As Large Language Model (LLM) reasoning capabilities continue to evolve, AI Agents have officially surpassed passive "Copilots" to become the core of global digital transformation. These agents are now capable of autonomous planning, multi-step execution, and real-time strategic adjustments, marking a shift in how organisations approach digital transformation. According to MarketsandMarkets' "AI Agents Market Report (2025–2030)," the global AI Agent market is projected to experience explosive growth over the next five years, reflecting widespread enterprise adoption.At AI Expo Taiwan 2026, this shift was a central theme across the event, with multiple organisations highlighting agent-based architectures. HCLTech was among those presenting its perspective under the theme "Are You Agent-Native Yet?". The company showcased a suite of solutions tailored for semiconductor manufacturing and financial services aimed at helping enterprises operationalise AI agents and integrate them into core business processes.Commenting on the evolving AI landscape, Terry Tai, Country Leader of HCLTech Taiwan, noted that "under the traditional Copilot model, humans act as coordinators, giving specific instructions via chat interfaces for AI to execute. In the AI Agent era, however, these agents take a high-level objective, autonomously plan, orchestrate tools, and iteratively complete tasks. Humans have transitioned from coordinators to supervisors and are no longer bogged down by tedious intermediate steps.""The real shift comes down to reasoning capability," said Alan Flower, Executive Vice President and Global Head of Cloud and AI Labs at HCLTech. "It's what allows the latest Frontier models to work through complex intermediate steps, with agentic frameworks enabling shared knowledge across the new multi-model, multi-agent solution domain.As organisations move to AI-Native approaches, it's becoming clear that this isn't just a technology change, it's a cultural transformation as organizations re-engineer their core value streams to be augmented and delivered by agentic AI. You need to think about the responsibilities you are prepared to delegate to AI, retain human-in-the-loop, or allow fully autonomous human-on-the-loop approaches. You need to reskill and train your workforce; teach them to assemble teams of AI agents to whom they will delegate work. For example, software engineers now need to describe software, and delegate the coding to agents, not write all of it themselves."Solving Smart Manufacturing Pain Points: HCLTech Kinetic AI.InspectTaiwan's semiconductor and high-tech industries lead the world, yet traditional facility inspections still struggle with high labor costs and significant safety risks. For instance, in a semiconductor wafer fab, engineers can spend considerable time merely complying with gowning and entry protocols before addressing a single device malfunction. These logistical delays represent a significant, yet often overlooked, hidden cost for high-tech manufacturers.HCLTech featured Kinetic AI.Inspect at the expo, a solution specifically designed to address these pain points. HCLTech builds "Hybrid Inspection Fleets" using quadrupeds (robot dogs) and drones, integrated with 3D reality capture and real-time AI analysis. This solution, already deployed by a leading global aircraft manufacturer, has delivered significant results: reducing unplanned downtime by 30%, increasing inspection frequency by 30x, and boosting post-processing productivity by up to 95%.Flower pointed out that with Kinetic AI.Inspect, if a robot dog detects an anomaly, it doesn't just sound an alarm; it can autonomously trigger an ERP system check for spare parts. If no stock is found, it automatically generates a Purchase Order (PO) to initiate the repair process. This Agent-Native flexibility is something traditional, stationary IoT sensors cannot achieve.Tai added that these applications extend across all manufacturing sectors, including steel, petrochemicals, and offshore wind power, where reducing on-site human risk is critical. Many Taiwanese firms expressed strong interest at the event and are currently planning Proof of Concepts (PoC).Implementing Agentic SDLC with HCLTech AI ForceBeyond manufacturing, HCLTech introduced the AI Force platform for the software-heavy tech sector. This platform supports the full Agentic SDLC (Software Development Life Cycle), covering automated requirement documentation, API specification architecture, and code refactoring. Internal benchmarks show a 30% increase in development speed, a 45% boost in testing efficiency, and a 60% acceleration in legacy application modernization. As a TSMC Design Center Alliance (DCA) partner, HCLTech also applies AI to semiconductor R&D, automating specification interpretation and test plan generation to maximize engineering throughput."When you look at the B2B Accounts Payable landscape, the scale is enormous - in Taiwan alone it's worth around USD 215 