Enterprise adoption of GenAI: Why is Agentic Workflow more suitable than AI Agent?

In recent years, generative AI has become a major focus of enterprise transformation, with the term “AI Agent” (also known as an Autonomous AI Agent) among the most frequently mentioned. Many companies fall into a blind spot—believing that simply adding a reasoning and conversational agent on top of RAG technology and internal data can automate internal workflows, thereby overestimating the automation capability of a single agent. In reality, Proof-of-Concept (PoC) projects repeatedly stall, systems are difficult to integrate, and results are hard to replicate. While AI can converse, it doesn’t necessarily assist in decision-making or reliably drive internal processes.

The Challenges of AI Agents: Flexible but Hard to Govern

AI agents excel in flexibility, autonomous decision-making, and exploratory tasks, yet they pose challenges in governance and auditability. Their decision-making process is often a black box—untraceable and hard to pinpoint when errors occur. For organizations with strict compliance requirements and clear accountability, such “unauditable AI” becomes a risk. Moreover, implementing and maintaining agents is costly and resource-intensive. Each additional task requires retraining or redesign, limiting long-term scalability. As a result, most companies still rely heavily on manual review and knowledge updates after adopting AI agents, making it difficult for AI to become a true operational asset.

When Agentic Workflow makes control and transparency the key to enterprise AI.

Agentic Workflow was created to bridge this gap. It transforms AI from a standalone assistant into a stable and governable node within enterprise processes. Unlike single-task agents, a workflow decomposes operations into structured, traceable steps—from data retrieval and semantic comparison to regulatory search and final output—each of which can be monitored and verified. AI is no longer a black box but a transparent and auditable automation chain.

Agentic Workflow enables multiple AI agents to collaborate: one focuses on search, another on analysis, and a third on report generation. Each subtask cross-checks the others, improving accuracy and reliability. When expanding to new tasks, organizations simply add workflow nodes instead of rebuilding entire systems. Knowledge is accumulated, processes are reusable, and AI evolves alongside the business.

From Demo to Launch: A Success Story from the National Taiwan Bureau of Kaohsiung, Ministry of Finance

Taking Headquarter.ai as an example, the Agentic Workflow platform, built by Tongche Intelligence, allows AI to run entirely within the client's own AWS account, coupled with AI Guardrails and sensitive data control, ensuring the entire process is executed in a secure and auditable environment. With the assistance of a team of smart technology experts, the time from implementation to go-live is as short as six weeks, eliminating the need for redevelopment or modification of internal systems, allowing AI to immediately become a productive force. For example, the "Business Tax Assistant" implemented by the National Taiwan Bureau of Kaohsiung, Ministry of Finance, automatically breaks down tax cases into steps such as "extracting statements, comparing regulations, and generating preliminary answers," making AI reasoning traceable and auditable. After going live, the accuracy rate of responses reached 98%, the processing time was reduced by 90%, and the cost of AI was reduced by 85%, making it a representative case of government implementation of governable AI.

AI agents are excellent for rapid task initiation, whereas agentic workflows are designed for long-term, stable operation. For enterprises, the key to adopting generative AI lies not in making AI more talkative, but in enabling it to continuously perform—and perform correctly—within a governance framework. We believe the future of enterprise AI is not about choosing between agents or workflows, but embracing a hybrid collaborative model that combines flexibility with control.