminal UI, or even a pipeline runner) to talk to a coding agent (like Copilot CLI). Instead of every tool inventing its own bespoke integration, ACP provides a shared contract for how requests, responses, and agent actions flow. A quick note: ACP support in GitHub Copilot CLI is currently in public preview, so details may evolve. Why this matters: IDE integrations: Bring Copilot style agent workflows into editors or internal dev environments that do not have first class Copilot plugins. CI/CD pipelines: Orchestrate agentic coding tasks in automated workflows, like generating patches, refactoring, or assisting with test updates as part of builds. Custom frontends: Build purpose specific interfaces for your team’s workflow, like a lightweight internal “coding assistant console.” Multi-agent systems: Coordinate Copilot alongside other AI agents with a standard protocol, rather than stitching together fragile adapters. The bigger takeaway for me is that standard protocols are what turn “cool demos” into ecosystems. If you have ever built a custom IDE integration or tried to automate code changes in a pipeline, you know how much time disappears into glue code. ACP aims to reduce that friction. If you are experimenting with agentic workflows, ACP server support in Copilot CLI is worth a look, especially if you want Copilot assistance outside the usual editor plugin path. #GitHubCopilot #DeveloperTools #AIEngineering #DevEx #Automation #CICD #IDEs

GitHub Copilot CLI’s new ACP server support: ACP - Agent Client Protocol, a standard way for a client (an editor, IDE, terminal UI, or even a pipeline runner) to talk to a coding agent (like Copilot CLI). Instead of every tool inventing its own bespoke integration, ACP provides a shared contract for how requests, responses, and agent actions flow. A quick note: ACP support in GitHub Copilot CLI is currently in public preview, so details may evolve. https://msft.it/6040QwL3a Why this matters: IDE integrations: Bring Copilot style agent workflows into editors or internal dev environments that do not have first class Copilot plugins. CI/CD pipelines: Orchestrate agentic coding tasks in automated workflows, like generating patches, refactoring, or assisting with test updates as part of builds. Custom frontends: Build purpose specific interfaces for your team’s workflow, like a lightweight internal “coding assistant console.” Multi-agent systems: Coordinate Copilot alongside other AI agents with a standard protocol, rather than stitching together fragile adapters. The bigger takeaway for me is that standard protocols are what turn “cool demos” into ecosystems. If you have ever built a custom IDE integration or tried to automate code changes in a pipeline, you know how much time disappears into glue code. ACP aims to reduce that friction. If you are experimenting with agentic workflows, ACP server support in Copilot CLI is worth a look, especially if you want Copilot assistance outside the usual editor plugin path. #GitHubCopilot #DeveloperTools #AIEngineering #DevEx #Automation #CICD #IDEs

GitHub Copilot CLI is now generally available! If you "live" and use terminal non stop, this is a big shift: Copilot CLI isn't just "chat in bash" anymore! It's a terminal-native coding agent that can: 👉 plan -> execute -> test -> review -> iterate without you bouncing between tools. I have been testing it and a few things worth highlighting: 💠 Plan mode (Shift+Tab) - get a structured implementation plan before any code is written. 💠Autopilot mode - let it run end-to-end for the tasks you trust. 💠Specialized agents - it can delegate to focused agents (explore, run tasks/tests, code review, planning). 💠Background delegation (& + /resume) - offload work to a cloud agent while you keep your terminal free. 💠Review + safety - /diff, /review, and session undo/rewind make it easier to stay in control. 💠Model choice - pick from leading models (and swap mid-session with /model). 💠Extensibility - MCP + plugins + skills + hooks = real workflow customization. Terminal feels like a full agentic dev environment, not just a place to run commands. I'd love to hear your thoughts and learn from your experience: ❔ How do we define safe boundaries, approvals, and repeatable workflows? #GitHubCopilot #GitHubCopilotCLI #DevTools #AIEngineering

