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Posted to issues@artemis.apache.org by "ASF subversion and git services (Jira)" <ji...@apache.org> on 2026/06/03 13:08:00 UTC

[jira] [Commented] (ARTEMIS-6092) Add AGENTS.md guidelines for AI assistants

    [ https://issues.apache.org/jira/browse/ARTEMIS-6092?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=18085784#comment-18085784 ] 

ASF subversion and git services commented on ARTEMIS-6092:
----------------------------------------------------------

Commit 7cef8fecfe1c1e7fb154936bcefcf6e012c54711 in artemis's branch refs/heads/main from Justin Bertram
[ https://gitbox.apache.org/repos/asf?p=artemis.git;h=7cef8fecfe ]

ARTEMIS-6092 add AGENTS.md guidelines for AI assistants


> Add AGENTS.md guidelines for AI assistants
> ------------------------------------------
>
>                 Key: ARTEMIS-6092
>                 URL: https://issues.apache.org/jira/browse/ARTEMIS-6092
>             Project: Artemis
>          Issue Type: Task
>            Reporter: Justin Bertram
>            Assignee: Justin Bertram
>            Priority: Major
>              Labels: pull-request-available
>          Time Spent: 40m
>  Remaining Estimate: 0h
>
> Create an {{AGENTS.md}} file to provide structured guidance for AI coding assistants (e.g., Claude Code, GitHub Copilot, Cursor) working with the Artemis codebase.
> h3. What is AGENTS.md?
>   
> {{AGENTS.md}} is an emerging standard format (https://agents.md). It serves as a condensed knowledge base that helps AI agents understand project-specific conventions, build processes, security requirements,  and contribution workflows.
> h3. Why add this to Artemis?
> # Consistency: Having explicit guidelines ensures AI-generated contributions follow Artemis conventions
> # Quality Control: Establishes clear rules like:
> ** Maximum PR volume per operator
> ** Required human attribution for AI-generated content
> ** Security review checklists
> ** Testing best practices
> # Efficiency: Points AI agents to authoritative documentation rather than having agents hallucinate or make assumptions based on outdated training data



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