billion annually. Yet much of it still runs on manual processes, with global Straight-Through Processing rates sitting at just 32.6%," said Tai."What we're seeing is a shift. By applying specialised agents to tasks like data extraction and duplicate payment detection, it's possible to move beyond those constraints. In some cases, STP rates are rising above 80%, invoice processing costs are dropping by more than 60%, and duplicate payments are falling to under 1%."With over 200,000 employees globally, HCLTech operates innovation labs in the US, UK, Germany, India, and Singapore. In late 2025, HCLTech partnered with NVIDIA to launch an AI Lab focused on scaling Physical AI and cognitive robotics for industrial use. By assessing technical maturity and data readiness, HCLTech continues to help enterprises explore and incubate new technology use cases as their primary AI transformation partner.To find out more, please visit HCLTech.HCLTech at AI Expo Taiwan 2026. Credit: HCLTech

National Yang Ming Chiao Tung University (NYCU) successfully hosted the 2026 Global Consortium for Innovation and Engineering in Medicine (GCIEM) Global Summit. This international academic exchange highlighted that the cross-disciplinary integration of medicine and engineering has entered a stage of systematic development. Dr. Albert C. Yang, Chairman of the Department of Medicine and Director of the Center for Digital Medicine and Smart Healthcare at NYCU, pointed out that Taiwan is progressively stepping outside traditional medical education frameworks to cultivate interdisciplinary talent bridging engineering and healthcare. The summit served as a crucial opportunity to showcase the results of these long-term investments to a global audience.ASUS showcased its smart healthcare strategic layoutroadmap at the summit. Joe Hsieh, Chief Operating Officer of ASUS, stated that in addition to its talent pool, Taiwan possesses key foundational advantages such as comprehensive medical data, industry agility, and system integration capabilities. While these factors have accelerated the real-world deployment of related applications, ASUS remains committed to further elevating Taiwan's global visibility through ongoing industry-academia collaborations and continuous international platform connections.GCIEM Strengthens International Ties; ASUS Showcases Smart Healthcare SolutionsThe inception of GCIEM traces back to post-pandemic international exchanges. In 2022, an NYCU delegation visited the University of Illinois Urbana-Champaign (UIUC) and observed that certain academic and research institutions had already integrated engineering into medical education. This catalyzed the joint efforts to establish GCIEM and its annual summit mechanism. Following the inaugural summit in the U.S., Taiwan was selected to host the second edition. Dr. Yang believes that hosting the summit in Taiwan allowed the international community to witness Taiwan’s departure from traditional medical education, systematically demonstrating its achievements in med-tech integration while strengthening global ties.As a global leader in smart healthcare, ASUS participated in GCIEM 2026. At the summit, the company aimed not only to demonstrate its technical expertise but also to validate Taiwan's integration capabilities in engineering medicine. Joe Hsieh noted that ASUS has long been strategically positioned in medical applications. Its technological focus has evolved from early physiological data collection via the ASUS VivoWatch smart health watch and medical imaging utilizing the ASUS Handheld Ultrasound, to advanced AI applications. Progressing from sensor technology and medical image processing to model-driven AI, ASUS is now advancing toward No-Code AI platforms and Agentic AI, showcasing the evolution of medical technology from assistive tools to intelligent decision-making systems.NYCU and ASUS have collaborated extensively in recent years to integrate smart healthcare systems. A prime example is the clinical application of the ASUS VivoWatch smart health watch, which collects physiological signals to assess risks related to sleep, stress, and sleep apnea. Additionally, the introduction of Ambient AI-powered voice recognition technology has significantly enhanced clinical documentation efficiency and optimized medical workflows. Reflecting on these collaborative experiences, Dr. Yang asserted that Taiwan’s smart healthcare capabilities, in terms of both clinical techniques and medical quality, are on par with those of other advanced nations. He believes that international platforms like GCIEM will continue to expand Taiwan's global visibility, systematically presenting its achievements and advantages in the smart healthcare sector.NYCU Highlights Physician-Engineer Program to Deepen MedTech IntegrationDr. Yang further pointed out that the integration of medicine and engineering has progressed from the application layer to the talent cultivation system. To address this, NYCU