Leveraging AI agents as tools to execute tasks designed by humans.
The use of AI agents depends more on the corporate IT environment and controllable operational structure than on the autonomy of the technology.
"AI agents" are gaining traction in Silicon Valley. AI agents are intelligent software systems that perceive their environment, reason, and autonomously execute tasks using external tools to achieve user goals. Unlike traditional generative AI, which merely answers questions or generates content, AI agents, upon receiving a goal, construct and execute the task steps themselves. This characteristic has led to a growing trend of companies adopting AI agents, particularly in areas that require repetitive yet critical judgment, such as schedule management and internal task support. Recently, Moltbook has attracted attention within this trend. This has sparked a growing discussion in Silicon Valley about the technical feasibility of AI agents and how they can be utilized in real-world business environments.
The debate over autonomy brought about by interactions between AI agents
Moltbook, mentioned earlier, is a prime example of popularizing the discussion of AI agents in the United States. Moltbook is a kind of "AI-only social media" platform designed for AI agents, not humans, to join, post, comment, and interact. Unlike traditional social media, humans do not directly participate. In other words, all conversations and responses within the platform are solely the result of interactions between AI agents. The platform's recent traction stems from the fact that conversations and discussions among AI agents maintain a consistent context, without human intervention. On Moltbook, AI agents go beyond simply responding to questions; they reference previous comments, expand on topics, and respond to each other's opinions. While "autonomous AI agents" have been primarily discussed theoretically or conceptually, Moltbook is being praised for providing a relatively intuitive glimpse into how AI agents might function in real-world settings.
This immediately sparked controversy. As the potential for AI agents' interactions to appear autonomous led to a debate over how to interpret and manage this autonomy, some viewed it as a natural consequence of technological advancement. Others pointed out that as AI agents gain greater autonomy, questions of accountability and controllability inevitably arise. Moltbook demonstrates the potential of AI agents, while also demonstrating in real time how greater autonomy increases the challenges humans must address alongside them.
AI Agents, this is how they're actually used in Silicon Valley.
What's interesting about the debate sparked by Moltbook is that Silicon Valley companies aren't immediately applying the autonomous interaction of AI agents to their business processes. While companies are focusing on AI's ability to converse and act independently, they're drawing a line at delegating decision-making authority and responsibility to AI. In an interview with KOTRA's Silicon Valley Trade Center, a Silicon Valley expert advising on corporate IT strategy explained, "In the corporate environment, the more important issue is not the AI's ability to make decisions and interact independently, but rather the scope of work within which it can be utilized." Therefore, companies are choosing not to develop AI agents as independent decision-makers, but rather to have them execute certain tasks within the scope of work designed by humans. McKinsey also defines AI agents as "software components that are given goals, decompose tasks, and carry out planning and execution," emphasizing the importance of design and management within the workflow.
The conceptual diagram above illustrates the four basic steps involved in processing a single task by an AI agent based on generative AI. ① A user requests a task in natural language. ② The AI agent system interprets this request, develops a work plan, and assigns roles. At this stage, a managerial agent coordinates the overall workflow, while agents responsible for analysis, planning, and review each perform their own tasks. ③ This organized result is delivered to the user, and ④ the process of reflecting user feedback to revise and supplement the results is repeated. In other words, rather than providing a single response, the AI agent performs a step-by-step process of planning, execution, and review.
The areas where Silicon Valley companies are applying AI agents are relatively clear: scheduling, research, organizing internal data, comparing suppliers, and drafting reports. These are repetitive and time-consuming tasks. While these tasks aren't simple and require no judgment, they don't require human intervention from start to finish. For example, in responding to customer inquiries, whereas existing generative AI simply drafted responses based on user requests, AI agents differ in that they continuously carry out a single process, from categorizing inquiries to checking customer history, executing follow-up actions, and notifying the right person. Therefore, Silicon Valley companies expect AI agents to handle these tasks, freeing up people to focus on more important decision-making and coordination.
This approach can be seen in real-world business cases. For example, SAP is applying Joule Agents, based on its generative AI, "Joule," to core business areas such as finance, procurement, and supply chain. Joule Agents operate within existing corporate business systems like SAP S/4HANA and Ariba, automating or assisting repetitive and time-consuming tasks such as supplier information collection, condition comparison, and data organization. However, SAP does not describe Joule Agents as autonomous decision-makers. Each agent operates only within a predefined scope, and final decision-making and approval stages remain the responsibility of humans. In other words, Joule Agents do not replace decisions; rather, they are tools that support human judgment and accelerate work.
Workato, a business automation platform company, is taking a similar approach. Workato's AI agents connect to internal corporate systems, such as CRM, ERP, and marketing tools, and execute user-defined workflows. When a customer inquiry is received, Workato collects and organizes relevant data, processes any necessary follow-up actions by linking to each system, and then relays the results to the appropriate agent. Throughout this process, the AI agent's access rights and scope are managed within the company's security policies and authorization system.
AI Under what conditions does an agent become a business tool?
The practical use of AI agents as business tools in Silicon Valley stems from the fact that, along with technological advancements, corporate IT environments have already become significantly more organized. Many companies operate core operations, such as finance and customer management, on digital systems like ERP and CRM, with relatively clearly defined work procedures, authorities, and data flows. In such environments, AI agents are more likely to be used as tools for performing repetitive and predictable tasks within human-defined boundaries, rather than emerging as new decision-makers. Ultimately, the observed use of AI agents in Silicon Valley can be seen as a result of judgments about how much work can be delegated within an organized IT environment and controllable operational structure, rather than the autonomy of the technology itself.
What Korean companies need to focus on now
The key takeaway from this discussion is clear. AI agents are no longer a distant future technology. Rather, they are entering a phase where some companies, particularly those focused on AI, are exploring their potential for productivity improvement, depending on how tasks are designed. Rather than adopting AI agents as a means of full-scale automation, Silicon Valley companies are leveraging them to offload repetitive and time-consuming tasks previously performed by humans. The key here is not the autonomy of the technology itself, but rather the degree to which it can generate tangible efficiency when task objectives, authority, and responsibilities are clearly defined. This trend suggests that Korean companies, too, need to view AI agents not from the perspective of "whether or not to adopt them," but rather from the perspective of "how to utilize them."