AI
AI Native DevelopmentAI-based development is evolving from AI assistants that support developers to Agentic AI, which can make decisions, plan, and execute tasks on its own, and ultimately to Autonomous AI, which can independently carry out the end-to-end development process. Early AI development tools were largely limited to supporting developers with repetitive tasks, such as code completion, refactoring, search, and basic code generation.
At this stage, AI was used as an assistive tool within Integrated Development Environments (IDEs), with a focus on improving developer productivity and accelerating the development process. AI then evolved into a prompt-based, conversational approach to development. Developers describe requirements in natural language, and AI generates code, fixes errors, writes test code, and suggests design ideas. Although AI began to take a direct role in development, the process still depended heavily on developers’ instructions and judgment.
Recently, development approaches powered by Agentic AI have been gaining attention, with AI understanding goals and system context, then autonomously breaking down, planning, and executing the required tasks. In enterprise environments in particular, this is evolving into spec-driven development—an approach that structures business knowledge, code knowledge, development standards, and project deliverables into a knowledge foundation, then automatically generates business, technical, and development specifications based on that foundation.
DevOn Agentic AIND(AI Native Development)s designed to bring this shift in the AI development paradigm into real-world enterprise environments. Drawing on the knowledge foundation, where business knowledge, code knowledge, development standards, and project deliverables are systematically structured, AI agents take on and execute key roles across analysis, design, development, and validation. Developers can move away from manually handling every task and instead focus on high-value tasks such as reviewing requirements, assessing quality, responding to exceptions, and making final decisions.
The spec-driven development approach that DevOn Agentic AIND aims to realize transforms the very way enterprises structure software development. By enabling AI to execute the development process consistently based on standardized specifications, rather than relying on individual capabilities or experience, organizations can reduce quality gaps among team members and improve project predictability. As routine implementation work is automated, development cycles are accelerated, and accumulated knowledge assets can be continuously reused in future projects.
This represents a tangible shift that goes beyond improving development productivity. It helps enterprises establish a standardized development model, secure consistent quality, strengthen operational stability, and improve project outcomes. DevOn Agentic AIND is a concrete first step for enterprises preparing to transition to an autonomous AI-powered development model.
End-to-End Expert Agents Across the Entire Software Development Life Cycle
In enterprise software development, AI is still often applied only to limited stages of the process. As processes—from requirements analysis and design to development, testing, deployment, and operations—are handled in silos, recurring bottlenecks and quality issues persist. AIND provides end-to-end AI agents that connect all processes across the Software Development Life Cycle (SDLC). Built on LG CNS’s experience in SI/SM projects and hands-on field expertise, these agents go beyond simple development support tools to provide expert AI agent capabilities that can be put to work immediately in real-world projects. From system implementation to operations, AIND continuously delivers consistent, high-quality outputs across the entire development process.
Knowledge Foundation-Based Understanding of the Business, Not Just the Code
One of the key risks in enterprise systems is that business knowledge and business logic often remain undocumented and depend on the tacit knowledge of experienced developers. This creates high onboarding costs when new team members join and poses the risk of business knowledge being lost when key personnel change roles or leave the organization. AIND’s knowledge foundation automatically analyzes and structures not only source code but also business logic, turning them into organizational knowledge assets. This enables new developers and newly assigned team members to quickly understand the business context and perform their work effectively. Furthermore, built on a self-improving architecture in which agents continuously accumulate and refine knowledge on their own, AIND can keep improving the accuracy and quality of task execution as it is used over time.
Enterprise-Ready, Governable AI Agents
When adopting AI, enterprises are most concerned about whether outputs can remain consistent and whether AI can be properly controlled. In development environments that rely on individual capabilities, quality gaps can easily arise, and it becomes increasingly difficult to maintain consistent standards and processes as organizations scale. AIND delivers consistent, high-quality outputs based on development standards, specifications, and processes, regardless of organizational or team size. Furthermore, with multi-agent collaboration and parallel processing capabilities, AIND can efficiently handle even complex legacy systems and provides the foundation for operating AI as a controllable and governable tool—not a black box.
The legacy transformation solution is an intelligent SI development solution in which AI agents analyze the structure and business logic of existing legacy systems. It supports the full transformation project lifecycle, from identifying modernization targets and designing architecture transitions to code conversion, test automation, and data and system migration. By doing so, the solution reduces risks such as analysis gaps, quality degradation, and schedule delays that can occur during complex legacy transformation projects, while improving the productivity and stability of next-generation system implement.
Captures business logic, code structures, data flows, interfaces, and change targets from As-Is and To-Be source code
Defines business functions, processes, and business rules based on As-Is code knowledge and business information
Consolidates business and technical specifications with change requirements into development specifications
Generates source code for new development, system transformation, and feature implementation based on development specifications
The continuous operation solution is an intelligent SM operation solution in which AI agents continuously support live system operation, from root-cause analysis of incidents and log- and source-based impact analysis to maintenance and enhancement work, testing, quality checks, and pre- and post-deployment validation. By doing so, the solution automates repetitive operational tasks, accelerates incident response and change handling, helps maintain service quality, and supports continuous system improvement and enhancement.
Analyzes operational source code and captures code structures, business logic, impact scope, and change targets in the knowledge foundation
Analyzes operational requests, such as CSR, SR, and JIRA, to identify the purpose of the request, change scope, and impact factors
Generates SR Specs for maintenance and enhancement based on SR analysis, code impact analysis, and technical specifications
Generates source code required for operational changes, feature enhancements, and bug fixes based on SR Specs
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Adopting DevOn Agentic AIND begins with an introductory briefing that helps customers understand the solution concept, SI/SM platform architecture, core agents, applicable use cases, and expected benefits. During this process, LG CNS analyzes the client’s development environment and current SI/SM work, and works with the client to review applicable areas and priorities. Once the need for adoption is confirmed after the briefing, selected tasks or parts of the target system are used for a Proof of Concept (PoC) to validate the solution’s real-world impact, including building the knowledge foundation, generating specifications, and generating code. Based on the PoC results, the solution is introduced into actual projects in phases, in alignment with the client’s development standards, deliverable framework, configuration management, and quality assurance processes.
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DevOn Agentic AIND is not just a code generation tool. It is designed to enable agents to carry out the development workflow step by step, based on the knowledge foundation and a spec-driven development framework. When requirements or SRs are entered, the system references relevant code knowledge and standards, generates the required specifications, and the development agent carries the process through to implementation. This enables AI to independently structure and execute development tasks.
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DevOn Agentic AIND does not require companies to simply replace their existing development processes. Instead, it helps reconfigure the workflow around AI agents, from requirements analysis and specification generation to code generation and validation. Therefore, it can be adopted in phases in alignment with the existing SI/SM delivery model, deliverable templates, development standards, configuration management, and quality assurance procedures. The solution can first be applied to specific business areas or modules, and the knowledge foundation and spec-driven development framework can then be expanded.
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DevOn Agentic AIND is not designed for a single AI to handle everything. Instead, it is structured so that role-specific agents work in coordination across the development workflow. This connects the process from analysis to specification generation and code generation, allowing developers to shift their role from performing detailed tasks to reviewing outputs and making key decisions. This is a core operating model for transitioning to an autonomous AI-powered development approach.
