Once I first began experimenting with voice AI brokers for real-world duties like restaurant reservations and customer support calls, I shortly ran right into a elementary drawback. My preliminary monolithic agent was making an attempt to do all the pieces without delay: perceive complicated buyer requests, analysis restaurant availability, deal with real-time telephone conversations and adapt to sudden responses from human workers. The end result was an AI that carried out poorly at all the pieces.
After days of experimentation with my voice AI prototype — which handles reserving dinner reservations — I found that essentially the most strong and scalable method employs two specialised brokers working in live performance: a context agent and an execution agent. This architectural sample basically adjustments how we take into consideration AI job automation by separating considerations and optimizing every element for its particular position.
The issue with monolithic AI brokers
My early makes an attempt at constructing voice AI used a single agent that attempted to deal with all the pieces. When a person needed to guide a restaurant reservation, this monolithic agent needed to concurrently analyze the request (“guide a desk for 4 at a restaurant with vegan choices”), formulate a dialog technique after which execute a real-time telephone name with dynamic human workers.
