@freeman1266: Software engineering methodology must shift from the traditional 'state perspective' to a dynamical system perspective. The core view advocates that 'attractor logic takes precedence over governance tools (Harness)', that is, first define the structural invariants that the system should converge to in the long term, rather than merely focusing on local constraints and verification. AI, as a high-frequency and directionless perturbing...
Summary
The article proposes that software engineering methodology should shift from a state perspective to a dynamical system perspective, emphasizing that attractor logic takes precedence over governance tools. In the AI era, it is necessary to explicitly model state space, attractors, trajectories, and controls to address architectural drift caused by AI as a high-frequency perturbation source.
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Cached at: 06/03/26, 11:48 AM
Software engineering methodologies must shift from the traditional “state perspective” to a dynamic system perspective.
The core viewpoint advocates that “attractor logic takes precedence over governance tools (Harness)” — that is, first define the structural invariants the system should converge to over the long term, rather than focusing solely on local constraints and verification.
As a high-frequency source of disturbance lacking sustained direction, AI can easily cause the overall system architecture to undergo trajectory drift even while all metrics pass inspection.
Developers need to explicitly model four categories: state space, attractors, trajectories, and control. The attractor should be materialized through architecture documentation, and independent audit and verification mechanisms should be used to ensure that system evolution does not deviate from the intended direction.
This methodology treats trajectory convergence as a first-class citizen, emphasizing that in the AI era, the architectural judgment to define system convergence goals is more scarce than mere code generation.
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