Three stages of AI agent implementation in the enterprise: RAG, Workflow, Agent

1. Agent and Agent

1. 1. Full marketing terms :
   Many companies may deliberately blur some technical terms when promoting products and package "workflow" into "intelligent bodies" to highlight the "intelligent" characteristics of the product. But in fact, workflows do not essentially have the ability to learn and make independent decisions.
2. 2. Functional crossover In some cases, some modules with intelligent features may be embedded in the workflow (such as AI-based recommendations or decisions), which makes it look more like an "agent". But overall, the core of the workflow is still to execute tasks according to rules rather than make independent decisions.
3. 3. Misunderstanding of the concept of agent In the process of technology dissemination at home and abroad, there may be misuse or expansion of the concept of "agents", resulting in some systems that do not have the core characteristics of agents being called "agents".

2. Why are these three directions

2.1 RAG: Foundation implementation

Features

• RAG is a technology that combines retrieval and generation, which can enhance the answering capabilities of language models by calling external knowledge bases or databases.
• RAG's main function is to solve the knowledge limitations of large models, enable them to access the latest information or domain-specific knowledge in real time, and generate more accurate and practical answers.

Reasons for landing

1. 1. 1. :

• RAG The implementation is relatively simple, and only requires combining a large language model (LLM) with a search system (such as vector databases, search engines).
• Many ready-made tools (such as diify, RAGflow, FastGPT, etc.) can quickly build a RAG system.
• 2. Clear requirements :
• In enterprise knowledge questions and answers, document search, customer support and other scenarios, users need accurate and real-time information, and RAG can meet these needs well.
• For example, for professional document Q&A in the fields of law, medical care, RAG can provide reliable answers by searching authoritative data sources.
• 3. 3. :
• RAG has already been widely used in the fields of enterprise document management, customer service, education, etc., with clear market demand and easy to verify ROI.

Summary

RAG is the basis for agent applications because it directly improves the practicality of the large language model, and its technical implementation and commercial value are relatively clear.

2.2 Workflow (workflow): middle-level landing

Features

• Workflow refers to the automated processing of complex business processes in the form of a fixed code flow, such as approval, data processing, task allocation, etc.
• It usually involves the collaboration of multiple steps and may require calling different APIs, databases, or other tools.

Reasons for landing

1. 1. 1. :
• Workflow RAG is more complex because it requires concatenating multiple tasks and ensuring logical and reliable execution.
• For example, automated order processing may involve multiple steps such as inventory inquiry, payment verification, logistics arrangement, etc.
• 2. 企业需求驱动 ：
• There are a large number of repetitive and regular tasks that need automation in enterprises, and agents can significantly improve efficiency in workflow automation.
• For example, in scenarios such as financial statement generation and contract approval process, the agent can assist in completing it.
• 3. The tool chain is gradually mature :
• As RAG and other technologies become popular, many toolchains (such as Dify, Coze, n8n, etc.) can already support complex workflow automation.

Summary

Workflow is an intermediate stage in the application of agents, because it requires technical integration of more modules, while market demand is gradually shifting from single tasks (such as information retrieval) to automation of complex tasks.

2.3 Agent: Advanced landing

1. Features

• Agent is a more complex and intelligent application form that can independently perceive the environment, plan tasks and execute operations, and even make decisions in an uncertain environment.
• Agists usually need to combine multiple technologies (such as reinforcement learning, dynamic programming) to achieve high-level intelligent behavior.

2. Reasons for landing

• • Agent The implementation requires stronger technical support, including task planning, context understanding, multimodal interaction, etc.
  • It not only requires calling external tools, but also requires dynamic adjustment and adaptability in complex scenarios.
  • Agents in vertical scenarios often require fine-tuning, which is a huge time cost, labor and computing cost for general enterprises.
• Agent There is huge potential, but the current market demand is still focused on clearer tasks (such as RAG and Workflow).
• In some high-complexity scenarios (and most of the B-end scenarios are such complex and low-tolerance scenarios), the Agent's ability is still being explored.

• Agents often require RAG and Workflow-based. RAG provides knowledge support, Workflow provides task execution framework, and Agent achieves higher-level intelligence on this basis.
• For example, an intelligent customer service agent may need to first acquire knowledge through RAG, and then complete the specific process of user requests through Workflow, and finally achieve the autonomy of the entire process.

