after a year of shipping with AI agents, here's what they still reliably get wrong

Reddit r/AI_Agents News

Summary

A developer shares consistent failure modes of AI agents after a year of shipping code with them, including confidently wrong code, inability to maintain cross-file architecture, lacking pushback on bad decisions, and security edge case issues.

i've spent about a year building a real product with AI agents writing most of the code, and the hype keeps skipping the failure modes. the stuff they still get wrong for me, pretty consistently: - confidently wrong code. it runs, looks right, passes a quick read, and is subtly broken in a way you only catch if you know the system. this is the dangerous one. - anything that has to hold across files. great in one file, loses the thread on architecture and consistency. - knowing why. they'll do what you asked even when what you asked is the wrong move, and never push back. - security and edge cases. happy path is trivial, the nasty inputs and auth corners are where i still slow all the way down. - debugging their own subtle bugs. they'll cheerfully "fix" it five times and make it worse. - knowing when to stop. an agent keeps going long after the right answer was "this whole approach is wrong, back up." none of this makes them not worth it, the leverage is real. but the job became catching all of the above, not typing. curious what others have hit that isn't on this list.
Original Article

Similar Articles

Building AI agents gets weird once real users show up

Reddit r/AI_Agents

An experienced developer reflects on the gap between AI agent demos and real-world performance, highlighting issues like poor documentation, naive permission expectations, and the misconception that probabilistic software becomes deterministic in production.