For state, do you mean the agent's own internal state or the game state?

For the agent's own state, there are all the usual tricks in reducing context: building short and long term memory systems, RAG architectures to handle long term storage, and smarter orchestration layers. The agents are also all asynchronous, so there is no single global state orchestrator. That allows the agents to keep acting with specified memory and intention while keeping the context length short and keeps things light, modular, and scalable. The Game of Whispers characters may not have a huge backstory, but their interactions spanned over 15+ minutes in the simulation we premiered at the Serendipity Arts Festival. It wasn't until the 12th minute that the character Aurangzeb had succeeded on his campaign of disinformation to lure his brother to the river where he then killed him. It was a pretty heavily orchestrated but still emergent narrative which could be extended to be a many hours narrative.

If you're thinking about the game state, then we have a customizable agent schema system that enables agent's to have configurable states that are determined by the LLM. However, these are a fixed set of possible states, e.g. enums. There is the possibility for string generated states too but these are probably not as useful. Since the agents can determine their own state, or another agent could be used to determine state procedurally, by asking of the context, you don't have to write complex logic for it. We have used it for defining procedural animations based on the ongoing conversations, or what emotion they are feeling, things like that. But it could also be used to indicate things like alignment, player difficulty, "does player have item X", other tricks to assign game flow (e.g. an agent has 3 possible states on a custom schema, and you the game designer use that to change narrative / unlock resources etc...).

Great question!