A Structural Theory of Agency is a foundational framework that examines what must be true, at a structural level, for a system to meaningfully qualify as an agent. Rather than focusing on any specific physical, biological, or technological implementation, the framework identifies minimal conditions that must be present for agency to exist, persist, and remain coherent over time.

The theory operates at the level of logical organization rather than mechanism. It does not propose architectures, algorithms, or training procedures. Instead, it asks a more basic question: what kinds of structural capacities must any system possess in order to initiate action, make choices, endure disruption, and avoid repeating the same failures across time?

This perspective is intentionally cross-domain. It is meant to apply equally to individuals, organizations, artificial systems, and biological processes, without privileging any single substrate. By remaining abstract, the framework avoids collapsing into psychology, neuroscience, or machine learning, while still offering a consistent way to reason about all of them.

In the context of artificial intelligence, the framework is not a training method but a constraint lens. It is intended to be used to evaluate whether a learning system's apparent recovery, adaptation, or alignment reflects genuine structural persistence—or whether it merely represents short-term repair that remains brittle under distribution shift or long-horizon stress.

Many AI systems demonstrate impressive performance improvements while still exhibiting repeated failure patterns under slightly altered conditions. This framework is designed to clarify why such recurrence happens and to help distinguish between systems that patch errors locally and those that structurally reduce the likelihood of returning to the same failure modes over time.

Used in this way, the framework can inform:

• evaluation of long-horizon agents
• analysis of failure recurrence after retraining or fine-tuning
• distinctions between superficial learning and durable adaptation
• safety and robustness discussions where "repair" and "responsibility" are often conflated

Importantly, the framework does not specify how these properties should be implemented. It constrains what must be present for them to be possible at all.

The claims of this framework are not intended to be accepted on authority. Its validity depends on application and stress-testing. In practice, this means examining real or simulated systems and asking whether the framework successfully predicts failure modes, explains recurrence, or rules out designs that appear adequate but prove unstable under pressure.

Where the framework fails to add clarity, that failure is informative. Where it consistently separates durable agency from brittle behavior across domains, it earns its relevance. The framework is therefore best understood as a diagnostic and evaluative tool rather than a prescriptive solution.

The material presented here represents an excerpted, non-exhaustive view of the broader theory. Specific structural elements, ordering relationships, and necessity arguments are intentionally omitted. This public version is shared to communicate the perspective and invite serious inquiry, not to provide a complete specification.