Why AI agents mishandle empty tool results — and how to fix it

Developers building AI agents frequently encounter a subtle failure mode where tool calls return empty results without explaining why, leaving agents unable to respond correctly. A single empty result set can stem from at least three distinct causes — a bad query, a stale data index, or a backend timeout — each requiring a different corrective action. Engineers have proposed that tools should return structured failure states with labels, supporting evidence, and a suggested retry interval, rather than treating all empty responses as equivalent. However, defining meaningful thresholds — such as what counts as a 'stale' index — introduces subjective calibration decisions that may not suit every use case. The broader issue is framed as an authority boundary problem: the assumptions baked into a tool's failure logic are set by its author, who may not anticipate the contexts in which the tool will eventually be deployed.