System thinking

System thinking in AI is treating a task as part of a wider system of interconnected elements — feedback loops, dependencies, long-term effects — instead of a linear chain of steps. In AI agents it shows up in multi-agent architectures where agents influence each other, in planners that account for side effects, and in reflective loops that evaluate how an action ripples through the system. It matters most in enterprise settings, where touching one business process can affect several others, and pairs naturally with OODA for adaptive decision-making. The opposite lens is sequential thinking.