STATE DYNAMICS OF AGENT HARNESSES：WHAT STATES MATTER FOR PRESERVATION？

Agent harnesses shape long-horizon agent behavior by controlling base model and tool calls, feedback integration, and persistent workspace state. As a result, the same base model can exhibit different interaction dynamics and task outcomes across Harnesses. Yet existing Harness designs are still shaped largely by empirical rules, leaving the propagation of local changes over a finite interaction window, the conditions that a state representation must preserve for later operations, and the formation of a readable task object without a common dynamical account. In this work, we develop a conditional state dynamics framework that formalizes finite response, conditional attractor structure, operation-aware state fidelity, and task readout. Analyses across models, harnesses, and tasks provide empirical support for the relationships among spectral structure, interaction persistence, successor prediction, and code readout. Building on the task-object criterion, we propose BasinGuard, an online plugin that runs at Harness protocol events, continues the host protocol while the object is still forming, and invokes the specified readout once the object is determined. Across the evaluated model–Harness conditions, BasinGuard raises the normalized task score by 17.15% on average on code workspaces and improves official pass rates on GAIA2.