Docket tool creates 'paper trail' for AI code to speed up human review
The open-source project attaches verification evidence to specific code hunks, highlighting unverified changes for human developers.
Developer Dillonsmart has released Docket, an open-source tool designed to bridge the verification gap in AI-generated software. The tool captures the implementation journey of coding agents and attaches evidence of verification directly to specific code hunks within a commit.
Docket functions by folding an agent's execution history—including what was attempted and how it was verified—against the final diff. This process creates a per-hunk evidence record, allowing human reviewers to see exactly which parts of a change were tested and which were not. According to the project's GitHub repository, the tool is specifically designed to help reviewers focus their attention on code changes that lack supporting evidence of verification.
The Verification Gap
As autonomous AI coding agents become more prevalent, they are capable of generating large diffs at a speed that exceeds human review capacity. Traditionally, these agents operate in a trial-and-error loop, running tests and iterating on code, but this context is discarded at the moment of commit. The human reviewer is left with only the final result, losing the critical history of how the agent arrived at that solution and whether specific lines were actually validated.
Reducing Cognitive Load
This lack of transparency creates a significant bottleneck in the AI software development lifecycle regarding trust and safety. By providing a durable paper trail for every line of agent-written code, Docket reduces the cognitive load on human developers. Instead of auditing an entire large-scale diff with equal scrutiny, reviewers can prioritize the "blind spots" where the agent provided no evidence of successful verification.
The Path to Autonomous Coding
As the industry moves toward more autonomous agentic workflows, the ability to audit the process—not just the output—becomes essential for deploying AI-generated changes safely. While Docket provides a mechanism for recording this evidence, the broader challenge remains in how teams integrate these evidence records into existing CI/CD pipelines and code review interfaces to fully automate the trust layer of software engineering. This shift from auditing results to auditing processes represents a critical step in scaling AI contributions without compromising system stability.