GitHub Copilot Extension vs GitHub Copilot CLI — What’s the Difference? With GitHub Copilot CLI now generally available, many teams are exploring how it differs from the VS Code Copilot extension. Here’s a quick comparison 👇 🔹 1. Where It Works • VS Code Copilot → Inside your IDE • Copilot CLI → Inside your terminal 🔹 2. Workflow Style • VS Code → Interactive, real-time coding • CLI → Command-driven, automation-friendly 🔹 3. Best For • VS Code → Writing code, refactoring, debugging, asking contextual questions • CLI → Multi-step tasks, repo-wide changes, scripting, DevOps workflows 🔹 4. Interaction Mode • VS Code → Inline suggestions + chat UI • CLI → Terminal commands and structured execution 🔹 5. Automation Capability • VS Code → Assists while you code • CLI → Can plan and execute structured tasks end-to-end 🔹 6. Ideal Users • VS Code → Developers working primarily inside IDE • CLI → Developers who live in terminal, CI/CD, or automation workflows 🔎When to Use What? ✅ Use VS Code Copilot when: 1) Writing or refactoring application code 2) Debugging inside IDE 3) Asking contextual coding questions 4) Iterative feature development ✅ Use Copilot CLI when: 1) Running terminal-heavy workflows 2) Automating structured development tasks 3) Working across repos via command line 4) Supporting DevOps or scripting use cases 💡 In short: VS Code Copilot = AI pair programmer inside your IDE Copilot CLI = AI agent in your terminal We are currently evaluating both (along with Copilot CLI capabilities like multi-model comparison and structured task execution) to enhance developer productivity and automation workflows. Curious - are you using Copilot only inside IDE, or exploring CLI workflows as well? #GitHubCopilot #AI #DeveloperTools #Automation #DevOps #ProductEngineering

Most enterprises adopt GitHub Copilot the same way.! 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗮𝗽𝗽𝗿𝗼𝘃𝗲𝘀 𝗶𝘁. 𝗜𝗧 𝗱𝗲𝗽𝗹𝗼𝘆𝘀 𝗶𝘁. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗹𝗼𝘃𝗲 𝗶𝘁. 𝗗𝗼𝗻𝗲. What nobody does — and I mean almost nobody — is look at token usage before the first big invoice. 𝗪𝗲 𝘄𝗲𝗿𝗲 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲.! Once Co-pilot was available as an extension to popular IDE's like Visual Studio Code and the various JetBrains IDEs (including IntelliJ IDEA, PyCharm) usage was sky-rocketed. The tool is genuinely brilliant and helping developers in most cases. But this burst of lavish usage turns out to have a hidden cost — 𝘁𝗵𝗲 𝘄𝗮𝘆 𝗺𝗼𝘀𝘁 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗻𝗮𝘁𝘂𝗿𝗮𝗹𝗹𝘆 𝘂𝘀𝗲 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗶𝘀 𝗮𝗹𝘀𝗼 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝘄𝗮𝘆 𝘁𝗼 𝘂𝘀𝗲 𝗶𝘁. Here's what was silently burning tokens for most of us: ❌ 10+ tabs open at all times in VS Code — Copilot reads all of them as context, whether you're using them or not ❌ Broad Copilot Chat prompts like "review this codebase" instead of "refactor this one function" ❌ Long chat threads on github.com that re-send the full conversation history with every single message ❌ Massive 600-line files that flood the context window before Copilot even gets to the useful part Nobody was doing anything wrong. We just didn't know these things mattered. What we actually changed — and it wasn't complicated: ✅ Close tabs not relevant to your current task. Sounds obvious. Almost nobody does it. ✅ Start fresh chat threads when switching topics. Don't carry a 40-message auth thread into your dev work. ✅ Write specific prompts and comments. "Validate email format, return boolean" beats "handle the email thing" — for output quality and cost. ✅ Scope your Copilot Chat with #file: or highlight a selection first. Give it less to think about, not more. ✅ Break large files into smaller modules. Good engineering practice that also happens to be significantly cheaper. If you're rolling out Copilot at scale right now — build these habits early. The tool ROI is real, but so is the cost of undisciplined usage across a large team. 𝗦𝗺𝗮𝗹𝗹 𝗵𝗮𝗯𝗶𝘁𝘀. 𝗖𝗼𝗺𝗽𝗼𝘂𝗻𝗱𝗶𝗻𝗴 𝗶𝗺𝗽𝗮𝗰𝘁. What's your experience been with Copilot usage at scale? Would love to hear what others have found. 👇 #GitHubCopilot #EnterpriseAI #DeveloperProductivity #SoftwareEngineering #AITools

