AI Code Review in Practice: How Entire Turns Trails, Continuous Memory and Faster Git Hosting into a Human-Agent Workflow

Once a team puts coding agents into its daily pipeline, the first thing to break is rarely model quality. It is judgment. When an agent can publish a long change description faster than a person can read it, the review page stops being a decision surface. Thomas Dohmke, co-founder and CEO of Entire, frames the company's release as the first Git forge built for collaboration between humans and agents, available today to any developer with special optimization for AI-native teams and their agents. Rather than re-narrate the announcement, this AIApplications piece asks three practical questions: which step of your workflow changes, how do you operate it, and under which conditions is adoption worth the effort?

The Application Problem the Forge Is Aimed At

Under the classic pull-request model, comments accumulate in a linear timeline, CI reports a status below them, and the merge button follows. As coding and code review agents push longer descriptions into that timeline, the pull request becomes more like an audit log read by the agent, leaving developers without the signals needed to decide whether they can merge confidently. That is a workflow failure, not a model failure: the information a human needs has been pushed off the top of the page.

Entire's offering rests on three primitives — Trails, distributed Git hosting, and Continuous Memory — and the company is also shaping an open ecosystem so developers can select the right tools for each task. Read as an application stack, each primitive owns one layer of the day-to-day loop: Trails carries judgment, hosting carries control and throughput, Continuous Memory carries context.

Running a Review Cycle on a Trail: the 3-Step Operating Path

A Trail places decision-relevant information at the top of the page as eval scores, recalculated on every push. The target is simple: show whether enough loops between coding and review agents have produced a stable state, or whether another iteration is needed. A merge should happen only when confidence is high, risk is low, and every finding has been cleared. Here is how the lifecycle works in 3 steps.

Step 1 - Open the Trail where the work actually starts

A Trail can begin on Entire by filing a bug report or a feature request. It can also be started directly from an agent before or after implementation, similar to opening a pull request when code is ready to push. Because a Trail can span multiple repositories, related changes across services or packages are grouped together instead of being reviewed one pull request at a time.

Step 2 - Let the review agents score every push

When code is pushed to a Trail, Entire triggers review agents to assess the commit. Picture a jury judging an ice-skating performance: each agent runs independently in a cloud sandbox and submits a score with rationale. Across many Entire repositories, Security, Slop, and Human Review Required evals are used. If the Slop score is orange, a developer can instruct the agent to keep correcting and pushing based on that feedback until the score turns green. The current run and the last 5 runs are shown for each eval, because one-shot loops often miss a critical issue and repeating loops to stabilize scores is important for agentic coding.

For teams customizing the loop rather than consuming it, each project on Entire comes with pre-defined agents for code quality, security, and slop that can be enabled per repository, and custom agents can be configured through the Entire user interface or as .json config in the repo. Internally, Entire uses Claude Code and Codex with the latest models, running on isolated cloud sandboxes provided by Daytona and E2B.

Step 3 - Close every finding before the merge gate opens

With every push, agents can also return specific findings and suggested fixes at different severity levels. Open findings stay visible in the Trail, while resolved findings are hidden but retrievable through a menu, keeping users alert to what remains unresolved. A finding can be addressed or dismissed by agents or human developers, and people can post findings and comments just as they would in a pull request. To prevent merging before agent findings are handled, Gates can be enabled per repo to define what must be satisfied before approval — and Gates can also include human approvals and passing CI checks.

Continuous Integration as the Guardrail for Agent Output

Continuous Integration was first proposed by Grady Booch all the way back in 1991. It has been crucial to modern software development, enabling teams to deploy hundreds of times per day. Because agents now write more code, CI is even more important as a safeguard, reducing the chance that an agent changes something the prompt did not intend. Entire adds first-class CI integrations to Trails, beginning with Buildkite and Depot.

