This chapter maps the transition from ad-hoc prompt interaction to agentic, workflow-orchestrated large language model applications in language services. A large language model agent is framed as a role-conditioned cognitive node executing perceive–reason–plan–act–reflect cycles via tool orchestration, explicit cognitive state, and layered (short/mid/long-term) memory. We distill seven enabling mechanisms: (1) cognitive state scaffolding for goal persistence and transparency; (2) selective memory capture, abstraction, retrieval, and metacognitive recall control; (3) language-based micro-planning (chain-of-thought, ReAct (reasoning + acting), tree of thoughts, Reflexion) decoupled from execution; (4) standardized tool/application programming interface schemas plus semantic invocation for scalable capability expansion; (5) perception–feedback pipelines converting partial signals into iterative strategy revision; (6) role alignment, coordination, and self-awareness to mitigate drift in multi-agent collaboration; and (7) workflow governance embedding observability, robustness (logging, tracing, retries, compensation), safety, and human-in-the-loop boundaries. Interoperability protocols, Agent-to-Agent protocol) lower integration friction and support decentralized task delegation. Workflow modeling paradigms (Business Process Model and Notation, code-as-workflow domain-specific languages, low-code/no-code visual canvases) externalize dependency logic and embed adaptive behaviors. A translation workflow lens shows modular allocation across translation, terminology governance, quality assurance, and compliance, enabling continuous optimization. Emerging competencies include agent orchestration literacy, workflow modeling, protocol stewardship, evaluative analytics, and ethical accountability.