Despite the rapid advancement of generative agents, their deployment in real world industry scenarios often encounters challenges due to a lack of domain-specific knowledge. To address this gap, we present KnowPilot: a Domain-Specific Knowledge Augmented Agent System. KnowPilot is an open-source framework that integrates task-specific priors, explicit knowledge, and experiential knowledge to enhance agent performance in specialized applications. It combines knowledge retrieval from structured repositories with a memory system capable of capturing expert experience through human–AI interaction.

Figurative art, as a culturally embedded medium, encodes narrative, symbolic, and emotional meanings that reflect artistic choices and historical realities. The recent availability of large-scale digital collections of figurative artworks creates opportunities for computational analysis, but existing methods mostly focus on classification or style detection, lacking structured modeling of high-level figurative elements and integration of cultural context. We present DAVA, a visual analytics system that supports interdisciplinary exploration of figurative art. First, we model paintings across three structural levels: facial expressions (micro), posture features (meso), and object co-occurrence (macro). Second, we employ a vision-language model to discover latent patterns from these features and present them through novel visualization designs. Third, we introduce domain-informed AI agents that simulate interdisciplinary research teams to interpret artworks in cultural and historical context. To evaluate DAVA, we first conducted a quantitative evaluation demonstrating the accuracy and consistency of the multi-agent interpretation mechanism. Case studies and expert interviews then confirmed the system's utility and support for semantically and historically informed exploration of figurative art.