TEA: Structurally Representing Arbitrary LLM Agents Collaboration

LLM agent collaboration may take many forms, ranging from predefined or dynamic workflows to open-ended interactions, in which agents create and revise tasks and choose when and with whom to work on the fly. Less structured collaboration lets agents organize their own work and adapt to diverse tasks, environments, and changing teammates, but performance also becomes less reliable and harder to understand. Indeed, even if a workflow is predefined, agents may deviate from it during execution, making tracing each agent's contribution difficult. We introduce Task-Environment-Agents (TEA), a flexible structural representation that makes evolving collaboration explicit without prescribing agent reasoning, behavior, or communication order. TEA couples an Agent Interaction Graph (AIG) to both a Task Activity Graph (TAG) and the environment to form a collaboration state, providing a basis for analysis and optimization. We demonstrate TEA’s potential by representing and recovering emergent collaboration structures and analyzing collaboration states. TEA also serves as an active framework through which agents communicate, transform tasks, and act in the environment. We show that TEA enables and improves decentralized agent collaboration on shared tasks.