How Agents and Knowledge Bases Enable AI to Truly Work for You

You may have experienced this: when chatting with AI, it can help you write poems, generate code, or even explain complex concepts like a teacher — its intelligence can be astonishing. But when you ask it for your company’s latest sales data, or to book a meeting room, it suddenly seems to “forget” everything and cannot respond. Why does this happen?

The reason is that most AI models do not directly access your proprietary information; they mostly rely on general knowledge learned from training data. To enable AI to truly “get things done,” it requires two key components: Agents and Knowledge Bases. The agent serves as AI’s “action brain,” capable of understanding instructions, invoking tools, and executing tasks; the knowledge base acts as AI’s “memory repository,” storing enterprise data, documents, and rules so the AI can access and utilize up-to-date information.

Agents: AI’s “Brain and Hands”

If an AI model is a “brain” with vast knowledge, then the agent is the commander that makes this brain actually “move.” It not only understands instructions but can also break a complex task into multiple actionable steps and determine how each step should be executed.

The agent’s core ability lies in decision-making and action. Its capability to truly “move” comes from invoking various tools, which can be categorized as follows:

1. Information Retrieval Tools

   • Web Browsers: such as Chrome or Edge, used for real-time search and data retrieval.
   • Database Queries: such as SQL databases or Notion databases, helping the agent access internal company information.
   • News/Document Sources: such as RSS feeds, Google News, or corporate document libraries, providing instant reference materials.

2. Productivity and Collaboration Tools

   • Calendar Apps: Google Calendar, Outlook Calendar, for scheduling meetings and reminders.
   • Email Clients: Gmail, Outlook, for sending notifications or reports.
   • Team Collaboration Platforms: Slack, Teams, Trello, for task assignment or project updates.

3. Data Processing and Analysis Tools

   • Spreadsheets: Excel, Google Sheets, for calculations, organizing, and visualizing data.
   • BI Tools: Tableau, Power BI, for generating reports or data visualizations.

4. Execution and Automation Tools

   • Scripts and API Calls: Python scripts, Zapier, Make, for automating tasks and cross-platform operations.
   • Hardware Interfaces: smart home devices, printers, robots, enabling AI to interact with the physical world.

With these capabilities, agents transform AI from a “talking robot” into an assistant that can truly interact with the external world and complete tasks. They are the key bridge for AI to move from “can chat” to “can act.”

Knowledge Bases: AI’s “Memory and Experience”

If agents are AI’s “brain and hands,” then knowledge bases are its “memory center.” They store vast, precise information and are crucial for the agent when executing tasks. Knowledge bases address two major limitations of large models:

1. Knowledge Timeliness Training data in large models usually has a cutoff date, meaning the model may not know the latest market data, news, or product updates. Knowledge bases can be updated in real-time, allowing AI to access the most current information and make more accurate decisions.

2. Private Data Large models cannot directly access internal company documents or personal notes. Knowledge bases can store these private materials, enabling the agent to securely retrieve them as needed, supporting personalized and enterprise-specific applications.

Technically, knowledge bases are often used together with RAG (Retrieval-Augmented Generation). RAG is a technique that combines information retrieval with generation models. Its core idea is that before generating an answer, the system first retrieves relevant information from external knowledge bases or documents, and then uses that information to generate a more accurate and context-aware response.

RAG involves three main steps:

1. Understanding and Vectorization

   • AI converts the user’s question or instruction into a vector representation (embedding) for matching with document vectors in the knowledge base.

2. Information Retrieval

   • The system retrieves the most relevant documents or data fragments from the knowledge base based on vector similarity.
   • Methods like vector search or BM25 are often used to ensure high relevance.

3. Answer Generation

   • The retrieved content is provided as context to the language model, which generates a response based on both the instruction and the retrieved information.
   • The output combines the model’s language capabilities with the latest or proprietary knowledge.

For example, if you ask AI to write a “quarterly sales report summary,” without a knowledge base, it can only generate generic content from its training data, which may not reflect your company’s actual figures. With RAG, the agent first retrieves the latest sales data from internal databases or document repositories and then generates the report based on that data. This gives AI “real-time access to information,” producing answers that are both accurate and specific.

Through knowledge bases, AI no longer remains a general-purpose knowledge repository; it gains its own “personal memory.” Its answers become more precise and can handle proprietary company information, upgrading AI from “can chat” to “can understand, act, and remember.”

Integrated Products: When “Brain” Meets “Memory”

With AI technology advancing, integrated products that deeply combine agents and knowledge bases are now available. These products not only provide storage but also embed the agent, allowing users to interact directly with AI without complex setup.

Based on the source of the knowledge base, these products can be roughly divided into two types:

1. Private Knowledge Bases Products like NotebookLM and Tencent IMA allow you to upload personal or corporate documents, which then become the knowledge base. The embedded agent can understand instructions like “summarize this” or “answer questions” and directly analyze and generate responses from the documents, enabling true “instant Q&A.”

2. Real-Time Dynamic Knowledge Bases Products like Perplexity feature real-time, dynamic knowledge bases. When a question is asked, the agent instantly queries search engines, retrieves the latest information from the web, and generates an answer. This ensures AI can handle breaking news, real-time data, or new product information immediately.

These integrated products make the “agent + knowledge base” combination accessible, greatly lowering the barrier for individuals and enterprises to use AI. AI is no longer just a “chatting tool” but a smart assistant that can solve real problems and support decision-making.

Collaborative Evolution: Making the “Brain” and “Memory” Work for You

Agents and knowledge bases are powerful on their own, but when they work together, AI’s capabilities are amplified, allowing it to truly “get things done.” Consider the example of having AI write an internal quarterly report. The workflow is as follows:

1. Step 1: Understanding and Planning (Agent) Once the task is assigned, the agent first interprets your instructions and plans. It determines: “What information do I need? Which data and documents are key? What steps are required to complete the report?” This is the AI’s “brainstorming” stage, formulating a clear action plan.

2. Step 2: Retrieval and Acquisition (Knowledge Base) With a plan in place, the agent retrieves relevant files and data from the knowledge base. It may consult sales spreadsheets, product progress documents, or internal meeting notes to ensure the information is comprehensive and reliable. This step gives AI “memory,” granting access to the latest and private materials.

3. Step 3: Integration and Generation (Agent) After gathering the necessary information, the agent integrates and analyzes the data, organizing scattered content into a logically structured report. The final output accurately reflects company status and meets your expression requirements, achieving the goal of a “task-completing” AI.

Through this collaborative mechanism, agents and knowledge bases act as a perfect combination of “brain” and “memory”: the agent makes decisions and executes tasks, while the knowledge base provides information and experience. Together, they enable AI not only to answer questions but also to proactively complete complex tasks, becoming a true intelligent assistant.