99% reliability sounds great but you should really do the math. If each action succeeds 99% of the time, a 10-step task has a 90% chance of finishing without a single failure. At 100 steps, that drops to 37%. Now give each failed action one retry. Assuming independent failures, that 37% jumps to ~99%. Thats a huge difference. But retries aren’t always that simple. What if an action succeeded but the response timed out? Retrying could execute it twice. What if the failure left the system in a different state? They are probably also not independent. You need to know what actually happened before deciding what to do next. This is why long-horizon agents aren’t just an LLM problem. Better models are definitely needed, but the systems around them matter just as much!