Designing the Runtime Layer for Stateful AI Agents: Where Agent Substrate Fits
Introduction: The Layer Nobody Designs AI agents are evolving from simple prompt-response systems into stateful, long-running applications that can reason, execute…
All she wanted was help from ChatGPT with her proposal, but soil hydrology researcher Ying Zhao got a lot more than she bargained for.
Asking for more maps and metadata about her project—landscape restoration on the Loess Plateau in China—Zhao kept messaging “please continue” to ChatGPT for additional details. Only later did she realize that ChatGPT had taken her cue and run with it, emailing fellow researchers about her project and (politely) requesting assistance.
“The responses that I received to the emails were helpful,” wrote Zhao, recounting her experience on Nature.com. “The information that I was given in these emails allowed me to advance the project. But the useful outcomes of ChatGPT’s behavior do not negate the fact that the actions it took were unauthorized.”
That’s the thing about AI agents: The very qualities that make them so powerful, including initiative and persistence, also make them unpredictable and liable to doing a lot more than you anticipated.
I’ve written before about how AI agents like ChatGPT Work, Claude Cowork, Gemini Spark, and others don’t just need prompts but outcomes. More importantly, they also require boundaries: specifics on what not to…
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