Modern intelligent systems do not fail randomly. They fail geometrically. Despite unprecedented performance gains, today's AI systems remain operationally opaque: we measure what they answer, but not how they behave under constraint. Existing evaluation methods focus on correctness, benchmarks and surface safety rules, leaving the internal mechanics of failure, compensation and termination largely uncharacterized. This paper introduces the Universal Intelligence Architecture™ (UIA), a model-agnostic forensic framework that makes AI behavior measurable, structural and predictable. UIA demonstrates that any agent processing information under constraint must traverse a fixed operational space composed of three phases (Analysis, Building and Closure) and nine irreducible computational primitives per phase. While all agents share this architecture, they do not traverse it equally. Each agent develops a single dominant operational trajectory, or Agent Manifold, that defines where it is fastest, how it compensates under stress and how it terminates under safety pressure. Crucially, this manifold can be empirically calculated.