The pull request is a relic from a slower era of software development, and Entire's new Git forge makes that case impossible to ignore. Built specifically for collaboration between human developers and AI coding agents, Entire argues that the traditional PR model, designed for human review cycles measured in hours or days, breaks down when AI agents generate, review, and iterate on code at machine speed. We agree, and the implications for how teams ship software are more practical than theoretical. As How AI is reshaping coding workflows beyond traditional version control made clear, the infrastructure we built for human-only development is already straining under the volume and velocity of AI-generated changes. Entire isn't just proposing a tweak; it's rethinking the forge itself.
The core problem is that the PR model assumes a bottleneck: a human reviewer must understand, critique, and approve every change. That made sense when a team of five people produced twenty commits a day. It makes no sense when a single developer, assisted by an AI agent, can generate hundreds of valid code changes in an afternoon. Entire's approach treats AI agents as first-class collaborators rather than tools that dump output into a human queue. This isn't about removing humans from the loop, it's about redesigning the loop so that humans focus on architectural decisions and high-level review while AI handles the iterative, low-level work. Why Grab's Layered Defense Keeps AI-Generated Code From Breaking Apps showed one company's approach to managing that volume through automated guardrails. Entire takes that logic a step further, embedding the collaboration model directly into version control.
For teams already using AI coding assistants, the friction is real. Developers waste time opening PRs that are too large to review, waiting for approvals on trivial fixes, or manually rebasing branches that an agent has already rewritten. Entire's forge aims to eliminate those friction points by supporting asynchronous, high-speed iteration between human and machine contributors. The practical takeaway is this: if your team is generating code with AI today, you are likely working against your own tooling. Entire suggests a future where the tooling adapts to the workflow, not the other way around.
What remains to be seen is whether the development community will accept a forge that fundamentally alters the review process. Git's dominance is built on its flexibility, but also on the social contracts teams build around pull requests, code ownership, accountability, and quality gates. Entire's success will hinge on whether it can preserve those human values while enabling a faster, AI-native collaboration model. The specific detail to watch is how Entire handles attribution and blame when an AI agent and a human both touch the same line of code. That question is not just technical; it's cultural.