AI voice agents often struggle to meet performance expectations in real-world settings despite impressive demonstrations, and this discrepancy largely stems from implementation issues rather than the technology itself. To build effective voice agents, it is crucial to make informed technical decisions at every layer of the system, including speech recognition, language models, and voice synthesis, while considering factors like noisy environments, latency, accented speech, and domain-specific terminology. Developers should prioritize asking the right questions during the design phase, focusing on aspects such as model accuracy, latency, conversational memory, and integration capabilities to avoid common pitfalls like hallucinations and poor user experience. Ensuring natural conversational flow, handling interruptions, and employing robust orchestration architectures are essential for creating user-friendly agents. Additionally, infrastructure considerations like reliability, security, and monitoring are vital for maintaining the agent's performance in production. Comprehensive testing and strategic planning can bridge the gap between demo success and actual deployment effectiveness.