AI agents can experience "context rot" when excessive information in their context windows overwhelms their ability to prioritize, causing confusion and misfocused attention. Overloaded agents may lose track of task objectives over time, degrading results. A recommended approach divides large tasks among fleets of specialized sub-agents, each with tailored goals and relevant context for its part of the workflow. Providing models with the most accurate and precise data and very clear instructions improves outcomes. The sub-agent architecture is presented as the preferred design for large-scale agent systems over a single, all-purpose agent.
Aaron Levie told Business Insider there's a catch to AI agents: If you feed them too much information, they start to lose the plot. The CEO of the cloud-storage giant Box calls this problem "context rot." The more data you give the AI model, "it doesn't necessarily lead to a better outcome," Levie said on Tuesday. "The model will just get very confused and potentially focus on the wrong part of the information," Levie told Business Insider. As the task drags on, the model can lose track of what it's supposed to focus on, leading to worse results, he added.
Instead of trusting one super-agent to handle everything, Levie said the smarter approach is to carve up the work and assign it to fleets of specialized sub-agents. "You're going to want to break apart the agents and the context that they have," he said. "You'll have multiple agents, all with a set of goals and a set of contexts that are germane to their particular part of the workflow," he added. It's counter-trend to the Silicon Valley dream of a single AGI overlord. Levie, who cofounded Box in 2005, said the sub-agent model is "definitely going to be the future of large-scale agent systems."
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