has implemented a six-year Physician-Engineer Program within its Department of Medicine. The program equips medical students with a solid foundation in electrical engineering and computer science, fostering the cross-disciplinary expertise needed to drive medical innovation and bolster Taiwan's talent advantage in both fields. Joe Hsieh added that beyond talent, Taiwan possesses critical competitive advantages, including comprehensive medical data, industry speed, and exceptional system integration capabilities. [1] Joe Hsieh stated that in addition to talent, Taiwan possesses key competitive advantages such as data, speed, and system integration capabilities.He noted that Taiwan's highly concentrated industrial supply chain enables rapid technical integration and product deployment, while its long-accumulated data provides ideal conditions for AI model training.[2] Regarding medical data, Taiwan's long-accumulated data foundation provides optimal conditions for AI applications. Furthermore, the high concentration of Taiwan’s industrial supply chain enables rapid technical integration and product deployment, ensuring extraordinary industrial responsiveness. In terms of system integration, the capability to transform systems into total solutions remains a core advantage of Taiwan's MedTech ecosystem.Addressing collaborations with academic and research institutions, Joe Hsieh pointed out that as AI enters a phase of high specialization, healthcare is a field with significant barriers to entry. This requires deep, tripartite collaboration between enterprises, academia, and medical institutions to effectively bridge technology with clinical needs. He stated that Taiwan's unique geographical and industrial concentration accelerates the verification and deployment of medical research findings. ASUS has currently deployed hundreds of engineers to develop medical AI, utilizing industry-academia-research collaborations to streamline the path to bringing efficient and high-impact results to real-world clinical applications.AI Enters Clinical Decision-Making; Trust Remains the Key to Healthcare SystemsJoe Hsieh further noted that AI's role in the medical field is rapidly transforming. Medical AI has progressed from the early AI 1.0, which focused on image recognition, to AI 2.0, capable of integrating multimodal data. Moving forward, it will transition into Agentic AI featuring task execution and proactive collaboration capabilities, gradually entering the core of medical workflows.Dr. Yang emphasized that AI's clinical positioning is not to replace physicians, but rather to serve as a support system for preliminary screening and alerts. In areas such as image interpretation, endoscopy, and critical care decision-making, AI assists in improving efficiency and reducing the risk of human omission.As AI evolves from assisting in interpretation to participating in workflows, the depth of its application increases. However, the high requirements for accuracy and accountability in healthcare make trust a critical factor for adoption. Joe Hsieh pointed out that due to the inherent uncertainty in AI judgments, reliability must be enhanced through foundational computing power, trustworthy models, and multi-model cross-validation mechanisms.The question of whether Taiwan can transition from a technology adopter to a standard-setter against the backdrop of rapid medical AI development has become a key focus for both the medical and tech industries. Dr. Yang mentioned that standards are not formed through a top-down approach; instead, they emerge from applications recognized by frontline medical staff. These practical experiences are gradually refined and accumulated, eventually transforming into followable guidelines. Joe Hsieh added that the core of standardization lies in verifiability. Establishing consistent workflows through multi-model cross-validation to drive the standardization of decision-making mechanisms will be an essential foundation for developing medical Agentic AI. Throughout this process, Sovereign AI serves as the critical foundation for ensuring data and model autonomy. By leveraging its existing advantages in medical data to build sovereign models and application ecosystems, Taiwan has the opportunity to secure a stronger voice and greater strategic influence in the global development of medical AI.Refocusing on the Patient-Physician Relationship in the Era of AI WorkflowsRegarding the future development of smart healthcare in Taiwan, Dr. Yang suggested starting by enhancing patient-physician interactions. He cited the concept of a "computerless clinic," powered by Ambient Clinical Intelligence (ACI), as a prime example. In this scenario, wearable sensors and Ambient AI systems collect and analyze patient physiological data in real time, while automatically generating electronic health records (EHRs), ordering tests, and entering data into backend systems. This innovation ultimately frees the consultation