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 basic drawback. My preliminary monolithic agent was attempting to do every little thing without delay: perceive complicated buyer requests, analysis restaurant availability, deal with real-time telephone conversations and adapt to surprising responses from human employees. The end result was an AI that carried out poorly at every little thing.
After days of experimentation with my voice AI prototype — which handles reserving dinner reservations — I found that probably the most strong and scalable strategy employs two specialised brokers working in live performance: a context agent and an execution agent. This architectural sample essentially adjustments how we take into consideration AI process automation by separating considerations and optimizing every part 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 every little thing. When a person wished to e-book a restaurant reservation, this monolithic agent needed to concurrently analyze the request (“e-book 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 employees.
