In the rapidly evolving world of artificial intelligence, agentic AI promises to automate complex tasks by enabling AI agents to interact with the real world. However, a critical bottleneck is emerging, one that threatens to hinder its widespread adoption and scalability. Giedrius Šteimantas from Oxylabs discusses this challenge, identifying the ‘missing layer’ in agentic AI as the efficient handling of product data.
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The core problem, as outlined by Šteimantas, lies in the sheer volume of data that current agentic AI systems must process. When an agent is tasked with analyzing product pages, for instance, it often needs to sift through numerous pages to find relevant information. Šteimantas provides a stark example: pointing an agent at ten product pages might yield useful content from only three. Yet, the system still sends all ten pages to the model for processing.