# Receptionist AI Design Pattern With the Receptionist [[Large Language Models (LLMs)|Large Language Model (LLM)]] pattern, all user inputs are passed to a receptionist LLM, which routes the user inputs to other specialized LLMs, and delegate the response generation to those. I discovered this pattern via [[Matt Pocock]]. He visualized it like this: ![[Receptionist LLM Pattern - visualization.png]] The interesting aspect of this pattern is that each LLM (including the receptionist) can load/use specific context, data, rules, tools, etc. In addition, combined with [[Prompt Lazy Loading AI Design Pattern (PLL)]], it's possible to avoid loading/keeping too much context, data, rules, etc. This reduces the risks of hallucinations, makes the usage more efficient, etc. ## Benefits - **Performance**: Only loads relevant information when needed - **Cost Efficiency**: Reduces token usage - **Clarity**: Clear separation of concerns - **Scalability**: Easy to add new roles or expand knowledge bases - **Maintenance**: Modular design allows for easy updates ## Extended Pattern: Agent Chaining The receptionist pattern can be extended with post-execution handoff handling. After a specialized LLM completes its work, the receptionist inspects its output for handoff suggestions (work discovered for another agent). If valid, it chains the next agent automatically; enabling emergent multi-agent collaboration without predefined team configurations. Safety rules prevent infinite loops: max chain depth (e.g., 3), no duplicate agents, no circular patterns (A->B->A). When the chain limit is reached, results are returned to the user with suggestions for manual follow-up. ## Related - [[Prompt Lazy Loading AI Design Pattern (PLL)]] - [[Progressive Disclosure]] - [[Context Engineering]] ## References - https://www.tiktok.com/@mattpocockuk/video/7514329600126635296