Use Git for Automated Checkpointing

Commit frequently to create a detailed history of your agent’s work.

Many AI development tools are introducing proprietary checkpoint features, but Git already provides a robust, universal solution for tracking progress. By instructing your AI agent to make small, frequent commits, you create an automated checkpointing system that captures every step of its work.

This approach offers several advantages:

• Detailed history — Every change is recorded with a timestamp and description, making it easy to understand what the agent did and when.

• Easy rollback — If the agent makes a mistake or takes a wrong turn, you can quickly revert to any previous state.

• Reduced cognitive load — The agent handles the mental overhead of saving progress, freeing you to focus on higher-level decisions.

• Standard tooling — Git is ubiquitous, well-understood, and integrates seamlessly with existing development workflows.

Implementation

Add a directive to your agent’s instructions that encourages frequent, descriptive commits:

# Development Process
- Commit frequently with concise, descriptive messages that summarize the changes made.
- Each commit should represent a single logical unit of work.
- Use imperative mood in commit messages (e.g., “Add validation logic” not “Added validation logic”).

This transforms Git from a passive version control system into an active checkpointing mechanism that documents your agent’s decision-making process. You gain not just the final result, but a complete audit trail of how it got there.