process from the distractions of manual computer operations.Joe Hsieh concurred, adding from the perspective of real-world deployment that multiple Agentic AI systems featuring voice recognition, image analysis, and sensory capabilities could operate synergistically in the future. This collaborative approach establishes a digital assistant architecture with a clear division of labor. Combined with wearable devices and smart glasses, technology can be integrated seamlessly and invisibly into medical workflows to provide real-time information. This ultimately allows physicians to focus entirely on clinical judgments and patient interactions, thereby elevating overall efficiency and quality of care.Dr. Yang concluded by pointing out that hosting GCIEM 2026 has allowed Taiwan to transition its role in med-tech integration from a mere participant to an active practitioner. As AI advances from a supportive tool to decision-making and execution, the competitive focus within the healthcare industry is shifting from singular technical capabilities to system integration and the establishment of trust mechanisms. The collaboration between ASUS and NYCU demonstrates the pathway from talent cultivation and data accumulation to real-world deployment, gradually forming a replicable and scalable development trajectory. With cross-disciplinary capabilities serving as a solid foundation, Taiwan is poised not only to participate in this smart healthcare transformation but also to define its future direction.

iCatch Technology announced that its 360-degree vision-based obstacle avoidance system has been successfully integrated into Avilon Intelligence's drone platform. Through four camera modules, multi-view image perception, real-time AI vision processing, and flight-control coordination, the system enhances autonomous flight safety, stability, and intelligence in complex environments.Compared with traditional single-direction or partial obstacle avoidance architectures, iCatch Technology's 360-degree vision-based obstacle avoidance system uses four camera modules to build a more comprehensive surrounding perception capability. This allows drones to simultaneously understand environmental information from multiple directions, including front, rear, left, and right, effectively reducing collision risks while improving flight stability and mission success rates.The integration between iCatch Technology and Avilon Intelligence demonstrates the practical application value of the 360-degree vision-based obstacle avoidance system on a real drone platform. By combining the 360-degree module system, image input, real-time computing, and flight-control coordination, drones can move beyond simply "seeing the environment" toward "understanding the environment and actively avoiding obstacles," making intelligent flight a truly deployable system capability.Weber Hsu, General Manager of iCatch Technology, stated: "360-degree vision-based obstacle avoidance is not only an upgrade in obstacle avoidance capability, but also an important foundation for drone platforms moving toward advanced autonomy. Through a comprehensive visual perception architecture and modular integration capability, we aim to help customers shorten development cycles, lower integration barriers, and enable more drone platforms to complete integration faster and truly take flight."Dafeng Huang, Chief Technology Officer of Avilon Intelligence, stated: "Our collaboration with iCatch Technology is not merely the adoption of a single module, but an important milestone in integrating visual perception capabilities with drone platforms. Based on Avilon Intelligence's existing autonomous flight controller and Visual SLAM architecture, the system further combines iCatch Technology's panoramic imaging and depth perception capabilities. In indoor environments without GPS, it can improve positioning accuracy and obstacle avoidance response. In outdoor scenarios where GPS signals are interfered with or obstructed, real-time image capture and environmental recognition can also support navigation decisions, further enhancing flight stability and mission reliability. This provides more resilient technical support for drone applications in highly complex environments."Through this successful integration with Avilon Intelligence's drone platform, iCatch Technology once again demonstrates its technical strengths in AI vision SoCs, image sensing integration, and drone application system development. Looking ahead, iCatch Technology will continue to promote a modular sales model, helping drone manufacturers avoid the complex process of redeveloping hardware. From assembly and integration to deployment, the solution enables products to take flight faster and business opportunities to be realized sooner. iCatch Technology will also continue working with partners to promote safer, smarter, and more practical drone solutions, redefining the core value of next-generation intelligent flight.