Agentic AI needs engineering rigor
Experts at AI Expo Taiwan argued agentic AI—systems that perceive, decide, and act—must be treated as an engineering discipline with rigorous design, deployment, monitoring, and security practices. The shift matters for manufacturing deployments where agents will directly control physical processes and safety‑critical flows. (digitimes.com)
AI EXPO Taiwan ran March 25–27, 2026 at the Taipei Yuanshan exhibition halls and centered tracks on agentic AI and inference at the edge. (inside.com.tw) The event agenda listed sessions including “Beyond LLM: The Rise of Inference Economics” (Linsey Rodenbach, NVIDIA) and talks from Winston Hsu (NTU), Jeffrey Huang (AMD Taiwan commercial head) and Johnny Wu on edge integration. (ai-expo.tw) Exhibiting firms named on the conference pages included AWS, Google Cloud, NVIDIA, AMD, IBM, Microsoft, Oracle, Lenovo and ASUS, signaling cloud-to-edge vendor alignment around agent frameworks. (digitimes.com.tw) An AWS-backed session at the expo broke agent architectures into goal/instruction layers, tool invocation, memory and data plumbing as operational primitives for autonomous workflows. (cyberq.tw) Manufacturing case studies presented at the show and in related reporting described agent-driven workflows that detect production deviations, reschedule lines, update MES work orders and automatically trigger supplier follow-ups. (manufacturingdive.com) Speakers and analysts at the expo emphasized guardrails, telemetry and role-based oversight for agents that interact with PLCs and physical actuators, raising cross-team requirements for deployment, monitoring and incident response. (cyberq.tw) The conference materials cited macro stakes for industrial AI, quoting a projected US$15.7 trillion in AI-created economic value by 2030 as a rationale for accelerating production-grade agent engineering. (ai-expo.tw)
