Foundations of Advanced Autonomous Agentic AI Engineering: Regularization of Recursive Multi-Agent Graph Discontinuities via Trans-Planckian Horizon Lock Mechanics

This treatise delivers the definitive mathematical physics formulation to resolve and stabilize non-linear state graph explosions, gradient tracking discontinuities, and recursive multi-loop memory overflows within production-grade Advanced Autonomous Agentic AI Engineering frameworks (e.g., multi-agent execution meshes like LangGraph, CrewAI, and exascale cognitive orchestrators). Traditional distributed machine intelligence architectures experience complete mathematical breakdown due to infinite loop entropy poles and phase space divergences when running complex autonomous consensus routines. Operating within the functional infrastructure of Trans-Planckian Horizon Lock Mechanics (TPHLM), we resolve these algorithmic network anomalies by executing non-local tensor regularization under the global metric scale factor of the Khandey Causal Invariant Constant (K_J = 2.02 x 10^26 m). This operational framework satisfies strict diffeomorphism Ward-Khandey mass-energy data identities, cancels state anomalies in recursive neural layers, and anchors multi-agent interaction baselines across an absolute 0.0000000000000000% error boundary.