Soon, you'll encounter the term "agent" in every AI project discussion. If you find this term puzzling, you're not alone. Many of the definitions are overly abstract, and the term means different things in various contexts. Here's a practical definition for our purposes:

‍A large-language model (LLM) agent is a software entity that's capable of reasoning and autonomously executing tasks.

In Episode 4 of our AI strategy series, I demonstrate LLM Agents by showcasing another example of the Unified Natural Language Query (Unified NLQ) use case. I detail how GPT-4 can query multiple tables and reason through data to furnish a user with the right answer. We even tried to trick the agent by feeding it messy data, and yet, it managed to reason through the scenario to pinpoint the correct answer.

Witnessing this has been among the most astonishing technological breakthroughs in my experience. Every report, dashboard, application screen—essentially every interface where you serve data—can transform into an interactive conversation. The widespread demand for this ability will fuel AI adoption within your company, making Unified NLQ the "killer app" for enterprise AI.

P.S. Have thoughts on deploying Unified NLQ at your company? If so, reach out at hello@prolego.com and share your ideas with us - we'd love to help out in any way we can.

‍