What Problem Does Entire Solve for AI Coding Agents?

Entire addresses what Dohmke calls the next bottleneck in software development: the reviewability, auditability, and transparency of AI-generated code .

When human developers write code, their intent can often be inferred from commit messages, code comments, and design documents. But when an AI coding agent writes code, its session context — the prompts, reasoning steps, tool calls, and intermediate decisions — is ephemeral. It vanishes when the terminal session ends . Traditional Git captures the 'what' (code diffs and file states) but not the 'why' (the reasoning process) .

Entire means to make that agent session context a first-class, versioned, and searchable part of the repository, creating what some have described as a "black box flight recorder" for AI-generated code .

Key Features: Checkpoints and Beyond

The first product from Entire is Checkpoints, an open-source CLI (MIT licensed) that hooks directly into Git workflows . Its core functionality is straightforward:

• Automatic Context Capture: When a developer or agent runs git commit, Entire automatically captures the full agent session — including prompts, responses, tool calls, files touched, and token usage — and attaches it to the commit as a permanent checkpoint .
• Searchable Provenance: Developers can later query "why did this code change" and see the full transcript that led to any specific diff, enabling much deeper code review and debugging .
• Multi-Agent Support: Checkpoints works with a range of popular AI coding agents, including Anthropic's Claude Code, OpenAI's Codex, Google's Gemini CLI, OpenCode, and others .
• Clean Git History: Session metadata is stored on a dedicated entire/checkpoints/v1 branch, keeping the user's active branch clean and preserving a traditional Git workflow .
• Upcoming Context Graph: Beyond the CLI, Entire plans to ship a broader 'semantic reasoning layer' called the context graph, designed to help developers navigate the decision trees of multiple agents across the entire repository .

The Entire.io website also mentions a repo mirroring feature designed for "maximum agent throughput," and the GitHub organization (entireio) hosts the open-source CLI code .

Benchmarks and Performance

The reporting around quantitative benchmarks is limited. Available sources describe performance claims in qualitative terms — for example, that Checkpoints makes it "instantly" searchable to recover the context behind any line of code . No standardized quantitative benchmark numbers (e.g., latency, throughput, code review time reduction) were found in the credible sources reviewed. This appears to be a gap in public disclosure as of the launch period.

Funding Background

Entire launched with what lead investor Felicis called the largest seed round ever for a developer tools company .

How Entire Fits into the Broader Trend

Entire is one of several high-profile moves signaling that the developer tooling stack is being fundamentally rethought for an AI-agent-heavy world:

• Open-source and independent. Entire is positioning itself as an open, independent platform — not tied to any single AI model vendor. Dohmke has emphasized that openness should sit at the heart of the next developer platform .
• Git-native, not Git-replacement. Entire extends Git rather than replacing it. It treats agent context as first-class Git metadata, a different approach from fully decentralized alternatives like Radicle or GitButler's reimagined Git client .
• Parallel moves by other GitHub founders. Scott Chacon (co-founder of GitHub) raised $17M from a16z for GitButler, a reimagined Git client for the AI era. Both Dohmke (Entire) and Chacon (GitButler) are betting that the existing Git workflow is insufficient for agent-generated code, but they take different architectural approaches — Entire focuses on context capture and provenance, while GitButler focuses on a new Git client UX .
• Human-agent collaboration as a first principle. Entire's design philosophy is that humans and AI agents will co-author code, and version control must capture the human's intent and the agent's reasoning together — not just the file diffs . This is a broader industry shift away from human-only Git workflows toward "agent-native" DevOps infrastructure.

What Problem Does Entire Solve for AI Coding Agents?

Entire addresses what Dohmke calls the next bottleneck in software development: the reviewability, auditability, and transparency of AI-generated code .

When human developers write code, their intent can often be inferred from commit messages, code comments, and design documents. But when an AI coding agent writes code, its session context — the prompts, reasoning steps, tool calls, and intermediate decisions — is ephemeral. It vanishes when the terminal session ends . Traditional Git captures the 'what' (code diffs and file states) but not the 'why' (the reasoning process) .

Entire means to make that agent session context a first-class, versioned, and searchable part of the repository, creating what some have described as a "black box flight recorder" for AI-generated code .

Key Features: Checkpoints and Beyond

The first product from Entire is Checkpoints, an open-source CLI (MIT licensed) that hooks directly into Git workflows . Its core functionality is straightforward:

• Automatic Context Capture: When a developer or agent runs git commit, Entire automatically captures the full agent session — including prompts, responses, tool calls, files touched, and token usage — and attaches it to the commit as a permanent checkpoint .
• Searchable Provenance: Developers can later query "why did this code change" and see the full transcript that led to any specific diff, enabling much deeper code review and debugging .
• Multi-Agent Support: Checkpoints works with a range of popular AI coding agents, including Anthropic's Claude Code, OpenAI's Codex, Google's Gemini CLI, OpenCode, and others .
• Clean Git History: Session metadata is stored on a dedicated entire/checkpoints/v1 branch, keeping the user's active branch clean and preserving a traditional Git workflow .
• Upcoming Context Graph: Beyond the CLI, Entire plans to ship a broader 'semantic reasoning layer' called the context graph, designed to help developers navigate the decision trees of multiple agents across the entire repository .

The Entire.io website also mentions a repo mirroring feature designed for "maximum agent throughput," and the GitHub organization (entireio) hosts the open-source CLI code .

Benchmarks and Performance

The reporting around quantitative benchmarks is limited. Available sources describe performance claims in qualitative terms — for example, that Checkpoints makes it "instantly" searchable to recover the context behind any line of code . No standardized quantitative benchmark numbers (e.g., latency, throughput, code review time reduction) were found in the credible sources reviewed. This appears to be a gap in public disclosure as of the launch period.

Funding Background

Entire launched with what lead investor Felicis called the largest seed round ever for a developer tools company .

How Entire Fits into the Broader Trend

Entire is one of several high-profile moves signaling that the developer tooling stack is being fundamentally rethought for an AI-agent-heavy world:

• Open-source and independent. Entire is positioning itself as an open, independent platform — not tied to any single AI model vendor. Dohmke has emphasized that openness should sit at the heart of the next developer platform .
• Git-native, not Git-replacement. Entire extends Git rather than replacing it. It treats agent context as first-class Git metadata, a different approach from fully decentralized alternatives like Radicle or GitButler's reimagined Git client .
• Parallel moves by other GitHub founders. Scott Chacon (co-founder of GitHub) raised $17M from a16z for GitButler, a reimagined Git client for the AI era. Both Dohmke (Entire) and Chacon (GitButler) are betting that the existing Git workflow is insufficient for agent-generated code, but they take different architectural approaches — Entire focuses on context capture and provenance, while GitButler focuses on a new Git client UX .
• Human-agent collaboration as a first principle. Entire's design philosophy is that humans and AI agents will co-author code, and version control must capture the human's intent and the agent's reasoning together — not just the file diffs . This is a broader industry shift away from human-only Git workflows toward "agent-native" DevOps infrastructure.