Corporate use of AI is no longer limited to simple knowledge generation—it is expanding toward real execution capability. If generative AI produces answers and AI agents automate individual task units, agentic AI is execution-centric AI that understands the objective, autonomously plans the entire end-to-end workflow, and carries it through to completion. This shift began the moment AI acquired “tool-use capability,” and with the recent emergence of MCP (Model Context Protocol), the foundation for building agentic AI in a standardized way is being established rapidly.
The Era of Generative AI: The Limits of “Answering AI”
Generative AI has created immediate efficiency across many workflows—knowledge summarization, document drafting, research, and generating data-driven report drafts. However, generative AI is fundamentally optimized for text-based work such as “answering” and “generating.” In real operations, answering is only the beginning. It is followed by a sequence of execution steps such as loading data, cleaning it, analyzing it, validating results, saving files, and sharing outputs.
For example, analyzing the cause of declining revenue requires multiple steps, from accessing a database and selecting a model to documenting the findings. Generative AI can assist with some of these steps, but it cannot complete the entire process. This is why companies have come to need AI that can actually execute work—not just language models that generate text.
The Rise of AI Agents: The First Step Toward Task-Level Automation
To address this need, AI agents emerged. An AI agent is an AI module designed for a specific role or objective, connected to external systems to automate a limited scope of tasks. As capabilities such as API calls, file read/write operations, and simple prompt-chain orchestration became possible, AI agents offered one step higher practicality than generative AI.
For example, to fulfill the request “Extract and summarize only last week’s customer inquiry emails,” an agent can execute a full sequence of steps—calling an email API, filtering messages, analyzing content, and documenting the results. If generative AI is a tool for producing sentences, AI agents can be seen as an attempt to automate work at the task-unit level.
However, AI agents tend to be vulnerable to unexpected exceptions, have limited planning ability for long-running tasks, and struggle to integrate deeply with external systems.
Agentic AI: AI That Plans and Executes Autonomously
Agentic AI goes beyond the limitations of generative AI and AI agents by strengthening goal-driven capabilities to plan and execute an entire workflow. The key is that AI can define the problem on its own, establish a plan to solve it, and leverage external tools and systems to produce the desired final output.
Core Capabilities of Agentic AI
First is the capability to understand the goal and redefine the task. Instead of executing instructions literally, it prioritizes identifying what the user truly wants to achieve. The request “Analyze the causes of last quarter’s revenue decline” is not a simple question—it represents an entire data analysis workflow. Agentic AI redefines it as an end-to-end task.
Second is advanced planning capability. It breaks the task into detailed steps and designs the execution order. It autonomously constructs the full workflow—from data collection and model execution to validation and report generation.
Third is execution capability. Agentic AI can call necessary tools, access databases, create and store files, and adjust next steps based on results. It is not merely a text-generation model—it is AI that completes outcomes by operating systems.
How It Differs from Traditional AI Agents
Unlike conventional agents, agentic AI has autonomy and resilience. When exceptions occur, it can correct errors, revise its path, and replan the entire workflow in line with the objective. It can also call a much broader range of tools and integrates more deeply with external systems. If existing agents focus on workflow automation, agentic AI is closer to an autonomous operating system for achieving goals.
Generative AI / AI Agent / Agentic AI Comparison
| Category | Generative AI | AI Agent | Agentic AI |
|—|—|—|—|
| Core role | A model that answers questions and generates text | An execution module that automates specific task units | Autonomous AI that understands goals and plans and executes full tasks end-to-end |
| Scope of work | Text-based tasks such as writing, summarizing, and analysis | Limited automation such as API calls and file read/write | End-to-end execution including data access, tool invocation, file creation, and process adjustment |
| Planning ability | None | Limited (prompt-chain based) | Advanced planning to decompose tasks and design execution paths |
| Exception handling | Not possible | Limited handling possible | Self-correction and path redesign to recover from failures |
| Representative use cases | Report drafting, summarization, content generation | Email filtering, file organization automation | Full data analysis workflow automation, operational workflow automation |
Agentic AI Will Define Competitive Advantage After Generative AI
Agentic AI signals that AI technology has entered a new stage. Corporate competitiveness will depend on how quickly organizations transition to an agentic-AI-driven operating model. The impact is particularly immediate in industries where productivity, cost efficiency, and quality standardization matter.
Elice provides a programmable hands-on environment through EliceLXP, a learning and practice platform designed to help practitioners quickly learn agentic AI and apply it to real work. With EliceLXP, users can run code, process data, and experiment with models instantly from the browser—enabling educators and practitioners to conduct AI practice under the same conditions.
Educators can also choose third-party environments such as VSCode and Dify within EliceLXP. Dify is a platform that allows users to visually build LLM workflows and connect external tools and APIs, making it possible to create early versions of agentic AI. By running Dify directly in EliceLXP, teams can practice agentic AI development and quickly validate how it can be applied to real-world workflow automation.
Along with this practice environment, Elice supports companies in building an agentic-AI-based operating model by connecting automation design, training, and consulting tailored to each organization. Design your agentic AI adoption strategy with Elice today.
FAQ
How much does it cost to adopt agentic AI?
The cost varies widely depending on the model size, GPU infrastructure scale, scope of tool integration, and the level of connection to internal systems. In particular, customization differs by each company’s data formats and operating environment, so phased investment is more common than fixed pricing. Most companies start with a pilot and gradually expand.
Does agentic AI affect enterprise data security?
Because agentic AI connects to internal systems, security is a critical factor. Requirements can be addressed through methods such as data encryption, access control, logging and auditing, on-premise execution environments, and dedicated model operations. In particular, financial institutions and public organizations often prefer running agentic AI inside internal networks without external data transfer.
How can we assess whether our organization is ready to adopt agentic AI?
The key is data accessibility, system integration structure, and compliance with internal policies. If data is siloed or system connectivity is low, agentic AI cannot function effectively. It is also important to check the foundational technical environment—whether workflows are documented and whether API-based integration is possible.
Do employees need to develop new capabilities to use agentic AI?
A certain level of AI literacy, data understanding, and tool-use capability is required. As a result, many organizations implement literacy training, role-based AI utilization training, and operator training alongside agentic AI adoption.