Large language models (LLMs) have transformed conversational agents, powering applications from everyday assistants to domain-specific systems. Yet, their internal mechanisms remain opaque, limiting our understanding of how complex behaviours are represented. Therapeutic conversational agents provide a compelling setting to study this problem, as they require models to encode empathic behaviours. For a better understanding of these behaviours, we present M-AIDE, an agentic framework designed to systematically interpret empathy-related features in LLMs. We apply this technique to therapeutic dialogue data, specifically to understand how LLMs may encode perceived empathy. Our approach leverages mechanistic interpretability to uncover artificial empathy features aligned with psychological categories of empathy. M-AIDE integrates automated interpretability into its pipeline, enabling large-scale classification and explanation of discovered features without exhaustive manual inspection. Our experiments reveal a gradient of representation: low-level features predominate at early layers, while distinct empathy features emerge as layers become deeper. The source code is available at: https://github.com/ai-voyage/M-AIDE.git.