Agents fail in their own ways

Agents add failure modes a plain prompt never has, because they take many steps and small errors compound. A 95%-reliable step sounds great until you chain ten of them and end up reliable barely half the time. Knowing the recurring failures — and their fixes — is most of what makes an agent trustworthy.

The usual suspects

• Looping or getting stuck — the agent repeats an action or can't decide it's done. Fix: a hard step limit, plus a clearer stopping condition and final-answer instruction.
• Wrong or unused tool — it ignores a tool it needs, or misuses one. Fix: sharpen tool descriptions and input schemas; the description is the model's only guide.
• Error spirals — a tool returns an error and the agent flails. Fix: return clear, readable error messages and tell the agent how to recover or back off.
• Context overflow — long runs blow past the window and the agent "forgets" the goal. Fix: summarize history and restate the objective periodically.
• Goal drift — many steps in, it's solving a subtly different problem. Fix: re-anchor to the original goal each iteration.
• Cost or latency blowups — too many calls. Fix: budget caps, fewer steps, and a smaller model for simple sub-decisions.

The compounding problem

Because errors multiply across steps, the highest-leverage move is usually to reduce the number of steps and make each one more reliable, rather than adding cleverness. Shorter agents fail less.

Don't ask "why is my agent dumb?" Ask "which step failed, and does it loop, misuse a tool, drift, or overflow?" Almost every agent bug is one of a handful of patterns with a known fix.

Try this: Next time an agent misbehaves, read its step log and label the failure with one of the types above. You'll find the same two or three recur for your tasks — and fixing those gives you the biggest reliability gains.