Buildkite: project-level configuration, Trail-level feedback

On Entire, CI is configured at the project level because multiple repositories inside a project often use the same infrastructure. After Buildkite is connected, every push builds either on Buildkite's infrastructure or on the user's own infrastructure. The result appears as a check on the Trail, and a failing build blocks merging. Agents can automate the full loop through Buildkite's API, CLI, or MCP. For teams using GitHub Actions, a workflow can be migrated by running bk pipeline convert without rewriting it.

Depot: keep the workflows you already built

Depot provides fast, reliable software delivery infrastructure for developers and coding agents, compatible with GitHub Actions, and Depot CI runs existing workflows without GitHub in the middle. Teams can connect Depot from Entire in a few clicks and create a Depot account within the same flow. Then depot ci migrate discovers GitHub Actions workflows, promotes them to Depot CI workflows, and flags anything that needs review. Companion commands support importing secrets and variables, so migrated workflows can run within minutes. Teams keep the workflows they built and get fast, reliable CI from the first run, billed by the second, meaning faster jobs cost less.

Hosting Decisions: Throughput, Residency, and Rollback Paths

Before distributed version control became mainstream, repositories and their history lived on systems such as Subversion. Git changed that model: developers could create commits and branches, merge, or even rewrite history locally, then reconcile later when needed. The Git protocol supported distribution and decentralization, but forges repeatedly centralized the server side. Entire views that centralized model as poorly suited to the future because it lacks scalability, resilience, and control over where code is stored.

Starting now, users can create and natively host a repository on Entire, or mirror one from GitHub. The platform is powered by a new source control server scaled with a distributed object store and a transactional ref database that remains compatible with Git. According to open-source benchmarks, pushes are up to 89x faster than GitHub and 6x faster than Cursor Origin.

Forge	Pushes per second
Entire	418
Cursor	65
Pierre	15
GitLab	10
GitHub	4.7

The same benchmark presentation highlights pushes up to 89 × faster than GitHub — a number that matters operationally only when agents are pushing continuously, which is exactly the usage pattern Trails assumes.

Pick a primary region, then decide how far to replicate

Users and organizations can replicate projects and repositories across multiple regions, or keep them in a chosen region, such as one needed for data residency requirements. Entire is live today in the US, EU, Australia, and India. To begin, a repository can be created on Entire from the web interface or from an agent using the Entire CLI, and repositories live inside projects belonging to an organization or a personal account. When a project is created, the user picks a primary region and can then replicate repositories across one or more regions.

Keep GitHub as source of truth and mirror instead

If GitHub should remain the source of truth, that remains possible. A repository can be mirrored by authorizing the GitHub App for Entire, then selecting the repo and regions on Entire. Trails can be used on top of pull requests, just as with a natively hosted repository. During mirroring, push traffic routes back through GitHub. If a service disruption occurs, Entire will keep serving clones and fetches from the regional mirror, and agents can keep pushing to Entire-native branches — any branch prefixed entire/unmirrored/ — until GitHub is back.

Move for good when you are ready

Two options exist for moving a repository from GitHub. When mirroring in the United States, users can detach the mirror by opening repository settings, clicking Detach from GitHub, and choosing the destination project. Alternatively, users can migrate directly from GitHub without mirroring using the entire-migrate plug-in, which handles the process. Users can also fetch from GitHub and push to Entire from a local machine or inside a CI/CD workflow.

Continuous Memory: The Feature With the Clearest Payback for Application Teams

Every day, developers and agents make decisions, discover constraints, try new approaches, and learn what works while building features and squashing bugs. Yet much of that knowledge disappears when a session ends. Starting a new session opens a new context window, forcing the agent to relearn information that is not already in the context, which is slow and also expensive.

