Download or update GitHub Mobile today from the Apple App Store or Google Play Store to get started.
This feature decouples long-running AI agent tasks from the local workstation. It allows engineers to maintain oversight and control over complex refactoring or scaffolding jobs while away from their desks, increasing the flexibility and continuity of agentic development workflows.
The best GitHub Copilot workflows don’t happen one–thing–at–a time. You might have an agent refactoring a module in VS Code, another debugging tests in the CLI, and a third scaffolding a new feature in the background.
Managing all of that used to only be possible from your desk. The moment you stepped away from your laptop, you lost visibility into every session you had running.
Now, developers can take their GitHub Copilot agent anywhere, with remote control for GitHub Copilot CLI sessions, now generally available on github.com and the GitHub Mobile app. We’re also introducing remote control in VS Code and JetBrains IDE, making GitHub Copilot truly multi-surface and available across any device.
Start a Copilot session in VS Code or the CLI, take it on the go with /remote on. Your session will be available on github.com and the GitHub Mobile app. Developers will experience one continuous workflow across CLI, VS Code, web, and mobile. Remote control works with any repository as well as directories without repositories, so you can take your work on the go, regardless of set up.
Open your session on any device to track progress as it happens. See exactly what Copilot is doing in real time, from the plans it’s researching, files it’s reading, the changes it’s making, to the commands it’s running.
Send additional instructions to a running session from anywhere using natural language. If an agent is heading in the wrong direction, you can send a follow-up to redirect it. Or you can tell your agent to expand scope while a task is in progress. Approve or deny permission requests and manage your sessions on the go.
Remote control enables a complete developer workflow once a session is sent to the web or GitHub Mobile app. For example, using Copilot CLI you could:
/planand scaffold with Copilot CLI./remote onto monitor progress in the GitHub Mobile app or web./remote on brings everything together, removing the pain of switching surfaces.
Your sessions are only visible to you. Remote control maintains full privacy; no one else can see or access your sessions.
Remote control is more than a convenience feature. It’s another step toward an end-to-end agentic platform.
Install GitHub Copilot CLI to get started in the CLI.
Or, if you’re already using the latest version of GitHub Copilot CLI or GitHub Copilot in VS Code, there’s nothing new to install. Start a session as you normally would, then use /remote on to send it to the web or mobile.
To learn more and for more detailed instructions, view our remote control documentation for CLI, VS Code, and JetBrains.
Download or update GitHub Mobile today from the Apple App Store or Google Play Store to get started.
The post Take your local GitHub sessions anywhere appeared first on The GitHub Blog.
Continue reading on the original blog to support the author
Read full articleThe Copilot SDK allows engineers to build custom AI tools for specific workflows. This server-side architecture pattern enables secure, scalable integration of LLMs into mobile and web apps, automating high-toil tasks like issue triage while protecting credentials.
Optimizing agentic delegation is critical for reducing latency and failure rates in AI tools. This research shows that more delegation isn't always better; selective orchestration improves reliability and speed by minimizing handoff friction and redundant tool calls.
False positives in security tools cause alert fatigue and erode developer trust. By using LLMs to understand code context, GitHub reduces noise by over 75%, ensuring engineers spend time fixing real vulnerabilities rather than triaging non-sensitive strings.
Integrating LSP servers into GitHub Copilot CLI replaces fragile text-search heuristics with precise semantic analysis. This enables the AI agent to accurately resolve types and definitions, significantly improving its reliability and effectiveness in complex codebases.