We want to optimize prompt engineering for chatbot development by splitting our monolithic prompt into multiple specialized agents. What coordination patterns work best to pass data cleanly between a routing agent and specialized execution sub-bots without breaking?
3 answers
The most reliable pattern for multi-agent orchestration is a hub-and-spoke supervisor model. Your central routing prompt should have one job: analyze user intent and output a strict, machine-readable destination token. The underlying application fabric captures this token and dynamically forwards the conversation to the designated sub-agent prompt template. Crucially, each sub-agent must remain completely isolated, receiving only its specific system instructions and the immediate state payload it needs to execute its task.
Do your sub-agents write back to a shared global memory state object, or do you pass the full raw text history across every single agent hop?
Using an adversarial checker agent to review outputs prevents routing loops.
Guarding against routing loops is highly critical, Evelyn. Having a dedicated validation prompt inspect the handoffs ensures that agents don't bounce the user back and forth indefinitely when encountering vague or highly ambiguous input phrases.
Passing raw chat strings across agent hops creates immense token bloat, Albert. We solved this by using a centralized key-value state store. The routing bot and sub-agents simply read and update specific structured keys, which keeps individual prompt payloads incredibly lean and lightning fast.