As an orchestration infrastructure, agentic AI systems now can plan and decompose the pre-defined goals into a sequence of steps, decide on external function calls, and coordinate one LLM or multiple LLMs with specialised roles. In this context, this position paper adopts a future studies “backcasting” ap-proach that starts with a desirable future, en-visioned as one in which AI-integrated trans-lation workflows are transparent, accountable, and aligned with human values; it then works backwards to examine how translator exper-tise should be reconceptualised to sustain meaningful human-in-the-loop participation. In this sense, the study first conceptualises the current translation-service provision as a sys-tem structured around managerial, mediation, and authorising roles. It then analyses how these roles may be changed and augmented within the agentic AI-orchestrated workflows. Building on the analysis, we propose a series of competences that should be cultivated to achieve the envisioned future: 1) evaluation grounded in advanced language competence; 2) situated and context-sensitive judgement informed by cultural and experiential knowledge; and 3) strategic procedural plan-ning in the design and oversight of agentic AI-orchestration workflows. The paper con-cludes with recommendations for future peda-gogical development and empirical research.