Why Your AI Agent Stops Working When Decisions Get Complex

Description and use

<h2> Intro </h2> <p>You're building an AI agent to handle a real workflow—something that looks tactical on the surface but demands holding multiple constraints across ten steps ahead. You expect it to work well. You get 60%, and it doesn't improve. That's not a tuning problem. That's a ceiling.</p> <p>I started thinking through what happens when an agent has to commit to a path before it sees the consequences. Most people assume agent failures are training failures—bad data, wrong reward signal, not enough examples. The real problem is stranger: even the best models are guessing better than ch

Problem

<h2> Intro </h2> <p>You're building an AI agent to handle a real workflow—something that looks tactical on the surface but demands holding multiple constraints across ten steps ahead. You expect it to work well. You get 60%, and it doesn't improve. That's not a tuning problem. That's a ceiling.</p> <p>I started thinking through what happens when an agent has to commit to a path before it sees the consequences. Most people assume agent failures are training failures—bad data, wrong reward signal, not enough examples. The real problem is stranger: even the best models are guessing better than ch

Outcome

<h2> Intro </h2> <p>You're building an AI agent to handle a real workflow—something that looks tactical on the surface but demands holding multiple constraints across ten steps ahead. You expect it to work well. You get 60%, and it doesn't improve. That's not a tuning problem. That's a ceiling.</p> <p>I started thinking through what happens when an agent has to commit to a path before it sees the consequences. Most people assume agent failures are training failures—bad data, wrong reward signal, not enough examples. The real problem is stranger: even the best models are guessing better than ch

Evidence

Why Your AI Agent Stops Working When Decisions Get Complex

Source