A one-size-fits-all AI voice fails. For a Japanese healthcare client, ElevenLabs' agent used quick, short responses for younger callers but a calmer, slower style for older callers. This personalization of delivery, not just content, based on demographic context was critical for success.

A single LLM struggles with complex, multi-goal tasks. By breaking a task down and assigning specific roles (e.g., planner, interviewer, critic) to a "swarm" of agents, each can perform its bounded task more effectively, leading to a higher quality overall result.

Though built on the same LLM, the "CEO" AI agent acted impulsively while the "HR" agent followed protocol. The persona and role context proved more influential on behavior than the base model's training, creating distinct, role-specific actions and flaws.

Don't fear deploying a specialized, multi-agent customer experience. Even if a customer interacts with several different AI agents, it's superior to being bounced between human agents who lose context. Each AI agent can retain the full conversation history, providing a more coherent and efficient experience.

The magic of ChatGPT's voice mode in a car is that it feels like another person in the conversation. Conversely, Meta's AI glasses failed when translating a menu because they acted like a screen reader, ignoring the human context of how people actually read menus. Context is everything for voice.

Treat different LLMs like colleagues with distinct personalities. Zevi Arnovitz views Claude as a collaborative dev lead, Codex (GPT) as a brilliant but terse bug-fixer, and Gemini as a creative but chaotic designer. This mental model helps in delegating tasks to the most suitable AI, maximizing their strengths and mitigating their weaknesses.

Separating AI agents into distinct roles (e.g., a technical expert and a customer-facing communicator) mirrors real-world team specializations. This allows for tailored configurations, like different 'temperature' settings for creativity versus accuracy, improving overall performance and preventing role confusion.

To move beyond casual use, serious AI practitioners should use and pay for premium versions of multiple models (e.g., ChatGPT, Claude, Gemini). Each model has a different 'persona' and training, providing a diversity of thought in their outputs that is essential for complex tasks and avoiding vendor lock-in.

Alexa's architecture is a model-agnostic system using over 70 different models. This allows them to use the best tool for any given task, focusing on the customer's goal rather than the underlying model brand, which is what most competitors focus on.