AI Agents Don't Have a Timeout Problem. They Have a Waiting Problem.
The next challenge for autonomous AI systems is not making them respond faster. It is teaching them how to wait without losing state, holding resources, duplicating work, or breaking the workflow.
Most AI agent architectures are designed around a request. A user asks something, the agent thinks, it calls a tool, the tool responds, the agent thinks again, and eventually something comes back to the user. That model works surprisingly well when everything involved in the workflow is fast. A database query takes milliseconds. An API responds in a few hundred milliseconds. The model generates an answer in a few seconds. The whole operation fits comfortably inside the lifetime of an HTTP request.
Then an agent enters the real world.
Suddenly, one of its tools takes thirty seconds. Another service takes two minutes. A payment provider accepts a request but does not immediately confirm the transaction. A document has to...
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