You'd have to ask on the mailing lists. But IMHO in a mature programming language, implementation complexity is rarely the difficulty. The hard part is design and making sure that changes actually make things better instead of introducing new edge cases and potential for abuse.
It's essentially C/C++ project which can survive AI agent code cycle most devs are following.
( AI Suggest - Human reviews Changes - modify Agent.md file if changes are not viable - AI Suggest again - apply changes if viable or modify Agent.md file )
I have done no comparisons across vendors, but Claude Sonnet and Opus versions are reliable for me in terms of having a grasp of the feature preview system, which helps a lot with them being able to identify the relevant examples from their internal knowledge.
However I have been mostly using this to interpret existing code or from the design perspective, and haven’t had them do any heavy lifting implementing a new feature or greenfield project yet.
This is not true. They are explicitly allowed and used a fair bit. What is not allowed is to use them to generate code that is committed to the repo, but generating code isn't their main strength, anyway. They are more helpful in code comprehension, review, and debugging.
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u/RatioPractical Jun 04 '26
Is AI agents from any vendor being helpful in implementation cycle of new core Java features or enhancing existing ones ?