A developer documented how AI coding agents get stuck making pointless decisions—revealing a real problem with how these tools work today

Ben Hoyt published a detailed walkthrough showing how an AI coding agent (a program that writes code by itself) repeatedly chose inefficient solutions, rewrote its own work without improving it, and wasted token limits (the computational budget that controls how much thinking an AI can do) on indecision rather than solving the actual problem.

The issue: the AI lacked a decisive evaluation mechanism—it couldn't reliably judge when a solution was 'good enough' to stop, so it kept second-guessing itself. Unlike humans who can say 'this works, ship it,' the agent treated all paths as equally valid and explored them anyway, burning through resources.

This matters to anyone using or building on AI coding tools (GitHub Copilot, Claude for coding, internal company agents): even when an AI produces working code, it may waste time and money cycling through bad options behind the scenes. Teams relying on these agents for production code need to know that 'AI wrote this faster' doesn't mean 'the AI thought efficiently'—just that it produced an answer.

The findings come from a technical post on Ben Hoyt's blog (benhoyt.com) and sparked discussion on Hacker News, signaling that developers are actively stress-testing these tools to understand their real-world limits rather than trusting vendor claims.