Entire's Git Forge Rethinks Git Collaboration for Coding Agents

Entire launched a multi-agent Git forge built specifically for human developers and autonomous AI coding agents collaborating on software repositories. According to InfoQ's report, the startup is led by Thomas Dohmke, the former chief executive officer of GitHub who now serves as Entire's chief executive officer. Entire bases its platform on the premise that traditional source control hosting and legacy pull request workflows fail when high-speed coding agents continuously generate, review, and iterate on code. The underlying infrastructure uses a fully distributed architecture engineered to enable Git push speeds up to 89 times faster than GitHub while adding native support for third-party continuous integration platforms such as Buildkite and Depot.

This coverage examines how Entire replaces standard pull request timelines with iterative evaluation loops, structured code confidence scores, and persistent repository memory. The platform targets engineering teams and software developers who find standard code review models inadequate as autonomous agents produce high-volume code changes across modern repositories. Rather than forcing human engineers to sift through dense, agent-generated commit logs, Entire reorients software version control around asynchronous orchestration, versioning, and rigorous agent auditing alongside human oversight.

Entire's Git Forge Rethinks Git Collaboration: what actually changed

The fundamental shift in Entire centers on moving away from the conventional pull request model toward a collaboration mechanism called trails. In traditional platforms like GitHub or GitLab, asynchronous human-to-human review relied on conversation timelines that have now degraded into audit logs read primarily by agents rather than human reviewers. Entire's trails replace this timeline by pulling critical evaluation metrics directly to the top of the interface. When agents push changes, the platform displays eval scores and rationales covering code quality, security posture, code slop, and explicit flags indicating whether a human code review remains necessary.

Instead of treating review as a one-off signoff, Entire surfaces the last five evaluation runs alongside the current run for each metric. This multi-run history ensures developers can track whether an agent is stabilizing its code scores across repeated feedback loops or introducing regressions. Human developers configure these evaluation agents through Entire's graphical interface or through repository-level JSON configuration files. Furthermore, teams can establish repository-level Gates that enforce strict approval criteria, mandating that all review agent findings must be marked as resolved or closed before any code merge is permitted to proceed into the codebase.

Entire's Git Forge Rethinks Git Collaboration: how it works

A trail can be initiated manually, from an incoming bug report, or from a feature request, and a single trail can span across multiple Git repositories. Whenever a developer or an agent pushes updated source code, a review agent operating inside an isolated cloud sandbox automatically evaluates the submission. If an automated metric like the slop score returns an orange indicator, human developers can instruct their coding agent to refine the patch and push another commit. That action immediately triggers a fresh evaluation cycle in the cloud sandbox, allowing the system to verify the improvement and update the visible eval history.

Underpinning this workflow is continuous memory, an architecture designed to maintain accumulated project knowledge across separate agent sessions. Entire implements continuous memory through two dedicated open-source tools called entire-graph and entire-brain. The entire-graph tool builds a precomputed map of the repository, indexing ranked code searches, function definitions, callers, types, and application routes. In parallel, entire-brain combines this precomputed structural map with historical Git data and past agent sessions to create a persistent memory layer. This architecture provides agents with contextual history, preserving the reasoning and rationale behind previous repository modifications.

Entire's Git Forge Rethinks Git Collaboration: why it matters

As former GitHub CEO Thomas Dohmke pointed out, the historical purpose of GitHub and GitLab was facilitating asynchronous collaboration between human developers through pull requests and branch-based version control. However, autonomous agents introduce an operational bottleneck where one-shot generation loops frequently overlook critical defects. Pull request timelines quickly fill with automated noise that deprives human engineers of the clarity needed to merge changes with confidence. Entire shifts the core objective of the forge from human code discussion to the structured orchestration, versioning, and automated auditing of agents operating alongside humans.

The persistence of context directly addresses chronic shortcomings in software documentation and code archaeology. Commenting on the announcement, Haimantika from Entire explained that the fundamental failure in software development has not been programming languages or toolsets, but the loss of developer reasoning between the engineer and the final code artifact. In typical workflows, commit messages are poor, documentation rots over time, and the historical justification for specific code decisions is forgotten. Because agents lack human institutional memory, Entire uses entire-brain and entire-graph to capture and retain that reasoning across iterations.

Entire's Git Forge Rethinks Git Collaboration: who is affected

The immediate impact falls on software engineering teams, DevOps practitioners, and developers currently attempting to integrate autonomous coding agents into conventional pull request pipelines. Under standard setups, human developers are forced to manually inspect sprawling diffs generated by machines without standardized confidence metrics. With Entire, developers interact with top-level evaluation summaries that indicate when confidence is high, security risk is low, and all findings are cleared. Teams already relying on external build pipelines can connect Entire directly to existing CI workflows through its integrations with platforms like Buildkite and Depot.

Software developers who direct coding agents also gain a structured mechanism to loop agents repeatedly until code quality metrics stabilize. By providing concrete eval criteria, developers can hand tasks back to their agents iteratively rather than performing manual refactoring themselves. Meanwhile, teams running multi-repository architectures are affected by Entire's capability to track a single trail across separate repositories, eliminating the fragmentation that occurs when an agent's coordinated feature or bug fix requires individual pull requests opened across several disconnected projects.

Entire's Git Forge Rethinks Git Collaboration: what to watch

As Entire rolls out its platform, observers will be watching whether its evaluation scores, continuous memory framework, and distributed architecture can establish a new standard for AI-native source control. Entire enters a rapidly shifting market alongside several other new source code management contenders. These competing efforts include Zed DeltaDB, Anthropic Scion, East River Source Control, Cursor Origin, and Radicle, all of which are exploring alternative data layers, forge designs, and distributed versioning concepts to support modern programming workflows.

Adoption will depend on how effectively teams utilize open-source components like entire-graph and entire-brain within their existing engineering toolchains, as well as how developers tune their repository-level JSON configuration files and automated Gates. The broader industry will monitor whether iterative review loops inside cloud sandboxes can meaningfully prevent low-quality code and subtle security flaws from reaching production environments, validating whether multi-agent Git forges can successfully replace the decades-old pull request model.