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AI Agent RPA
AI
Agent RPA
The enterprise AI trend is moving toward the agent era. Beyond intelligently responding chatbots and AI assistants, the scope of enterprise AI is expanding to include AI agents that handle tasks autonomously, and multi-AI agents that collaborate with various tools across multiple specialized tasks. Based on LLM, MMLM, and RAG, Daewon CTS proposes a mid- to long-term plan to maximize autonomy and collaboration by connecting AI agents with various tools, such as RPA.
Infrastructure and
Unifying the platform
AI agents can leverage a variety of tools, much like experts, to work more productively and creatively. The integration of AI agents with RPA, a tool that has become a core component of corporate task automation, is attracting attention as a highly effective implementation scenario. However, this approach cannot be implemented from scratch. The approach should begin with a simple chatbot and then evolve to enhance the AI environment with AI agents.
Challenge
Key challenges
DIA NEXUS focuses on.
Technology integration.
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Integrating AI agents with existing systems presents challenges such as API integration, security, and data format transformation.
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If integration platforms or interfaces are insufficient, they can become technical debt, limiting future scalability and expansion.
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Because AI agents access diverse data, strong security frameworks—such as access control, encryption, and data protection—are required, but existing security tools have limited effectiveness in controlling AI agents.
AI agent and tool integration.
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When using RPA as a tool integrated with AI agents, data formats and API invocation methods must be standardized.
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Because the integration methods between AI agents and RPA differ by vendor, enterprises may need to develop their own communication modules.
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Although RPA platforms provide capabilities to invoke AI models, complex scenarios require the implementation of AI agent orchestration logic, which significantly increases integration complexity.
Decomposition of complex tasks and AI agent role design.
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When implementing a multi-agent AI system, it is difficult to properly decompose work and define each agent’s role and scope.
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If the design is flawed, conflicts or gaps between agents can occur, reducing overall efficiency.
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Even a single agent can be complex; enabling multiple agents to collaborate effectively is significantly more difficult.
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Key challenges include task allocation, interaction methods (protocols), and shared memory/context management.
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Reward design and control mechanisms are needed to ensure the overall system operates in the intended direction
Operations and governance framework.
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To operate AI agents and RPA at scale, a governance framework for continuous monitoring and control is essential.
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Similar to the “bot sprawl” phenomenon seen in the early stages of RPA adoption, unplanned deployment of AI agents can lead to issues such as duplicated development, version inconsistencies, and unclear accountability.
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Centralized registration and management of agents are required, along with controls over access permissions and updates.
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Many enterprises face challenges due to a lack of specialized governance personnel and expertise.
Service
Optimization services
DIA NEXUS focuses on.
AI agent platform design
and POC.
We design AI infrastructure, platforms, and agent architectures tailored to customer workflows, provide pilot projects (PoCs) to validate feasibility, and present a development roadmap.
Business analysis–based
AI agent design.
We break down enterprise workflows in detail, redesign automation and human roles, define AI agent functions and responsibilities based on the analysis, and propose integration approaches between AI agents and RPA.
Enterprise-wide integration and internalization support.
We propose strategies and governance frameworks to scale AI and RPA convergence across the enterprise.