### Answer The key difference is in control and structure [(1) ]() [(2) ]() [(3) ]() : Multi-Prompt Agent (with states) : - Organizes conversations into a structured tree of states, each with its own focused prompt and behavior [(1) ]() - Each state includes focused prompts, state-specific functions, transition logic, and context preservation - Provides predictable behavior where each state has a clear, focused purpose - Easier debugging since issues are isolated to specific states - Better function control as tools are only available when appropriate - Scalable design where you can add new states without affecting existing ones Conversation Flow Agent: - Provides the most control over dialogue paths with intricate conversation structures [(2) ]() - Allows developers to create dynamic interactions without rigid scripts - Minimizes the need for extensive prompt engineering - Offers more fine-grained control compared to Single/Multi Prompt agents [(3) ]() - Enables handling of more complex scenarios with predictable outcomes - Each node defines a small set of logic with transition conditions determining which node to transition to When to choose each: - Use Multi-Prompt when you need organized state management with focused prompts per conversation phase [(1) ]() - Choose Conversation Flow when high controllability over interactions is

required and you need to handle complex scenarios without heavy prompt engineering [(2) ]() [(3) ]()

​ To learn more, see the sources I considered: [[1] Single/Multi Prompt Agent Overview](https://docs.retellai.com/build/single-multi-prompt/prompt-overview) [[2] Retell AI’s Advanced Conversation Flow | Retell AI](https://www.retellai.com/blog/unlocking-complex-interactions-with-retell-ais-conversation-flow) [[3] How to Deploy Conversational AI | Retell AI](https://www.retellai.com/blog/how-to-deploy-conversational-ai) ​

Feel free to tag @AI Evy with additional questions. I also appreciate feedback, it helps me improve.