{"id":1228,"date":"2026-06-15T01:20:44","date_gmt":"2026-06-15T01:20:44","guid":{"rendered":"https:\/\/kstories.kr\/en\/tec260608003-en\/"},"modified":"2026-06-15T12:38:34","modified_gmt":"2026-06-15T12:38:34","slug":"tec260608003-en","status":"publish","type":"post","link":"https:\/\/kstories.kr\/en\/tec260608003-en\/","title":{"rendered":"LG CNS Unveils Agentic AI Platform for Large-Scale IT System Automation, Advancing Beyond Traditional AI Coding"},"content":{"rendered":"
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LG CNS Unveils Agentic AI Platform for Large-Scale IT System Automation, Advancing Beyond Traditional AI Coding<\/div>\n

LG CNS has officially launched 'DevOn Agentic AIND' (AIND), an innovative Agentic AI-based development platform. Designed to automate the entire lifecycle of large-scale IT system construction and operation, AIND aims to significantly boost development productivity. It achieves this by moving beyond the limitations of existing natural language-based AI coding, employing specialized AI agents optimized for complex enterprise environments.<\/p>\n

Addressing the Challenges of AI Coding in Large-Scale IT Systems<\/h2>\n

While natural language-based AI coding methods, such as 'Vibe Coding,' have garnered recent attention, their utility for large-scale IT system development has shown significant limitations, primarily by being confined to mere code generation. A key issue arises from generating code without a comprehensive understanding of the enterprise system's structure and context, leading to potential conflicts with existing systems or modifications that unintentionally impact the entire codebase.<\/p>\n

In large-scale enterprise environments spanning finance, public services, and manufacturing, strict adherence to security regulations, development standards, and legacy system structures is paramount. Traditional AI coding methods have struggled to meet these complex requirements, making their application in real-world operational settings difficult. This has ultimately hindered development productivity and posed significant challenges to building high-quality systems.<\/p>\n

Core Features and Operational Mechanism of Agent-Based AIND<\/h2>\n

LG CNS's AIND platform streamlines the development process: users input requirements in natural language, after which specialized AI agents, each proficient in specific areas such as customer requirement analysis, design, coding, testing, and quality verification, organically collaborate to execute the entire development process end-to-end. This comprehensive approach is a culmination of LG CNS's extensive expertise gained from building and operating systems across diverse industries.<\/p>\n

For instance, if a financial company needs to add a new service to its existing core banking system, a user might simply input: 'Build a deposit and installment savings auto-transfer service linked to the account system.' An analysis and design agent would then interpret this to structure the system, while a coding agent would generate the necessary code, adhering strictly to the financial company's development standards. This allows developers to focus on reviewing and approving the results, drastically reducing development time.<\/p>\n

AIND's core competitive advantage lies in its 'Knowledge Foundation,' which integrates and analyzes all enterprise IT information essential for development. This foundation is an ontology database that structures vast corporate IT data\u2014including development standards, security regulations, system source code, and development artifacts\u2014in a way that AI can comprehend. Leveraging this, AIND learns the enterprise's systems and operations to perform highly customized development.<\/p>\n

Supporting 'Spec-Driven Development' and Legacy System Modernization<\/h2>\n

AIND incorporates 'Spec-Driven Development,' ensuring consistent quality throughout the process. By having AI perform design, coding, and verification based on predefined specifications, the platform delivers stable outcomes regardless of the user or developer, while minimizing 'hallucinations' and errors. This approach simultaneously enhances both the efficiency and convenience of system management.<\/p>\n

Furthermore, AIND's 'Legacy Modernization' feature allows systems to be transformed into structures optimized for modern technological environments, irrespective of their original development language. Specifically, it automates the conversion of systems developed in older languages like COBOL to Java, and also upgrades existing Java-based systems to align with the latest architectures and development standards. This capability drastically reduces code analysis, conversion, and verification tasks from weeks to mere minutes, thereby maximizing productivity.<\/p>\n

Collaboration with Cline and Global Market Expansion<\/h2>\n

LG CNS co-developed AIND with Cline, a U.S.-based global open-source AI coding company. Cline's AI coding agent gained recognition as one of the world's fastest-growing AI software, recording an astonishing 4,704% growth on the global open-source development platform GitHub. This strong technical synergy between the two companies was instrumental in enhancing AIND's sophistication and completeness.<\/p>\n

LG CNS and Cline plan to expand AIND's application across the United States, Japan, and Southeast Asia. They intend to actively deploy AIND in IT system construction and operation projects for global enterprises where security and regulatory compliance are critical, including sectors like finance, public services, manufacturing, and defense. This strategy aims to strengthen their position in the global market. Ahn Hyeon-jeong, Executive Director of Application Architecture at LG CNS, emphasized, 'By automating the construction and operation of large-scale IT systems with expert-level AI agents that understand enterprise systems, we will contribute to significant productivity innovation for our corporate clients.'<\/p>\n

English-Korean Version<\/a><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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