Agentic AI ② Driving Enterprise Automation with Agentic AI

The early stage of generative AI—where AI simply produced text—is already behind us. Enterprise AI adoption is shifting away from simple response generation toward AI systems that can plan and execute real business workflows autonomously. At the center of this transition is Agentic AI.

Agentic AI is a new type of execution-oriented AI system: it understands a goal, builds a plan, continuously interacts with external systems, and ultimately delivers the intended output. The recently introduced Model Context Protocol (MCP) makes this architecture more reliable to implement, significantly increasing the feasibility of adopting Agentic AI in real enterprise environments.

This shift is already being validated across multiple operational contexts. Elice is working with enterprises across a wide range of industries—designing Agentic AI-based automation architectures and delivering projects that connect directly into real production operations.

From Generative AI to Agentic AI: A Technical Evolution

Generative AI is effective at transforming input information into refined summaries or new text outputs. However, it is fundamentally limited to a single input → single output structure, which restricts its ability to interact continuously.

AI agents emerged next, combining ReAct-based reasoning with tool-calling capabilities, enabling limited automation through specific tools. Still, AI agents often lack the ability to plan and orchestrate entire workflows from a long-term perspective.

Agentic AI, by contrast, is designed to maintain long-term context around the goal and adapt its execution process when failures occur. Because Agentic AI assumes a functional composition of multiple system components, it proposes an entirely new technological paradigm—one capable of covering complex labor that used to require human execution.

→ Learn the differences between Generative AI vs AI Agents vs Agentic AI

Elice Consulting Case: Adopting Agentic AI in a Field Operations Organization

A large field operations organization at Telecommunications Company S—consulted by Elice—had to retrieve quality, process, and operations data from different systems. As a result, report creation and decision-making required significant time and manual effort.

In this environment, generative AI remained at the level of summarizing data. However, after applying an Agentic AI architecture, the organization was able to redesign the entire process around the business goal itself.

Data retrieval across systems, analysis, visualization, and report generation were planned and executed as a single continuous flow—minimizing human involvement to the final review stage.

How Agentic AI Works

Agentic AI is built as a complex system where multiple modules interact. The core is a planning module called the Planner, which interprets goals and converts them into executable steps.

The Planner analyzes the user’s goal, breaks it down into practical action units, and designs the overall workflow. To maintain and operate this plan, the system includes a layered memory structure consisting of short-term, mid-term, and long-term memory.
• Short-term memory stores information needed to resolve the current step.
• Mid-term memory preserves context across the working session.
• Long-term memory stores accumulated knowledge in the form of a vector database, producing an effect similar to learning over time.

In the execution phase, the Tool Executor connects to external systems to retrieve data, run code, generate documents, and manipulate databases. Outputs from these steps are validated by a Critic or Verifier module based on internal quality criteria. If errors are detected, the Planner reconstructs the path and adjusts the execution process so the system can complete the task end-to-end.

▲ Diagram: Agentic AI system architecture

Elice Consulting Case: Building Multi-Agent Systems in Manufacturing and Mobility

For Manufacturing/Mobility Company H, converting unstructured data into structured tables was a major bottleneck. Previously, humans had to manually inspect complex table structures one by one, repeatedly causing quality inconsistencies and resource drain.

To resolve this, Elice partnered with the company to design a multi-agent architecture by separating the roles of table conversion, validation, and correction. Through repeated feedback cycles between agents, the system automatically improved output quality over time.

In particular, by automatically detecting outputs that did not meet requirements, the system prevented error accumulation and significantly reduced the need for human intervention.

The Emergence of MCP and the Expansion of the Agentic AI Ecosystem

For Agentic AI to scale in practice, models must have interfaces that allow them to access external systems and execute tools. Traditional plugin-based approaches are often platform-dependent, limiting scalability and making them difficult to apply in enterprise environments.

MCP, a protocol that standardizes communication between models and external tools, can solve these limitations. Even across different operating systems or development environments, tools can be connected in the same way and execution structures remain consistent—allowing developers to integrate multiple capabilities through a single interface.

Ultimately, adopting MCP significantly reduces the cost of building Agentic AI systems. Enterprises no longer need to redefine all integration standards to connect internal business systems to AI. Instead, they can configure standardized MCP-based tool modules and rapidly apply them in production environments.

Applying Agentic AI in Enterprise Environments

Agentic AI is not limited to a specific industry or organizational type—it can deliver immediate impact across many real operational contexts.

Use cases include monthly performance report generation, code analysis and refactoring for development teams, test automation, and operational automation for MLOps/DevOps environments. The scope of application is extremely broad.

From Elice’s consulting experience, a consistent insight emerges: what matters most is not “which model to use,” but how to structure business goals and design them into an executable agentic system. Agentic AI delivers the highest impact when enterprises go beyond surface-level automation and restructure entire workflows around AI.

Elice’s AI Consulting Strategy Proven Through Real Operations

Elice provides practical adoption strategies tailored to each organization’s operational characteristics—based on proven experience designing Agentic AI systems in real enterprise environments, combined with high-performance infrastructure capabilities.

If your organization wants to shift from simply “adopting AI” to redefining how work gets done with AI, you need systematic design and real operational execution experience.

If you need a tailored strategy for adopting Agentic AI, design an executable Agentic AI environment with Elice.