Audit a feature's issues and hand them to an engineer's agent
I recently had an experience where I had an AI agent list all of the issues with a feature and sent it to an engineer, then I got an automated response that their agent had gone through the list and addressed most of the issues. Task: I recently had an experience where I had an AI agent list all of the issues with a feature and sent it to an engineer, then I got an automated response that their agent had gone through the list and addressed most of the issues. Whether or not you call this vibe coding, this is software dev today.; tools: custom-built or unnamed agent. Human role: The original post does not say how a person stayed involved.; limitations: Source reliability is low as the original post is blocked or too short to verify details. No specific tools or costs are mentioned. Human approval is critical for code integrity, as automated agents may introduce subtle bugs or security vulnerabilities..
Tools
custom-built or unnamed agent
Published on Agent Work Atlas:
Overview
I recently had an experience where I had an AI agent list all of the issues with a feature and sent it to an engineer, then I got an automated response that their agent had gone through the list and addressed most of the issues.
Workflow
I recently had an experience where I had an AI agent list all of the issues with a feature and sent it to an engineer, then I got an automated response that their agent had gone through the list and addressed most of the issues. Whether or not you call this vibe coding, this is software dev today.
Reported result
The source does not provide measured outcomes or success rates. It only describes an anecdotal experience where an agent addressed 'most' issues. A measurable acceptance check would be the percentage of issues resolved without human intervention in a controlled test.
Human role
The original post does not say how a person stayed involved.
Limitations
Source reliability is low as the original post is blocked or too short to verify details. No specific tools or costs are mentioned. Human approval is critical for code integrity, as automated agents may introduce subtle bugs or security vulnerabilities.
Try it
I want to test an AI-to-AI workflow for software maintenance. First, analyze the code in [replaceable: path/to/feature] and list all potential bugs or style issues. Save this as 'issues.json'. Second, send 'issues.json' to a separate AI agent configured to write code fixes. Third, generate a diff file showing the proposed changes. Do not apply changes directly. Instead, present the diff and the issue list to me for review. I will approve or reject each change. If the source code is missing, ask me for the file path. Ensure the output format is a unified diff and a markdown summary of resolved vs. unresolved issues.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.