Long-term conversational AI requires two distinct capabilities: remembering what was discussed and adapting to how a user’s preferences evolve over time. We argue these operate on different timescales and require separate but interoperable architectures. AgenticLOCI is a five-agent framework built on this premise: session retrieval fuses a short-term semantic ranker with a long-term keyword scorer via rank fusion, while personalization is maintained through a versioned deliberation-state memory that accumulates decision-episode snapshots across interactions. We evaluate on three benchmarks using Qwen 2.5-7B-Instruct. On LoCoMo, the AgenticLOCI Backbone achieves F1 = 0.342 versus BM25 = 0.285 while consuming 7.3× fewer input tokens per item. On LongMemEval-S, item-level analysis confirms that the two retrieval layers solve architecturally distinct sub-problems; at 7B scale the keyword layer leads, a reversal expected to resolve at larger model sizes where LLM ranking is more reliable. On PersonaMem, all configurations perform within a narrow band, establishing that memory benefit is conditional on context-budget pressure. The longitudinal personalization layer is evaluated on a synthetic mobility benchmark with known user trajectories, keeping 97.6% of stable preference fields unchanged and making warranted updates with 58.3% precision against an ablation baseline. We demonstrate the full framework as a personalized urban mobility assistant for Magdeburg.