Lately, there's been a lot of excitement around AI agents. These digital helpers seem poised to take on everything from booking appointments to handling complex business workflows. The promise is clear: AI agents will save us time, make decisions for us, and reduce the burden of mundane tasks. But as thrilling as this sounds, something deeper is unfolding in the AI space—Subject Matter Experts (SMEs) are playing a more crucial role than ever before.
It's becoming clear that these agents need a guiding hand. At times LLMs may seem like an all knowing oracle but they often can't reason and act out on their own. While LLMs may yield convincing and confident responses they can lack the context and domain-specific knowledge to judge truthfulness.
Behind a AI agent is a complex network of decision-making processes. Actions are encoded and teased out from the natural language forming the next step in that decision tree. Some steps are code to retrieve or send data while others are analysis generating more data from an AI model against data. SMEs are essential in shaping these processes to ensure the AI doesn't just route information blindly.
Especially when evaluating outcomes of steps within the egent, using an LLM as a judge needs clarity and refinement from a SME.
Behind the Agent's logic, there's a SME who ensures that the agent is making decisions rooted in reality. Without an experts input and polish, an LLM is just an oracle—offering answers without truly understanding the problem.
Ultimately, AI is at its best when it works in harmony with human expertise. AI agents aren't replacing people—they're enhancing what we already do. SMEs are becoming the crucial bridge between AI agents and the real world, ensuring these systems deliver results that are both meaningful and effective.
As AI agents evolve their success won't just be about the tech behind them. It'll be about the expertise guiding them. LLMs are smart, but without SMEs they may not be smart enough.
