According to a new analysis from The Next Platform, the gap between current agentic AI capabilities and what is needed is not a model problem. The paper argues that the bottleneck lies in data. This reframes the conversation around where research and engineering efforts should be directed.
The analysis contends that while models have received much attention, the underlying data used to train and ground agentic systems is the limiting factor. Without better data, advances in model architecture or scale may not translate into more capable agents.
Because this is a single source, there are no differing viewpoints to compare. The paper's central claim is straightforward: to make progress on agentic AI, the field needs to treat data as a first-class problem.