ECHO: Evolving Coordination from Human Operations for Training-Free LLM-Based Multi-Agent Decision Making

Large language models (LLMs) enable training-free multi-agent decision-making, but human interventions in these systems are typically executed as transient, single-agent commands. This local execution fails to re-coordinate the remaining agents, forcing human operators to repeatedly perform similar manual interventions across episodes. We formulate human-guided multi-agent adaptation as inducing reusable, anchor-conditioned coordination patterns from localized human operations. We introduce ECHO, a training-free framework that grounds each human intervention as an immutable anchor within a shared collaboration graph and dynamically reorganizes the roles, intents, subgoals, and coordination dependencies of responder agents around it without modifying the underlying LLM parameters. The resulting responder-side adjustments are abstracted into identity-agnostic, relational coordination operators that encode transferable team reorganization knowledge while accumulating outcome evidence and human feedback. A structured matching mechanism and an evidence-gated Shadow deployment pipeline progressively promote candidate operators from confirmation-dependent execution to fully autonomous application. Evaluations on SMAC and Overcooked-AI show that ECHO improves immediate complementary coordination and supports cross-episode knowledge reuse. Across continual evolution, ECHO maintains a 100.0% success rate while reducing human intervention rate by 51.8%. Frozen operators also reduce human interaction on held-out tasks, with no harmful automatic applications observed in the evaluated transfer trials. These results show that human operations can provide persistent coordination supervision rather than only momentary control.