Continuous Memory is introduced as a way to carry understanding forward. If CI brings code together and CD ships it, Continuous Memory preserves the context needed by agents: the repository becomes the memory the agent should need. Two commands in the Entire CLI make the concept possible — entire-graph, which maps how code fits together and in testing saved up to 71% on tokens, and entire-brain, which indexes agent sessions and attaches them to the graph to create durable memories retrievable faster than grepping through the codebase. Both plugins are open source and local first, and they are designed to work together: entire-brain builds on entire-graph's map of code.

entire-graph: a precomputed map the agent reads instead of searching

entire-graph hands an agent a precomputed map of a Git repository: ranked code search plus definitions, callers, types, routes, and change impact, each with file:line locations. The built-in analyzer parses the repository locally with tree-sitter and makes no network requests, model calls, or API-key lookups — which is why it can be adopted ahead of any hosting decision.

entire-brain: from personal session history to a team memory space

entire-brain creates a durable memory layer for a repository, allowing agents to have the right context at the right time: the why behind the "what changed." Under the hood, entire-brain indexes entire-graph's map of code and binds it to past agent sessions recorded in Git history via the Entire CLI. For example, when fixing a rounding bug that creates a mismatch error in a billing system, entire-graph traces the invoice calculation through every caller and test, while entire-brain gives the agent the intent behind current logic, the fixes already shipped and why, or what tests broke the last time someone changed it.

entire-brain can also connect to the Entire backend, enabling a team's agent memories to live in one shared space instead of individual machines. Each Trail then shows the memories the agent created, so reviewers can see what decisions the developer made and what facts the agent learned. To start, set up entire-graph and entire-brain in the repository.

Common Misreads Before You Pilot This

• Reading one green run as proof. The current run and the last 5 runs are shown per eval precisely because one-shot loops often miss a critical issue; stabilization takes repeated loops.
• Assuming evals replace tests. Trails carry scored review, while CI stays the safeguard against an agent changing something the prompt did not intend.
• Turning Gates on without owners. Gates define what must be satisfied before approval and can include human approvals and passing CI checks, so the threshold has to be agreed with the team first.
• Confusing mirroring with migrating. While a repository is mirrored, push traffic still routes back through GitHub; only detaching or migrating ends that dependency.
• Expecting memory to accumulate by itself. Continuous Memory requires setting up entire-graph and entire-brain in the repository, and team-wide memory requires the Entire backend connection.
• Treating regional hosting as a checkbox. Region choice is the lever for data residency requirements, so it should be decided per project, not globally at signup.

Action Recommendations for Teams Running Coding Agents Today

1. Baseline your current review flow first: how long a pull request waits for a human signal, and how many findings arrive after merge. Those two numbers are what a Trail changes.
2. Open one Trail for a change that spans several repositories so you can test cross-repo grouping rather than a single-file diff.
3. Enable the three shared evals first — Security, Slop, and Human Review Required — and only then add custom agents via the interface or .json config.
4. Connect Buildkite or Depot at project level before enforcing Gates, so a failing build blocking a merge reflects your real infrastructure.
5. If your automation already lives in GitHub Actions, test the conversion path with bk pipeline convert or depot ci migrate on one workflow and read what gets flagged for review.
6. Decide residency and replication explicitly against the US, EU, Australia, and India regions available today, and start with mirroring if GitHub must stay the source of truth.
7. Install entire-graph and entire-brain in one pilot repository, then compare tokens per session against your current setup, where testing reported savings up to 71%.
8. Report what works and what needs improvement through the project's Discord channel; building a new developer platform requires community collaboration.

Where This Workflow Story Goes Next

One year after the project began, the team says it is excited about what Entire has already become and what agents will work on next, and invites users to try it and report what they love or want improved through Discord, so that the forge of the future can be designed together. For application teams, the practical takeaway is narrower and more useful: the bottleneck you can feel this week is not agent capability but the absence of a shared decision surface — and Trails, Continuous Memory plus regional hosting are an attempt to supply exactly that.

Analysis for the AIApplications desk by David Nakamura, AI & Data News. Corrections and tips: editorial@aidatanews.com.