The final paradigm of enterprise AI agents: feeding enterprises into the big model
The ultimate form of enterprise AI agent: "feeding" the entire enterprise to the big model to create a true business intelligence agent.
Core content:
1. How the enterprise agent makes up for the limitations of LLM through four key modules
2. Analysis of the five-layer structure of the enterprise AI agent
3. The paradigm shift from "using AI" to "becoming AI"
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Building an enterprise AI agent based on LLM is a hard job of providing LLM scaffolding, adding plans, tool use and memory to LLM, and then feeding it with enough enterprise knowledge. All of this is to solve the problem that LLM context is limited and only one path can be activated at a time.
To transform the public domain cognitive intelligence of the big language model into the business intelligence of the enterprise, it is necessary to combine the business and intellectualize the internal information of the enterprise as the LLM context, and then use process behavior as the hands and feet, and finally deliver it in the form of AI Agent services within the enterprise:
Endgame thinking An enterprise that feeds all information to the big model is a big o3-ultra!
#artificial intelligence #language intelligence #enterprise intelligence #business intelligence #information knowledge #knowledge graph
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How to transform LLM’s general language intelligence into the company’s exclusive business intelligence?
The answer is emerging - building an enterprise-level AI agent .
It seems to be a technology implementation project, but in fact it is a paradigm shift. We are not just deploying a tool, but building a "cognitive operating system" for enterprises , making the enterprise itself an intelligent structure with generation, memory and action capabilities .
2. The hard work of “building scaffolding” is actually a project of “replenishing the brain and planting the soul”
To put it simply, the process of building an enterprise intelligent entity is to add four key supports to LLM:
Modules | Purpose | nature |
|---|---|---|
Plan | Task decomposition and multi-step reasoning | Give a sense of purpose |
Tool Use | External system linkage and action capabilities | Empower execution |
Memory | Long-term knowledge and multi-turn context | Building cognitive continuity |
Enterprise Knowledge Feeding | Structuring of private domain knowledge | Shaping the world model |
What you see is "scaffolding", which essentially does one thing:
Make up for LLM's "context myopia" and "behavioral fragmentation" and build an enterprise intelligent entity that can continuously recognize and interact.
So, this is a project that seems like hard work but is actually subversive: translating LLM unstructured intelligence into enterprise-level structured intelligent entities.
3. The five-layer structure of enterprise intelligence: creating a "big model embedded in the enterprise"
We can understand a mature enterprise intelligent entity as a "customized large model activated by private domain knowledge and behavior."
The five-layer structure of Enterprise AI Agent is as follows:
Information input layer: semanticize and knowledge all enterprise information (systems, processes, documents, data).
Context fusion layer: Build the RAG system, knowledge graph and vector database as long memory support.
Behavior execution layer: encapsulates business processes into callable tool chains (API, process automation, RPA, etc.).
Intelligent activation layer: deploy multi-role agents based on LLM (customer service, sales, approval, etc.).
Delivery service layer: actual delivery and embedding are completed through Bot, workflow, and business portal.
Finally, a "cognitive engine within the enterprise" is formed:
A "digital agent" that can understand semantics, master processes, continuously learn, and act on behalf of the enterprise.
4. Paradigm Insight: Enterprises do not use AI, but become AI
End-game thinking: An enterprise feeds all information into a big model, which is a big o3-ultra.
This sentence may sound like a joke, but it is actually a profound cognitive leap. It points to an essential trend:
The ultimate form of enterprise digitalization is not a collection of process systems, but an embodied cognitive system, an enterprise agent entity that can self-generate, schedule and make decisions.
Just like the human brain center does not survive on a set of process charts, but on a continuously generated cognitive flow - the enterprise of the future will no longer be the sum of processes, but an activated large model intelligent entity.
V. Summary: From “Using Models to Serve Enterprises” to “Enterprises Becoming Intelligent Entities”
From deploying AI tools, to building enterprise agents, to enterprise ontology intelligence , this is a paradigm migration path:
LLM as a cognitive engine
Knowledge structuring and process behavior linkage
Multi-role Agents form enterprise task flow
The enterprise as a whole is mapped as a continuously running generative intelligent entity
This is not only a way to implement AI, but also the future evolution direction of the enterprise itself .
In the era of big models, every enterprise will have the opportunity to have its own "enterprise GPT". And ultimately - the enterprise itself is the GPT.
