Stateful Reasoning Runtimes: Architectural Patterns for Identity Persistence Over Stateless LLM APIs

Research Context: This work is a core component of the Presence Engine™ Living Thesis (DOI: 10.5281/zenodo.17280692). 

This paper introduces the architectural concept of stateful reasoning runtimes for LLM applications. Modern LLM APIs operate as fully stateless inference engines. Each call is independent and retains no memory of prior interactions. Current industry solutions externalize context using session replay, vector memory, and retrieval systems. These approaches reconstruct history, but they do not preserve identity continuity.

This paper proposes a three-layer architectural taxonomy for AI state: conversational state, associative state, and dispositional state. We demonstrate that dispositional state is required for identity-preserving applications such as therapeutic AI, tutoring systems, and autonomous agents. We present architectural patterns, runtime design principles, and failure-mode mitigations for implementing dispositional state without modifying the stateless foundation of LLM APIs.

The contribution is a reference framework and technical implementation pattern for persistent identity governance in AI agents. This shifts the focus from context reconstruction to continuity of reasoning, behavior, and ethical constraints across sessions.

Keywords: stateful AI, identity persistence, LLM architecture, cognitive runtime, dispositional continuity, memory systems, agent orchestration