LangChain highlights an IO-HMM way to model agent turns and user signals
LangChain · x · 2026-07-23
IO-HMM separates what users signal from what agents control
LangChain highlights a design from @getcandidly that treats each conversation turn as two distinct pieces:
- User behavior: observable signals that reveal the conversation state
- Agent behavior: controllable inputs that shift the state forward
The shared diagram shows an IO-HMM framing: hidden state evolves over turns, user signals are emissions, and agent responses act as inputs that change the transition dynamics. The point is to keep observation and intervention separate rather than collapsing them into one stream.
This is a compact but useful way to think about agent orchestration and turn-level state modeling.
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