This paper introduces a conceptual architecture for a next-generation Agentic AI e-service using Generative AI and Augmented Reality (AR) for real-time, field-based timber properties and carbon footprint assessment. Designed to address limitations of traditional manual methods in forestry, it combines Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and autonomous agents within a unified, multimodal framework. At its heart lies intelligent orchestration of AI Agents responsible for query analysis, knowledge retrieval, and prompt composition, working with a domain-specific vector database and embedded LLM for accurate, personalized, evidence-based insights in the field. AR serves as an active cognitive layer, projecting enriched information-visual annotations, quality classifications, compliance indicators-onto the physical environment. The e-service enables foresters and field technicians to receive instant, context-aware guidance via AR interfaces, significantly reducing uncertainty and improving decision-making. Furthermore, this paper presents an illustrative conceptual case study, demonstrating how this next-gen AI service will transform wood properties and carbon footprint evaluation into an interactive, data-driven process powered by intelligent agents and immersive visualization.