New Tool Solves AI Agent PR Merge Dilemma

Artificial intelligence agents are becoming increasingly proficient at writing code, but a fundamental challenge remains: knowing when the work is truly done. Developers using AI coding assistants like Claude Code often encounter a frustrating loop where agents push changes, respond to reviews, and wait for continuous integration, but never receive a clear signal that a pull request is ready for final merge.

This ambiguity creates inefficiency. Agents might poll CI systems in endless loops, miss critical feedback buried among dozens of automated suggestions, or incorrectly declare victory while unresolved discussion threads remain open. The core problem is a lack of a deterministic, automated way for an agent to know a PR is ready to merge.

To address this specific workflow gap, a developer has built a new tool called gtg (Good To Go). The tool is designed to provide a single, unambiguous answer to the question: "Is this PR ready?" It operates via a simple command-line interface, returning a clear status message.

For example, running gtg 123 might return:

OK PR #123: READY CI: success (5/5 passed) Threads: 3/3 resolved

Behind this simple output, gtg performs several complex tasks. It aggregates the status of continuous integration pipelines, intelligently classifies review comments to separate actionable feedback from noise, and actively tracks the resolution status of discussion threads. The tool can output its findings in both human-readable text and JSON formats, making it suitable for integration into automated agent workflows.

The most sophisticated aspect of gtg is its ability to parse and understand review comments from various automated tools. It is specifically tuned to recognize the patterns and severity markers used by popular code review assistants like CodeRabbit and Greptile, as well as the blocking and approval language used by AI agents like Claude.

This allows the tool to make nuanced decisions about what requires attention. For instance:

• A comment flagged as "Critical: SQL injection" would be immediately identified as a blocking issue.
• A comment noting "Nice refactor!" would be recognized as positive feedback that doesn't block the merge.
• It filters out low-priority suggestions that might otherwise clutter the review process.

This classification system is crucial for preventing agents from either ignoring critical security warnings or getting stuck on trivial stylistic suggestions.

The tool is built as a pure Python application, making it lightweight and easy to integrate into existing development environments. It is distributed under the permissive MIT license, encouraging adoption and modification by other developers.

The creator has implemented gtg within a larger agent orchestration system, using it daily to manage automated coding workflows. This real-world application demonstrates its practical utility in complex, multi-agent development environments where clear merge criteria are essential for maintaining velocity and code quality.

The tool was shared with the developer community to gather feedback and foster collaboration. The creator expressed interest in hearing from others who are building similar agent orchestration workflows, suggesting a desire to refine the tool based on real-world use cases.

The discussion around the tool highlights a growing need in the software development landscape: as AI agents take on more coding tasks, the infrastructure supporting their workflows must evolve. Tools like gtg represent a new layer of middleware designed specifically for human-AI collaboration in software engineering.

The development of gtg signals a maturation in AI-assisted coding tools. While early focus was on generating code, the industry is now addressing the operational challenges of integrating AI agents into existing software development lifecycles.

As more teams adopt AI coding assistants, the need for deterministic merge criteria will only grow. Tools that can reliably interpret complex CI results, review feedback, and discussion threads will become essential infrastructure for maintaining both development speed and code quality in AI-augmented teams.