<p>Large language models (LLMs) have demonstrated remarkable capabilities across different domains, yet two critical challenges limit their reliability: opaquely generated responses and role drift in extended interactions. Moreover, the possibility of a multi-layered dialogue, shaping LLM behavior both asynchronously across sessions and, optionally, in real time, remains largely unexplored. This paper presents XBot, a conversational agent designed to address these challenges. Built on the GPT-4o API, XBot requires no access to internal weights or activation space, making it portable across different LLM-based systems. It analyzes the user message and decomposes it into chunks, each annotated with topic and sentiment. It then selects a domain validated strategy from a curated expert-defined set, based on the assigned role, spanning multiple levels of granularity from general response types down to topic- and sentiment-specific guidance. Each response is therefore accompanied by explicit justifications accessible to non-technical users, supporting a continuous three-way dialogue among users, domain experts and XBot for ongoing validation and iterative refinement. Domain experts can inspect, modify and extend the strategy set at any time, ensuring alignment with professional standards and ethical requirements. Experimental comparisons with GPT-4o vanilla across three roles, evaluated through an ablation study and a multi-evaluator panel combining LLM-based and human judges, consistently rank XBot as the best performing system across all dimensions, demonstrating superior empathy, role stability and conversational depth, while GPT-4o vanilla exhibits pervasive persona drift across all experimental scenarios.</p>