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Author：Jeremy Huang

Definition of AI Agent

According to NVIDIA, an AI Agent is an advanced AI system capable of independently reasoning, planning, and executing complex tasks based on high-level goals. These agents represent a new generation of digital labor—working for us and alongside us. AI agents can automate repetitive and time-consuming tasks, while also serving as intelligent personal assistants to enhance productivity for individuals and organizations.

Unlike traditional generative AI, which typically follows a "user input, system response" model, AI agents possess autonomous collaboration capabilities. They can coordinate resources, interact with other agents, and flexibly utilize a variety of technologies and tools, such as large language models (LLMs), retrieval-augmented generation (RAG), vector databases, APIs, development frameworks, and advanced programming languages like Python.

This type of system is also referred to as Agentic AI, with its defining feature being the ability to achieve goals through iterative planning and decision-making. For instance, an AI agent tasked with building a website can autonomously handle layout design, write HTML and CSS, integrate backend processes, generate content, and perform debugging—virtually without human intervention.

Application Scenarios of AI Agents

The application potential of AI agents is nearly limitless, ranging from simple content generation to complex enterprise system integration and database management.

✅ Task Execution
Execution-oriented agents (e.g., API agents) can complete predefined tasks based on user instructions.
Example:
“Write a social media post to promote our latest product launch. Be sure to mention the new green variant and the current discount.”

✅ Workflow Optimization
Assist users in improving operational efficiency, such as providing recommendations or automating tasks within specific applications.
Example:
Using AI agents with the OODA (Observe, Orient, Decide, Act) loop strategy to optimize data center performance.

✅ Data Analysis
Leverage multi-agent systems to retrieve and analyze data, implementing a “retrieve and act” approach.
Example:
“How many quarters this year has the company maintained a positive cash flow?”

✅ Customer Service
Provide 24/7 voice or text-based customer support, with automated system integration to resolve issues.
Example:
A customer service agent automatically connects to a CRM system to check refund eligibility or submit return requests.

✅ Software Development Assistance
Support developers with code suggestions, bug fixes, pull request summaries, and even full code generation.
Example:
GitHub Copilot is a leading example of an AI agent that assists in coding, document management, and debugging.

✅ Supply Chain Management
Use multi-agent systems to analyze real-time data, automatically adjust inventory and procurement, and monitor market changes.
Example:
Build a multi-level agent architecture where each agent manages a specific part of the supply chain and reports back to a central agent for decision-making.

Global and U.S. Market Trends in AI Agent Adoption

IBM and PwC have recently released separate reports on the current state of AI agent adoption in enterprises. While the data varies significantly between the two, both reports indicate a growing trend in the integration of AI agent technology into business workflows.

According to IBM’s AI Projects to Profits report, published in June 2025 in collaboration with Oxford Economics, 2,900 executives worldwide were surveyed. The findings show that only about 3% of enterprise workflows are currently supported by AI agents, but this is expected to increase to 25% by the end of 2025. Furthermore, 70% of respondents believe Agentic AI will be strategically important to their organizations, and 83% expect AI agents to improve efficiency and output by 2026.

The report also states that AI investments currently account for 12% of IT budgets and are projected to grow to 20% by 2026. Currently, 64% of AI spending is allocated to core business functions. Among organizations adopting an “AI-first” strategy, 52% attributed revenue growth and 54% attributed operating margin improvement over the past year to AI initiatives. The top reported benefits of adopting AI agents include: improved decision-making (69%), cost reduction (67%), competitive advantage (47%), enhanced employee experience (44%), and talent retention (42%). However, concerns remain over data quality (49%), trust (46%), and skills shortages (42%).

In contrast, PwC’s AI Agent Survey, released in May 2025 and focused on the U.S. market, surveyed 308 senior executives. It found that 79% of companies have adopted some form of AI agent—17% have fully implemented them, 35% have adopted them extensively, and 27% are using them in limited areas. These agents are primarily used for routine tasks like data analysis, record updating, and responding to queries. A majority of respondents reported tangible benefits: 66% observed increased productivity, 57% reduced costs, and 55% faster decision-making.

Additionally, 88% of respondents expect to increase AI-related budgets in the coming year, with over half actively deploying AI agents in departments such as customer service (57%), marketing (54%), and cybersecurity (53%). However, only 45% of companies are undergoing full-scale operational restructuring, suggesting that the depth of adoption remains in progress. Key challenges to adoption include cybersecurity (34%), costs (34%), and trust issues (28%).

Overall, both reports suggest that AI agents are evolving from experimental tools into practical business solutions, though the degree of implementation and trust in the technology varies across regions, industries, and company sizes.

AI Agent Adoption Fuels Demand for Compute Power—Taiwan's Server Supply Chain Benefits

Compared to traditional AI applications, AI agents require significantly more computational resources due to their advanced reasoning, decision-making, and tool invocation processes. These systems, characterized by being goal-driven, self-planning, and interactive, often involve multi-step task decomposition, long-term memory management, and integration with external tools such as code editors or database queries. These operations not only demand large language models but also rely heavily on GPU-intensive logic processing and parallel execution capabilities.

As AI agent technologies proliferate, the global demand for high-performance computing (HPC) and data center infrastructure is expected to grow substantially. Governments and enterprises worldwide are ramping up investments in AI infrastructure, particularly in AI-optimized servers, GPU accelerators, liquid-cooling systems, and edge computing devices.

Taiwan, as a critical hub in the global AI server supply chain, continues to benefit from this wave of infrastructure upgrades. From upstream semiconductor manufacturing (e.g., TSMC’s advanced nodes for NVIDIA’s H100 and B200 chips), to midstream server design and assembly (led by companies such as Foxconn, Quanta, GIGABYTE, ASUS, and Pegatron), and downstream system integration and brand services, Taiwanese firms offer a comprehensive and highly flexible hardware ecosystem. As global cloud service providers (CSPs), hyperscalers, and emerging AI-focused nations continue placing large-scale orders, Taiwan’s supply chain is well-positioned for sustained growth and to reinforce its strategic role in the global AI ecosystem.

📌 Disclaimer

The content of this article is for informational and reference purposes only. It aims to explain the definition, application scenarios, market trends, and industry supply chain developments related to AI agents. The information presented is based on publicly available sources and commonly accepted industry knowledge. All analysis and interpretations are made to the best of the author's understanding at the time of writing and do not constitute any form of investment advice, technical guarantee, or business assurance.

Reference

What are AI Agents? | NVIDIA Glossary

IBM Study: Businesses View AI Agents as Essential, Not Just Experimental

PwC《AI代理調查報告》：近九成企業將增加AI預算組織變革與信任為關鍵