What the Research Literature Tells Us About the Challenges Retell and Vapi Must Solve for Multi-Turn Voice Workflows

When developers evaluate voice AI platforms like Retell and Vapi for complex, multi-turn conversational workflows, the comparison usually centers on four dimensions: how much control you get over the underlying LLM, how flexible the system prompt configuration is, how tool-calling and function routing work, and how conversation state is managed across turns. These are the right questions to ask. But answering them for any specific platform requires platform documentation and hands-on testing — and the source materials available for this article do not contain Retell- or Vapi-specific implementation details. What they do contain is a body of recent research that frames the problem space these platforms operate in: what multi-turn conversational AI systems are struggling with today, where the field's infrastructure pressures lie, and why the gap between perceiving input and sustaining coherent interaction is the central engineering challenge. That research context is what follows. Where platform-specific claims cannot be supported by the provided sources, that gap is noted explicitly rather than filled with plausible-sounding detail. A blank field is a research problem; a fabricated one is a lie that propagates downstream.