The looping problem usually comes down to poor prompt structuring and not setting clear boundaries for what the agent should do when it hits a dead end. A solid approach to agent optimization starts with trimming down the instruction set and layering in proper error handling so the thing knows when to stop instead of spiraling, you can dig into the mechanics here. Keeping the context window lean also makes a legit difference, feeding the agent too much irrelevant history causes hallucinations and drift, so short test loops with a fixed dataset catch weird behavior before it wrecks a full run.