To address latency in multi-turn conversations within Cortex Analyst, implementing an LLM summarization agent to condense conversation history is the key strategy. Cortex Analyst utilizes such an agent to manage arbitrarily long conversation histories, preventing longer inference times, non-determinism, and performance degradation that would occur if the full history were passed to every LLM call. Option A is incorrect because Snowflake recommends executing queries that call Cortex AI SQL functions, including those underlying Cortex Analyst, with smaller warehouses (no larger than MEDIUM), as larger warehouses do not increase performance for these functions. Option C is a manual approach to context management, whereas Cortex Analyst incorporates an automated summarization agent for this purpose. Option D is problematic because while a smaller model might reduce general inference latency, Cortex Analyst specifically chose Llama 3.1 70B as its summarization agent due to its superior accuracy (96.5% good rating by LLM judge) over Llama 3.1 8B (5% error rate) for this task, indicating that a smaller, less capable model could degrade summarization quality. Option E is incorrect as 'verified_queries' are for specific, pre-defined questions and do not handle the dynamic, contextual nature of multi-turn conversations or the summarization of past turns.