The Power Combo of AI Agents and the Modular Data Stack: AI that Reasons

Today, more than 60% of CIOs are integrating AI into their innovation strategies—making AI a good-to-use stack and a compulsory strategic confidant! The last few years have seen such sudden surges in these advances that in no time, we see a new development making its way into the orgs’ workflows or, at the very least, becoming the focus of countless boardroom pitches.While we are all discussing LLMs, there’s now a simultaneous thrust of agentic AI across industries. Because while the first wave of LLMs cast a magic spell on ambitious enterprise leaders, it also broke the spell on implementation when the AI layer started demanding more than the usual effort to set up the right prompts, validate the outcomes (which were mostly subpar or too generic for any strategic use), and then figure out why and how the outcomes went wrong, and then find out the right results.The next wave naturally resulted in a wider adoption of AI agents, which were more “reasonable” and, therefore, closer to the human intellect and empathy, which were desirable for logical operations as well as for strategic aid. By the end of this article, you’ll have a working knowledge of what AI agents are, why they give additional leverage relative to generic LLMs when measuring ROI on AI initiatives, and how to ensure the AI Agents' success.