GitHub Copilot CLI adds ACP server support for easier 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. 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

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

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. 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/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

I’ve been digging into GitHub Copilot CLI’s new ACP server support, and it’s 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 🔌📐 Quick note: ACP support in GitHub Copilot CLI is currently in public preview, so some details may evolve 🚧 https://msft.it/6047QZCZ9 Why this matters 👇 🧩 IDE integrations Bring Copilot‑style agent workflows into editors or internal dev environments that don’t have first‑class Copilot plugins. ⚙️ CI/CD pipelines Orchestrate agentic coding tasks in automated workflows - generating patches, refactoring code, or helping update tests as part of builds. 🖥️ Custom frontends Build purpose‑specific interfaces for your workflow, like a lightweight internal “coding assistant console.” 🤝 Multi‑agent systems Coordinate Copilot alongside other AI agents using a standard protocol, instead of stitching together fragile adapters. The bigger takeaway for me: standard protocols are what turn “cool demos” into ecosystems 🌱 If you’ve 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 is aiming to reduce that friction - significantly. If you’re experimenting with agentic workflows, ACP server support in Copilot CLI is absolutely 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

🚀 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 

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