Why do agents fail on long-horizon tasks, and how do you keep reliability up over many steps?

Per-step accuracy looks fine, yet a 50-step task fails. The signal is understanding compounding error and the techniques (decomposition, verification, checkpointing) that keep long-horizon agents from collapsing.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

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LEARN THE BACKGROUND

These lessons teach the material this question tests, in order and from the beginning.

Agent Engineering·The single-agent loopFree13mPlanning ahead versus deciding as you goCommitting to a plan first makes a run cheaper, more predictable and easier to check, and it is wrong whenever the plan cannot survive what the first step returns. This lesson is how to tell which case you are in, and the hybrid that usually wins.Agent Engineering·The single-agent loopFree14mStopping is a design decision, not a budgetBudgets stop a runaway agent. They do not tell it when it is finished, and the difference between those two things is where most agent quality is lost. This lesson covers the four ways to define done and what to return when you never got there.

UP NEXT ON YOUR JOURNEY

Next in this trackWhat are Reflexion and self-critique loops, and when do they actually improve an agent?Next in this trackHow do you design human-in-the-loop checkpoints so an agent can pause, ask, and resume?Next in this trackAn agent reads untrusted web content and tool output. How do you defend against prompt injection?

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