GitHub

GitHub has released Code Quality as a generally available feature for GitHub Enterprise Cloud and GitHub Team subscribers. The tool aims to address code review challenges created by AI-accelerated code generation.

GitHub released its June 2026 update for Copilot in Visual Studio, adding a trust layer for Model Context Protocol (MCP) servers, improved usage analytics visibility, and the first C++ language scenarios for the AI coding assistant.

GitHub reduced costs for Copilot code review by migrating to Unix-style code exploration tools. The company found that more sophisticated tools made reviews worse, leading them to reshape agent workflows around pull request evidence.

GitHub reduced costs for Copilot's code review feature by replacing more sophisticated structured tools with simpler Unix-style code exploration commands. The company found that better tools paradoxically made the system worse, leading to a redesign focused on pull request evidence-based workflows.

GitHub has deployed agentic workflows that automatically generate documentation pull requests from merged code changes. The system, used by the Aspire team, creates SME-reviewed documentation updates across repositories, reducing the lag between feature releases and documentation updates.

GitHub has deployed agentic workflows that automatically generate documentation pull requests from merged product changes. The system, built by GitHub's Aspire team, creates SME-reviewed documentation to reduce the lag between code releases and updated docs.

GitHub has extended access to Moonshot AI's Kimi K2.7 model to Copilot Business and Enterprise customers. The model was initially made available to Copilot Pro, Pro+, and Max plans on July 1, 2026.

GitHub has made browser tools for Copilot in VS Code generally available. The feature allows Copilot agents to control real browsers, navigate live web applications, and integrate findings back into the development environment.

GitHub has published benchmark results for its Copilot agentic harness, evaluating performance across multiple tasks and over 20 different models. The company claims the framework achieves leading token efficiency while maintaining flexibility in model selection.

GitHub published benchmark results for its Copilot agentic harness, which supports more than 20 models from providers including Anthropic, OpenAI, and others. The company claims the harness delivers leading token efficiency while maintaining flexibility across model choices.

GitHub has released the redesigned terminal interface for GitHub Copilot CLI to general availability. The update, previewed at Microsoft Build 2026, introduces a tabbed layout for working with GitHub directly from the command line.

GitHub has released technical details on Qubot, an internal analytics agent powered by GitHub Copilot that enables employees to query company data using natural language. The agent represents GitHub's implementation of AI-assisted data analysis for internal operations.

GitHub has built Qubot, an internal analytics agent powered by GitHub Copilot that allows employees to query company data using natural language. The project represents GitHub's approach to building domain-specific AI agents for data analysis tasks.

GitHub has updated Copilot's architecture to optimize token consumption through improved context handling and model routing. The changes aim to make user credits last longer by reducing unnecessary token usage in coding sessions.

GitHub has improved how Copilot handles context and routes requests to models, reducing token usage per session. The changes aim to make user credits last longer by eliminating wasted tokens.

GitHub published documentation outlining slash commands for Copilot CLI, the company's terminal-based AI coding assistant. The guide targets developers new to using AI agents directly in command-line environments.

GitHub has published beginner documentation for Copilot CLI's slash commands. The guide covers how to control the terminal AI agent using command shortcuts.

GitHub has updated Copilot CLI to reduce unnecessary handoffs between AI models. The improvement delivers faster command execution through better orchestration logic, implemented without adding new user configuration options.

GitHub has updated the orchestration logic in Copilot CLI to make it more selective about when to delegate tasks between language models. The changes reduce unnecessary handoffs and improve response times without introducing additional configuration settings.

GitHub has added Language Server Protocol (LSP) support to Copilot CLI, replacing its previous grep and decompile-based code analysis. The update enables the AI coding assistant to use structured language intelligence for understanding codebases.