• Summary

  Agent is an advanced stage of agent application, and its implementation requires higher technical support and more mature market demand.

  
  

  
  

  Why is RAG → Workflow → Agent?

  1. Technical complexity gradually increases :

  • From RAG to Workflow, to Agent, the complexity of technology implementation is gradually increasing, so it needs to be implemented in stages.:
• • The market demand for agents has gradually expanded from a single task (information acquisition) to complex tasks (process automation) and comprehensive intelligence (autonomous decision-making).
• Ecosystem gradually improved :

   

  • The development of RAG and Workflow provides the technical and application foundation for the Agent, making the implementation of the Agent possible.

• This phased development path is not only in line with the logic of technological development, but also in line with the evolutionary laws of market demand.

   

  3. The other side of the enterprise implementation

   

  In fact, current enterprises are often moving from Workflow to RAG

  RAG It is easy to implement, but it is difficult to do well, and it is difficult to achieve high accuracy.

   

   

  3.1 RAG Why is the threshold for enterprise application higher?

  Data quality and knowledge base construction

  • The core of enterprise-level RAG is the construction and maintenance of a "high-quality knowledge base". This requires:
  • Data collection and organization : Enterprises need to clean, classify and structure internal documents, databases, logs and other data.
  • Knowledge Base Update and Management : The knowledge base needs to be updated in real time to ensure the accuracy and timeliness of information.
  • Privacy and Security : Enterprise data usually involves sensitive information. How to protect privacy and meet compliance requirements during the retrieval process (such as departmental authority, personnel authority) is a high threshold.

  Scene Complexity

  • RAG scenarios in enterprise applications are usually very complex. For example:
  • In the field of law or medical care, RAGs need to retrieve authoritative documents and generate high-precision answers, and the cost of errors is extremely high.
  • In the financial field, RAGs may need to process dynamic data (such as stock markets) in real time and generate predictions in combination with historical data.

  Technical complexity

  • The implementation of RAG requires the integration of multiple technologies:
  • Vector search : It is necessary to build an efficient vector database and optimize query performance. Even professional teams (such as data engineers, NLP experts) perform system development and optimization.
  • Multimodal data support : Enterprise data may be in multiple formats such as text, tables, pictures, and videos. The RAG system needs to support multimodal retrieval and generation.
  • Model fine-tuning : In order to meet the specific needs of the enterprise, RAGs often need to fine-tune the large language model to increase their domain adaptability.

   

  3.2 Why is the enterprise application of Workflow relatively simple?

  Clear rules and procedures

  • The core of Workflow is to automate existing business processes in the enterprise, which are usually clear and regular. For example:
  • Automatic approval process: From form submission to approval approval, clear logic.
  • Data processing flow: regularly extract data from the database and generate reports.
  • Because these processes are highly regular, the development difficulty is relatively low.

  Tool ecology mature

  • There are many mature Workflow construction tools on the market, whether it is commercial products or open source frameworks:
  • Low code/no code platform : Such as Dify, Coze, n8n, it can enable enterprises to quickly build automated processes.
  • API Integration Tool : By calling off-the-shelf APIs, enterprises can easily achieve workflow connections across systems.
  • Modular design : Workflow systems are usually modular, and enterprises can select and combine functions according to their needs.

  The threshold for technical implementation is low

  • Compared with RAG, Workflow's technical implementation focuses more on logical orchestration and system integration:
  • No complex model training is required, just integrate existing tools and systems.
  • Data processing requirements are usually structured and have fewer technical challenges.

  Quick Verification ROI

  • Workflow The effects of automation are easy to quantify: reduce manual operation time, improve efficiency, reduce error rate, etc. This makes it easier for companies to verify their return on investment (ROI), thereby speeding up their landing.

   

  
  

  Although RAG is the basis of agent applications from the perspective of technological development path,

• Companies prefer to start with Workflow because it has a low threshold and fast effect, while RAG is suitable for in-depth application in specific scenarios.

   

• Efficiency improvement can be achieved through Workflow automation, and then RAG technology can be introduced to enhance the capabilities of knowledge-based tasks.