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GitHub expanded Copilot code review to cover bot-authored pull requests, Copilot cloud agent pull requests, and very large pull requests. It also added resolution reasons for Copilot review comments.
GitHub’s August 27, 2026 changelog says Copilot code review can now review two categories of pull requests it did not previously cover: pull requests authored by bots when a review is requested automatically, and very large pull requests. The update is a product change to an AI review system, rather than a general change to GitHub’s pull-request workflow. The source does not state how many customers are affected or how frequently these cases occur.
For bot-authored pull requests, GitHub says organizations must enable the policy allowing members without a Copilot license to use Copilot code review on GitHub.com. Under that arrangement, the review can proceed even though there is no Copilot-licensed account to attribute it to, and GitHub says usage is billed directly to the organization. The source does not specify the billing unit, price, quota, or whether administrators receive additional controls.
The update also changes the treatment of pull requests opened by Copilot cloud agent. Previously, GitHub says automatically requested reviews of those pull requests fell back to a limited experience. Copilot code review can now provide what GitHub calls a full agentic review. Separately, GitHub says its former limit of 300 files or 20,000 lines of code no longer applies to large pull requests. The announcement does not identify a replacement upper bound or explain how review latency and cost may change for unusually large changes.
GitHub added a dropdown beside the “Resolve conversation” button on Copilot code review comments. Developers can select “Addressed,” “Won’t fix,” or “Incorrect” when resolving a comment. GitHub says these selections provide feedback to its product team and help improve the product. The source does not say whether the reasons affect future reviews for an individual repository, are used only in aggregate, or are exposed through reporting tools.
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The changes extend AI code review into workflows where software is produced by bots and remove a stated size limit. They also create a feedback signal about whether developers addressed, rejected, or disputed an AI comment.
The bot-review change matters because it brings AI review into a workflow in which the author of a pull request may itself be an automated software agent. That could help organizations apply a review step to code produced by Copilot cloud agent or other bots without requiring every bot-authored change to map to a licensed human account. At the same time, the source establishes a billing and policy dependency: the organization must explicitly enable the relevant setting, and the organization bears the stated usage cost.
The Copilot cloud agent change is significant within GitHub’s product ecosystem because it removes the limited-review fallback for automatically requested reviews of the company’s own agent-authored pull requests. In practical terms, a software agent can generate a change and another Copilot review capability can examine it under the organization’s configured automation rules. The announcement does not establish that this creates independent assurance, catches more defects, or replaces human review. It only describes a broader product capability and a change in the review experience.
Removing the stated 300-file or 20,000-line limit could make Copilot code review more usable for large dependency updates, generated changes, migrations, or other broad modifications. However, the absence of a replacement limit is important. Very large reviews may still face undocumented constraints, higher costs, longer processing times, or reduced usefulness if comments become difficult to prioritize. GitHub supplies no comparing large-pull-request review quality with smaller reviews, and no evidence in this source shows that the expanded capability improves software reliability.
The resolution-reason may improve the information available to GitHub about how developers judge AI-generated review comments. “Addressed,” “Won’t fix,” and “Incorrect” distinguish a fix from a deliberate decision and from an erroneous suggestion, which could be more informative than simply closing a conversation. But the source does not quantify expected quality improvements, describe how the data will be used, or address whether the selections are visible to repository participants beyond the comment itself. Those details affect the feature’s governance and workplace implications.
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The source does not provide review-quality measurements, a new maximum pull-request size, pricing details, or broader availability conditions. Organizations will need to assess accuracy, cost, and oversight as automated reviews cover more generated code.
Organizations considering the expanded bot-review workflow should watch the policy and billing consequences first. The source says automatic review of bot-authored pull requests is available when the policy allowing unlicensed members to use Copilot code review is enabled, and that usage is billed directly to the organization. GitHub does not publish pricing, caps, or examples in this announcement, so administrators still need those details before estimating the financial effect of broader automation.
The next practical question is how the full agentic review behaves on code opened by Copilot cloud agent. Useful evidence would include error-detection rates, false-positive rates, review completion times, and examples of issues found or missed. None of those measurements appears in the source. Human maintainers will still need to determine whether comments are technically correct and whether a pull request is safe to merge, particularly because the announcement describes capability expansion rather than an independent evaluation.
The removal of the former size limit warrants monitoring for a new operational boundary. GitHub says the 300-file or 20,000-line restriction no longer applies, but it does not state the maximum size that can be reviewed, how the system handles generated or repetitive code, or whether large reviews are priced differently. Users should look for documentation on limits, latency, failure handling, and controls for breaking large changes into smaller reviewable units.
The resolution reasons could become an important product-feedback mechanism if GitHub reports how they influence model or review-system improvements. For now, the source only says the selections provide valuable feedback to the product team. It does not say whether developers can export the data, correct a mistaken , or audit how the feedback is used. Those questions will matter to teams that want measurable oversight of AI review quality rather than a simple record that a conversation was closed.