Why Most AI Agent Projects Should Just Be Simple Code, Experts Warn

A widely circulated engineering perspective argues that the majority of AI agent projects are unnecessary, as most tasks can be handled by straightforward code with a few language model calls at fixed points. The core distinction drawn is that true agents are only warranted when the next step cannot be determined until the previous one completes — a condition far rarer than the industry assumes. Reliability concerns compound the problem: a 20-step agent where each step succeeds 95% of the time completes correctly only 36% of the time overall. Benchmarks support this skepticism — Carnegie Mellon's TheAgentCompany found top models completing just 24% of real professional tasks autonomously as of late 2024, with the leaderboard only reaching the low thirties since. Even Anthropic, a leading AI model vendor, published guidance in December 2024 recommending developers seek the simplest solution and avoid adding agent complexity unless genuinely necessary.