Effective today, the startup Entire delivers a full‑stack Git service that eliminates the conventional pull‑request step. Instead, every push triggers a set of AI‑generated evaluations—collectively named Trails—that score the change and feed back to coding agents. For engineers who rely on automated pipelines, this redesign changes where and how code quality decisions are made.
Agentic code review replaces pull requests
Trails presents a dashboard where configurable evaluation modules run on each commit. The modules, powered by Claude or OpenAI models, return risk scores, intent‑drift metrics, and other custom signals. Results are exposed through a web UI and an open‑source CLI, allowing agents to adjust their output and push revised commits without a human reviewer intervening. The platform ships with a set of default agents, but teams can supply a JSON‑based configuration to define additional checks or swap model providers.
Platform architecture and performance claims
Underlying Trails is a Git‑compatible server that distributes repository data across regional mirrors in the United States, Europe, India, and Australia. Private repositories are available at launch; public repositories and self‑hosted Git nodes are slated for future releases. The vendor asserts that its distributed storage can push changes up to 89 times faster than GitHub and six times faster than Cursor’s Origin, though the claim is not benchmarked in this article.
Operational impact: CI integration, continuous memory, and model configuration
Entire partners with Buildkite and Depot to let teams migrate existing GitHub Actions pipelines. The migration path involves converting the workflow definition into a Buildkite bk pipeline or a Depot CI configuration, then linking the repository to the new service. A third pillar, called Continuous Memory, records AI‑agent sessions in the repository and makes them queryable for future tasks. This creates a persistent “agentic brain” that can retrieve prior decisions, potentially reducing repetitive prompting.
Security and data‑governance considerations
Because private repositories are hosted in specific geographic regions, organizations can align storage with local compliance requirements. However, the Continuous Memory feature stores agent interaction logs alongside code, which may introduce new data‑exposure vectors if not properly scoped. Selecting Claude or OpenAI models also means that API credentials and usage quotas become part of the operational surface, requiring careful secret management.
Related CloudNinjas coverage: AI engineering.
What This Means For Practitioners
- Evaluate whether the AI‑driven evals meet your organization’s code‑quality policies before disabling human review.
- Map the regional repository mirrors to your data‑residency obligations and verify synchronization latency for distributed teams.
- Plan for secret‑management practices around model API keys, especially if custom JSON configurations are used.
- Test the migration path from GitHub Actions to Buildkite or Depot early, noting any feature gaps.
- Monitor agent run costs and the size of Continuous Memory logs, as they may become the next scalability bottleneck.
