New study reveals optimal windows for clarifying instructions in long-horizon agents, with goal info losing value after 10% of execution.When Should AI Agents Ask for Clarification? Timing Matters
A forced-injection framework across 6,000+ runs shows that the value of clarification depends sharply on information type and timing. Goal clarification loses nearly all value after 10% of execution, while input clarification retains value through 50%. Current frontier models fail to ask within optimal windows.
