This study analyzes natural interaction in assistive systems that require low latency and high reliability. Monolithic LLM models struggle in this setting because a single agent must handle reasoning and tool execution simultaneously, leading to overload and long delays. A common failure occurs when the model misses required parameters and enters repeated inference cycles, causing timeouts exceeding 300 seconds. To address this issue, a modular multi-agent architecture was designed using deterministic orchestration and intent classification. Both approaches were tested across 108 tasks per batch, including searches, weather queries, and reminders. The multi-agent system eliminated all timeout errors, increased the success rate from 37% to 98%, and reduced average latency from 204 to 26 seconds. The system also demonstrated stable and safe behavior, essential in assistive environments. Results suggest that distributing work among specialized agents with clear rules is more reliable than a single monolithic model.