Current paradigms in cloud-centric AI and edge pipelines treat planning, reasoning, and execution as monolithic, incurring high latency and energy costs as agentic systems scale. The Tri-Spirit Architecture decomposes intelligence into a Super Layer for planning on high-precision cloud substrates, an Agent Layer for reasoning on accelerator-class hardware, and a Reflex Layer for execution on low-power edge devices, coordinated by an asynchronous message bus with parameterized routing, habit-compilation, convergent memory, and safety constraints (arXiv:2604.13757).

Software-focused agent work such as ReAct prompting interleaved reasoning and acting but operated atop uniform GPU infrastructure, missing the hardware substrate mismatch that emerges at scale (arXiv:2210.03629). The transformer foundation similarly optimized for massive parallel matrix math on homogeneous GPUs yet created the very dependency Tri-Spirit targets by failing to differentiate cognitive latencies (arXiv:1706.03762). Original coverage overlooked how habit compilation in the Reflex Layer converts repeated reasoning traces into zero-inference policies, directly addressing behavioral continuity gaps neither ReAct nor transformer literature solved.

Simulation results across 2000 tasks demonstrate 75.6% latency reduction, 71.1% energy savings, 30% fewer LLM calls, and 77.6% offline completion, establishing cognitive decomposition as a primary efficiency lever over model scaling alone. This exposes a critical gap: GPU-centric designs cannot sustain persistent autonomous agents without layered, heterogeneous hardware. Tri-Spirit therefore reframes AI infrastructure away from uniform accelerators toward brain-like specialization, though real-world validation of the routing policy and memory convergence remains required.