Entire, the startup founded by ex-GitHub CEO Thomas Dohmke, launches a global cloud-hosted GitHub alternative introducing Trails, an agentic code review system that replaces traditional pull requests with AI-powered review agents and continuous evaluation.
- Replaces manual pull requests with AI-evaluated Trails review system
- Integrates Git hosting, continuous delivery, and agent feedback in one platform
- Supports customizable AI model configurations for review agents
Infrastructure signal
Entire launches a global cloud-native Git hosting platform designed for private repositories, with plans to extend support to public and self-hosted Git nodes. The platform blends source code hosting infrastructure with advanced continuous integration and continuous delivery mechanisms, aiming to embed continuous memory features directly into the development lifecycle.
This new infrastructure approach is built to support agent-driven automation that continuously evaluates code changes on every push, providing real-time insights across multiple sites worldwide. By integrating agent feedback tightly with the platform’s infrastructure, Entire aims to improve operational reliability and reduce cloud costs associated with manual review overhead.
Developer impact
Entire redefines the developer workflow by replacing traditional pull requests with Trails, an AI-powered review dashboard that scores code changes based on risk, intent consistency, and other custom evaluative metrics. Developers can see automated feedback instantly, enabling faster iteration cycles and reducing time spent on manual review tasks.
With Trails, developers interact with customizable AI review agents using JSON configuration files, choosing from models like Claude or OpenAI. This transforms the developer lifecycle, making agent collaboration natural, flexible, and integrated into everyday communication tools such as Slack, reducing context switching while increasing productivity.
What teams should watch
Teams should monitor how Entire’s agentic code review impacts deployment velocity and release confidence, particularly if auto-approved merges based on AI evaluations replace detailed human reviews. Observability around agent-generated evaluations and continuous memory will be critical to validating changes and maintaining security posture.
Development and operations teams need to evaluate integration points between Entire and their existing CI/CD pipelines and observability stacks, understanding how agent feedback interfaces with broader platform APIs and database workflows. The customization potential in review agents presents opportunities but also requires governance to ensure alignment with organizational risk tolerance and quality standards.