AI models to AI agents: What it takes to scale intelligent systems
By Ramakant Yadav, Co-Founder, Scalar Field
Enterprise AI has quietly changed shape over the past eighteen months. The question is no longer whether a model can produce a useful answer. It is whether a system built around that model can be trusted to act on one. That shift, from generating output to taking action, is where most of the engineering difficulty now sits.
Capital markets illustrate the gap more sharply than most sectors. Trading terminals have historically been passive dashboards, dense with data but entirely dependent on a human to form the hypothesis, pull the inputs, test the idea and place the order. The software displays. The analyst works. Agentic systems invert that relationship, and inverting it turns out to be far harder than improving the model underneath.
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