{ "story_id": "2db742c9e4fbc42fc86b5bfa3c3654fa", "desk": "drm3", "revision": 2, "published_at": "2026-09-02T23:12:01.000Z", "content_hash": "4c30ae7b76009016fd5d87bb6f719c176de4ccf24d7529f0597b169b4f723de6", "hash_basis": "sha256 over `headline\\ndek\\nprose`, plus `\\n` + the canonical citations JSON when any source is placed, plus `\\n#blog` for blogs", "basis": { "headline": "GitHub Copilot Can Now Approve Pull Requests in Public Preview", "dek": "GitHub Copilot can now approve pull requests, a feature available in public preview for Pro, Business, and Enterprise plans.", "prose": "GitHub Copilot can now approve pull requests, a feature currently available in public preview for GitHub Copilot Pro, Pro+, Max, Business, and Enterprise plans. [^1]\n\nOn 2026-09-02, GitHub announced a series of updates to its AI coding assistant GitHub Copilot aimed at reducing token usage and operational costs without compromising task quality, as detailed in a blog post by GitHub engineers Erik Kristensen and Napalys Klicius. [^2]\n\nLoop engineering is the discipline of designing workflows where AI agents run unattended to build, test, and fix software, replacing the need for manual code writing. [^3]\n\nAn approval assessment alone does not count toward merge requirements unless the feature is explicitly enabled by administrators. [^4]\n\nEvery Copilot code review now includes an approval assessment in the overview comment to signal whether Copilot considers the pull request ready to approve. [^5]\n\nWhen enabled, Copilot can submit an approval that counts toward the repository's required-approvals rule, but its approval is dismissed if new commits are pushed after the approval. [^6]\n\nGitHub engineer Erik Kristensen stated on 2026-09-02 that the goal of Copilot's efficiency update is 'fewer tokens per completed task' rather than fewer tokens per call, shifting focus from individual token use to end-to-end task efficiency. [^7]\n\nGitHub engineers reported that batching results from background tasks such as long-running shell commands reduced token-related usage by 2.3%. [^8]\n\nGitHub engineers reported that shortening Copilot's internal prompts without affecting critical model behaviors resulted in a 2.9% cost reduction per active hour in controlled experiments. [^9]\n\nAI coding assistants like Claude Code, Cursor, and GitHub Copilot have moved past single-shot prompting to support autonomous agents that work through backlogs unattended. [^10]\n\nThe bottleneck in AI software development has shifted from the ability of AI to write code to the ability of humans to trust AI to run unwatched. [^11]\n\nYash Thakker, founder and CEO of AISOLO Technologies, stated that loop engineering involves setting up programs that call AI agents on a schedule or trigger, such as every 15 minutes or when a build fails. 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