Single-Turn and Multi-Turn Interactions in Frontier Generative AI Models (LLMs)

This article examines how moving from a single-turn interaction to a multi-turn trajectory changes the state, cost, reliability, security, and governance of systems built on large language models. The analysis is a critical narrative review supported by scientific papers, official provider documentation, standards, security taxonomies, and explicitly identified original calculations. A model has no intrinsic conversational memory across independent inferences; continuity is reconstructed by the client or through provider-managed state services. When an application fully replays a history that grows at an approximately constant rate, the logical sum of input tokens grows quadratically with the number of calls, although caching, truncation, compaction, and external memory change billed cost, latency, and effective computation. Benchmark evidence shows that requirement fragmentation, cross-turn dependencies, intent shifts, information retrieval, and reliance on previous model outputs create different failure modes; there is no single “multi-turn” condition. Evidence on explicit reasoning and memory techniques is mixed and task- and protocol-dependent. In agentic systems, accumulated history also expands gradual attacks, persistence of malicious content, error propagation, and unbounded consumption. The article relates these findings to governed routing and SGAEIA, arguing for a separation between transcript, operational state, and assurance evidence. This relationship is an architectural interpretation and validation agenda, not empirical proof of the framework. Keywords: LLM; single-turn; multi-turn; conversational state; LLM routing; prompt caching; reliability; security;runtime governance; SGAEIA.