Big news for AI-assisted development Two major coding agents just landed inside GitHub Copilot for Business and Pro users. Claude by Anthropic and OpenAI Codex are now available directly within GitHub Copilot for Business and Pro customers. Enterprise and Pro+ had early access, and now this is rolling out more broadly. Here’s what matters. You can run Claude, Codex, and Copilot: - On github.com - In GitHub Mobile - Inside VS Code Same workflows. Shared history. Shared context. No context switching. And no extra subscriptions. It’s included in your existing Copilot plan. During public preview, each coding agent session consumes one premium request. One platform. Multiple agents. All agents run on a unified platform inside GitHub with: - Repository code and history access - Issues and pull requests context - Copilot Memory - Repository instructions and policies - Enterprise governance via the Agent Control Plane (now GA) This is important. We are moving from “AI features” to an agent layer embedded directly into the SDLC, governed and observable at enterprise scale. What you can actually do - Start sessions on web or mobile - Assign agents to issues and PRs - Mention @copilot, @claude, or @codex in PR comments - Let agents open draft PRs - Compare approaches across agents For me, the bigger shift is this: We’re no longer debating which model is better in isolation. We’re orchestrating multiple agents inside one governed developer platform. That changes how teams experiment, compare, and standardize. Have you started running side-by-side agent comparisons in real repos yet? #GitHubCopilot #AINativeDevelopment #AgenticAI #msftadvocate

🚀 𝐓𝐡𝐞𝐫𝐞’𝐬 𝐚 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐆𝐢𝐭𝐇𝐮𝐛 𝐂𝐨𝐩𝐢𝐥𝐨𝐭 𝐚𝐥𝐦𝐨𝐬𝐭 𝐧𝐨 𝐨𝐧𝐞 𝐢𝐬 𝐮𝐬𝐢𝐧𝐠… 𝐚𝐧𝐝 𝐢𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠. Most people customize GitHub Copilot with prompts or custom instructions. Few realize that 𝐀𝐠𝐞𝐧𝐭 𝐒𝐤𝐢𝐥𝐥𝐬 change the game entirely. 𝐀𝐠𝐞𝐧𝐭 𝐒𝐤𝐢𝐥𝐥𝐬 𝐚𝐫𝐞 𝐧𝐨𝐭 𝐩𝐫𝐨𝐦𝐩𝐭𝐬. They’re reusable, portable capabilities—collections of instructions, scripts, examples, and workflows that Copilot (and other agents) can load only when they’re actually needed. Why this matters 👇 ✅ 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞 𝐂𝐨𝐩𝐢𝐥𝐨𝐭 𝐟𝐨𝐫 𝐫𝐞𝐚𝐥 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 Testing. Debugging. Deployment. Reviews. Skills let Copilot carry out specialized, repeatable tasks—not just suggest lines of code. ✅ 𝐂𝐫𝐞𝐚𝐭𝐞 𝐨𝐧𝐜𝐞. 𝐑𝐞𝐮𝐬𝐞 𝐞𝐯𝐞𝐫𝐲𝐰𝐡𝐞𝐫𝐞. The same skill can work across: • GitHub Copilot in VS Code • Copilot CLI • Copilot coding agent ✅ 𝐍𝐨 𝐜𝐨𝐧𝐭𝐞𝐱𝐭 𝐨𝐯𝐞𝐫𝐥𝐨𝐚𝐝 Skills load progressively: • Metadata → Instructions → Resources Only what’s needed enters the context window, when it’s needed. ✅ 𝐂𝐨𝐦𝐩𝐨𝐬𝐚𝐛𝐥𝐞, 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞‑𝐫𝐞𝐚𝐝𝐲 𝐝𝐞𝐬𝐢𝐠𝐧 Multiple skills can be combined to build complex, repeatable workflows that match how your team actually ships software. And here’s the real shift: 👉 𝐂𝐮𝐬𝐭𝐨𝐦 𝐢𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧𝐬 define how code should look 👉 𝐀𝐠𝐞𝐧𝐭 𝐒𝐤𝐢𝐥𝐥𝐬 define how work should be done This is a big step toward 𝐢𝐧𝐭𝐞𝐧𝐭‑𝐝𝐫𝐢𝐯𝐞𝐧 𝐀𝐈 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭—where developers describe what they want to achieve, not how to prompt for it. If you’re still thinking of Copilot as “autocomplete for code,” 𝐀𝐠𝐞𝐧𝐭 𝐒𝐤𝐢𝐥𝐥𝐬 𝐚𝐫𝐞 𝐲𝐨𝐮𝐫 𝐢𝐧𝐯𝐢𝐭𝐚𝐭𝐢𝐨𝐧 𝐭𝐨 𝐭𝐡𝐢𝐧𝐤 𝐛𝐢𝐠𝐠𝐞𝐫. #GitHubCopilot #AgentSkills #AIAgents #DeveloperProductivity #AIEnablement #EnterpriseAI