Entire

Entire positions itself as a Git forge built specifically for collaboration between humans and AI agents. Rather than treating agent output as an afterthought, it stores intent alongside code on a fast, distributed Git network, so the reasoning behind changes stays attached to the commits that produced them.

The platform replaces the traditional pull request with an eval-centered review flow. Reviewers see gates for risk, intent drift, slop, and test coverage, plus approvals and check results in one place. Continuous memory turns the repository into a multi-agent brain, while agentic search queries code, commits, and sessions together. The Entire CLI captures every agent session from the terminal, and native CI integrations target agent-scale throughput.

It is aimed at engineering teams adopting agentic coding at volume, especially those frustrated by agents burning tokens searching for context or producing changes that are hard to audit. Platform and DevEx leads evaluating how to govern agent contributions are another natural audience.

To get started, connect a repository to the distributed Git network and install the CLI so sessions are recorded automatically. Define the review gates that matter for your team, then use agentic search to trace why a change was made. Expect a shift away from PR habits, and plan onboarding time for the new review model.

Entire is best understood as infrastructure for teams that treat agents as real collaborators rather than autocomplete.