AI Dictionary › AI Fundamentals
A timeout is the maximum time a system waits for a response before giving up and declaring the operation failed. It is a fundamental defense: without timeouts, a request to a stuck service would hang forever, holding resources and spreading paralysis in a chain. A clean error after five seconds beats an infinite wait.
Technically, the caller sets a limit: if the response arrives within it, everything proceeds; otherwise an exception fires and the caller decides how to react, retry, degrade, report. Timeouts stack in layers: connection timeout (how long to establish the link), read timeout (how long to wait for data) and an overall deadline. Choosing values is an art: too short causes false failures, too long defeats the protection.
In AI, timeouts demand special care: LLM generations can legitimately take tens of seconds, and agents orchestrating multiple calls must balance patience and responsiveness, often combining timeouts with streaming to show progress. Beyond AI, every network protocol, from TCP up, lives on carefully tuned timeouts.
The term comes from sports: a time-out is the pause in play called to regroup, an expression documented in American sporting English since the nineteenth century. Network engineering adopted it naturally: the earliest transmission protocols of the 1970s, including Cerf and Kahn's TCP, already used retransmission timers to decide when a packet should be considered lost.
Grace's daily gym gets you used to working within defined time limits, just as a well-designed system works with clear timeouts. In the scenarios on agents you learn to specify wait limits and fallback behaviors for when a step gets stuck.
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