A pull request was designed around a simple unit of work: a person writes a change, proposes a diff, and asks another person to review it. Coding agents change the volume and shape of that work. They can produce multiple commits quickly, repeat a task across repositories, and revise code in response to automated feedback. The familiar review page can become a queue of diffs and comments while the real question remains unanswered: did the change accomplish what was intended?
That is the problem Entire is trying to address with Trails, a review workflow in its new Git forge. Rather than treating the pull request diff as the only review surface, Entire puts the task, agent activity, evaluations, and build results together. The company says agents can score customized evaluations on each push, while a human validates progress and merge gates determine when the change is ready.[1]
It is an ambitious proposal, but it should be judged as a workflow hypothesis, not as proof that human code review is obsolete. The useful comparison is not “old GitHub versus a magical agent forge.” It is diff-centered review versus evidence-centered review, and whether the second can preserve the strengths of the first.
A pull request is more than a diff viewer. In GitHub’s documented workflow, reviewers inspect files and changes, leave comments or suggestions, and submit a decision such as approve or request changes.[4] Branch protection can also require reviews and passing status checks before changes merge.[3]
That model has several durable advantages:
These are not incidental features. A repository needs a clear record of what changed, who approved it, and which checks passed. A faster code generator does not remove that need.
The weakness appears when the patch is no longer the whole story. A reviewer may need to understand the original request, the agent’s plan, which tests were selected, what changed after a failure, and whether the latest push invalidated an earlier review. A diff can show what changed without explaining whether the code solves the right problem.
Entire launched its forge on October 26, 2026. Its announcement describes Trails as a new review surface where the idea, agent trace, and outcome sit alongside the code. It says customizable evaluations can score each push, agents can address findings in a loop, and humans can validate progress.[1]
The company also describes a broader platform, not just a new pull request screen: Git-compatible hosting, integrations with Buildkite and Depot for CI, and “Continuous Memory” that connects codebase information and previous agent sessions.[1][2] Its blog says failing CI blocks merging on a Trail, while the announcement says the service can mirror GitHub repositories as well as host repositories natively.[2]
That distinction matters. Teams do not have to interpret the launch as “replace GitHub today.” Entire says repositories can be mirrored, and Trails can be used over pull requests while GitHub remains the source of truth.[2] For a cautious evaluation, mirroring a low-risk repository is a more reversible experiment than migrating a production codebase and its governance all at once.
Entire also advertises pushes up to 89 times faster than GitHub and up to six times faster than Cursor Origin, based on its own open-source benchmark.[1][2] Those are company-reported benchmark results, not an independent finding that the forge will make a team’s delivery 89 times faster. Push throughput is not review quality, merge safety, CI reliability, or time-to-correctness. Keep those measurements separate.
Pull requests optimize for human-readable change review. They make diffs, comments, approvals, and required checks the center of the workflow. This remains a strong fit when changes are small enough to inspect, maintainers need a clear discussion record, or the team’s approval process is built around reviewing code line by line.
Trails try to optimize for intent and evidence around agent work. The proposed unit includes the task and the agent’s work history, with machine-generated evaluations and CI results providing more context than a diff alone.[1][2] This may help when agents make repeated pushes and the reviewer needs to follow progress rather than re-open a separate comment thread for every iteration.
The tradeoff is that an evaluation score is not a substitute for understanding the change. An agent can produce a plausible score against an incomplete or poorly designed evaluation. Passing tests demonstrate that selected tests passed, not that the system has no defect. A session trace can explain the agent’s process, but length does not guarantee that the reasoning was sound.
The best version of an agent-first review system would therefore make evidence easier to inspect without hiding the source code. A reviewer should be able to move from task intent to relevant tests, from test results to the actual diff, and from an evaluation failure to the affected behavior. If the UI presents a green score more prominently than the code and the evaluator’s limits, it risks creating a new kind of automation bias.
A useful trial should answer a workflow question, not simply test whether the interface looks modern. Pick one low-risk repository and a handful of representative changes. Keep the existing source of truth and required merge rules intact during the trial.
For each change, record four separate signals: Do not combine these into a single “agent review score.” A tool might reduce review time while increasing missed defects, or improve context while adding operational work. Decide in advance which outcomes matter to your team.
For every evaluation that can affect merge readiness, ask: These questions apply to any forge that adds AI scoring, not just Entire. The goal is to make checks reviewable, repeatable, and appropriately scoped. An opaque “9/10” should not be treated as stronger evidence than a failing test with a clear reproduction.
Stay with pull requests for now if:
Run a bounded Trail trial if:
Do not migrate just because of a speed claim. First verify what the benchmark measured, reproduce it for your own workload where possible, and separately measure review outcomes. Push speed may matter for some distributed workflows. It does not establish that the resulting code is easier or safer to merge.
Entire’s launch is a useful signal that the pull request is no longer the only plausible interface for reviewing software. Agent-heavy development creates more activity around a change: prompts, plans, repeated edits, evaluation runs, and test results. A single diff-and-comment page may not organize all of that particularly well.
But a new surface does not eliminate the basic engineering obligations. Teams still need to know what was requested, what code will ship, which checks ran, who is accountable for approval, and what evidence supports the decision. Trails could make that evidence more visible. Pull requests already provide a mature way to inspect code and apply governance. The right choice depends on whether a new workflow measurably improves the review process without weakening its controls.
The takeaway: evaluate review systems by the quality and inspectability of the evidence they put in front of a human, not by how many agent-generated changes they can push per second.
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Have you tried an agent-first code review workflow, or does the pull request still fit your team? What evidence would make you trust an automated evaluation?
[1] [Entire launches an independent Git forge for agent-human collaboration](https://entire.com/news/entire-launches-an-independent-git-forge-for-agent-human-collaboration)
[2] [The Git Forge for Agent-Human Collaboration](https://entire.com/blog/the-git-forge-for-agent-human-collaboration)