Articles

The article introduces GitHub Copilot Agent HQ's Mission Control, a unified interface designed to streamline the management of coding agent tasks across multiple repositories. It emphasizes a critical mental model shift from sequential to parallel agent interaction, empowering developers to assign, oversee, and review numerous tasks concurrently. The guide offers practical advice for effective orchestration, including crafting clear, context-rich prompts, utilizing custom agents via `agents.md` files for consistency, and actively monitoring real-time session logs to identify and address issues like scope creep or failing tests. It also covers the 'art of steering,' providing specific intervention techniques, and outlines efficient review strategies such as examining session logs, changed files, and leveraging Copilot for self-review. The core benefit highlighted is the ability to unblock and progress multiple workstreams within the timeframe previously dedicated to a single task, driven by disciplined prompt engineering and timely human intervention.

GitHub Copilot now supports custom agents, enabling developers to integrate their team's specific tools, workflows, and standards directly into Copilot. These Markdown-defined domain experts extend Copilot's capabilities beyond code generation to cover the entire software development lifecycle, including observability, security, and Infrastructure as Code (IaC). Agents can be deployed at repository, organization, or enterprise levels and operate across Copilot CLI, VS Code Chat, and github.com. The article highlights partner-built agents for various domains like incident response (PagerDuty), security (JFrog), database management (Neon), and experimentation (Amplitude), showcasing real-world workflow automation with simple commands. This innovation helps teams maintain consistency, reduce context repetition, share expertise, and work directly with their DevOps, security, and observability systems via the Model Context Protocol (MCP), significantly enhancing developer productivity and compliance.

The article details the remarkable rise of Home Assistant, an open-source, local-first home automation platform, which has seen explosive growth in contributors and installations, ranking among the fastest-growing open-source projects. It emphasizes Home Assistant's core philosophy of local control, privacy, and interoperability, enabling users to orchestrate thousands of devices without cloud dependency, running on their own hardware. The piece delves into the architectural challenges of managing a combinatorial explosion of devices through a general-purpose abstraction layer, effectively operating as a real-time OS for the home. It also explores the innovative community model, where developers contribute and test integrations in their own homes, ensuring high software quality and rapid development velocity. Furthermore, the article introduces Assist, Home Assistant's privacy-aware voice assistant that prioritizes deterministic, local commands with optional AI integration, supported by open hardware initiatives. This local-first approach, solidified by the Open Home Foundation, ensures user ownership and sustainability, making the home programmable and resilient against vendor lock-in.

This article introduces GitHub Copilot Spaces, a solution designed to address the common developer pain point of lacking immediate context when tackling issues. It highlights how developers often spend significant time 'hunting' for relevant information (old pull requests, design documents, security guidelines) before even starting to code. Copilot Spaces solves this by allowing users to curate a 'project knowledge bundle' containing files, pull requests, issues, repositories, notes, and architectural decisions. This centralized context enables GitHub Copilot to provide responses that are grounded in the actual codebase, rather than generic suggestions. The article demonstrates a practical debugging scenario, showing how to create a space, add relevant project documentation and an issue URL, and then instruct Copilot to generate a debug plan and propose a pull request with cited sources. Key benefits include automatic syncing of content, integration with IDEs via the GitHub MCP server, and applications beyond debugging, such as feature planning and knowledge sharing for team onboarding. Upcoming features like a public API and support for additional file types are also mentioned.