Beyond Git: How Entire is Reinventing Version Control for the AI Era

Software development is undergoing a quiet but profound transformation. Code is no longer written line by line in isolation. Instead, developers increasingly operate at a higher level of abstraction - defining intent, constraints, and architecture - while AI agents generate, refactor, and validate the implementation.

Yet, despite this shift, one of our most foundational tools has remained largely unchanged.

Version control systems-most notably Git-were designed for a world of human-paced iteration. In an era of autonomous agents and high-velocity code generation, that mismatch is becoming impossible to ignore.

This is where Entire enters the picture.

1. The Refactoring of the Developer Role

In the architectural evolution of software engineering, we are witnessing a profound refactoring of the developer role. We have transitioned from the manual, line-by-line artisanry of coding into an era of "spec-driven development." Today, the terminal has become the high-density centre of gravity, where senior engineers prompt fleets of agents across multiple windows simultaneously.

However, as a former insider from the GitHub era, I recognise a glaring systemic misalignment: while our workflows have moved to the speed of thought, our Version Control Systems (VCS) have remained architecturally decoupled from the reasoning loop. We are attempting to manage high-velocity, machine-generated code using a toolset designed for human-speed iteration. Git, in its current form, is a record of results, not a record of intent. Entire is the first platform to acknowledge that for the agentic era to succeed, the VCS itself must be modernised to serve as a semantic foundation for both humans and machines.

2. The $60 Million Thesis: A Pedigree of Experience

The industry has taken note of this bottleneck. Entire recently emerged with a $60 million seed round a figure almost unheard of for a developer tool startup to build the world’s next developer platform. Their mission is not to have agents mimic human workflows, but to create a space where agents and humans collaborate, learn, and ship together as first-class citizens.

Industry Context: From GitHub to Entire Entire was founded by the former leadership team of GitHub, including its former CEO. This transition represents a shift in philosophy: moving away from traditional, human-centric repositories towards a "git-compatible database" designed specifically for the high-volume reasoning logs of the AI era.

3. The Problem: Why Git is the Bottleneck for AI Agents

Git was designed to preserve the "what" the code diffs and file states but it is notoriously poor at preserving the "why." In an agentic workflow, this becomes a critical failure point. Agent sessions are ephemeral; the prompts, constraints, and reasoning that lead to a 500-line diff disappear the moment the terminal session ends.

Without a shared context, agents are forced into an expensive cycle of retracing steps. They must re-analyse raw diffs to guess at previous intent, which is not only slow but extremely wasteful for tokens.

4. The Solution: Introducing Entire’s 'Checkpoints'

To solve the context-loss problem, Entire introduces a new versioning primitive: the Checkpoint. A checkpoint is designed to capture agent context automatically, serving as what the founders call the "foundational right path" of a semantic reasoning layer.

By capturing metadata alongside the standard Git commit, Entire ensures that the agent's logic is never lost. A single checkpoint captures:

• Full session transcripts and conversation logs (stored as JSON lines).
• The exact prompts that triggered code changes.
• Granular file touches and specific tool calls made by the agent.
• Token usage statistics for cost and efficiency auditing.
• A structured summary of the intent behind the architectural decisions.

5. Technical Deep Dive: The Mechanics of the Entire CLI

For systems engineers, the beauty of Entire lies in its implementation as a "gateway CLI" that wraps standard Git operations. It does not replace your repository; it augments it.

The Implementation Flow:

1. Initialisation: Upon running entire enable, the CLI installs a series of hooks and creates a local .entire directory. It also modifies the environment's settings.json to ensure agent awareness.
2. Metadata Branching: To keep the main codebase clean, Entire creates an orphan branch - specifically entire-checkpoints-v1 to store session metadata. This architectural choice prevents bulky reasoning logs from polluting the primary commit history.
3. The Hook System: The CLI monitors lifecycle events through JSON lines files. On every commit generated by an agent, Entire writes a structured checkpoint object and associates it with the specific Git commit SHA.
4. Append-Only Audit Log: When a developer pushes their code, the entire wrapper simultaneously pushes the metadata to the orphan branch. This creates a permanent, searchable audit log of every decision made during the development session.

6. Synergy with Agents: The Claude Code Integration

The power of Entire is most evident in its integration with agents like Claude Code. The Entire CLI acts as the connective tissue, eavesdropping on the agent's logic through specific hooks: on session start, tool use, post tool use, and prompt submit.

When you launch an agent in an Entire-enabled repo, the terminal confirms this link with a "Powered by Entire" notification. This indicates that the conversation is now "checkpoint-aware." If an agent is asked to "check unstaged files and find a bug," Entire doesn't just record the fix; it records the agent’s internal summary of the regression and the logic used to identify it. This metadata is then available for the next agent (or human) to query, ensuring the "thread of decision-making" remains unbroken.

7. The Business and Engineering Case: Traceability and Efficiency

Engineering leaders must view Entire not just as a tool, but as an optimisation of the modern software development life cycle (SDLC).

Traceability and Accountability

Every change can be traced back to the specific reasoning and mood of the session that produced it. This eliminates the "black box" of AI-generated code, providing a clear audit trail for compliance and debugging.

Accelerated Reviews and Handoffs

Reviewers no longer need to spend hours reverse-engineering an agent's intent from a raw diff. By reviewing the associated checkpoint, they can see the explicit reasoning and tool calls, making the approval process significantly faster and more accurate.

Token Efficiency and Cost Reduction

The most significant hidden cost in agentic workflows is "token waste." Agents often consume thousands of tokens re-analysing raw diffs to understand a repository's history. Entire allows agents to perform semantic searches over previous reasoning logs, drastically reducing the need for expensive re-analysis of the codebase and lowering the overall cost of development.

Conclusion: The Universal Semantic Reasoning Layer

Entire represents the necessary evolution of version control. It is not a replacement for Git, but a sophisticated expansion a Git-compatible database that unifies code with intent. By building this "context graph," Entire creates a universal semantic reasoning layer that allows for true multi-agent coordination.

We are moving toward an era where the reasoning behind the code is as valuable as the code itself. Entire provides the infrastructure to unlock this "10x experience," ensuring that as we scale our use of AI agents, we do so with the traceability and efficiency required for professional-grade engineering. It is the foundational layer for an accountable, agent-native future.