How to Build a Knowledge Base AI Agent

Sectors such as finance, education, and legal, often require workers to spend hours sifting through documents. Thankfully, with the advent of LLMs, this process of document analysis is now much faster.

However, LLMs are trained on broad data sets that are not specialized for specific domains. This is where the Knowledge Base comes in. 

A Knowledge Base allows teams to curate a set of documents for LLMs to pull information from. These documents are typically more detailed, niche, and up-to-date compared to the LLM’s training data.Knowledge Bases enhance the capabilities, accuracy, and reliability of LLMs. 

In the following blog, we’ll show you how to build a Knowledge Base Agent in StackAI, so you can harness the full power of LLMs.

Knowledge Workers: Analyzing Documents is Difficult & Expensive

Sifting through large quantities of documents is both difficult and expensive because it demands significant time, labor, and cognitive effort. 

Manually reviewing large volumes of content — whether legal contracts, technical manuals, research papers, or internal reports — requires skilled professionals who can interpret complex language, extract relevant information, and ensure accuracy. 

This process is not only time-consuming but also prone to human error, especially under tight deadlines or when dealing with dense, repetitive, or unstructured text. Moreover, scaling this task across massive datasets often means hiring additional staff or investing in custom tooling, which adds to operational costs. 

As organizations generate and accumulate more data than ever before, traditional methods of document review simply can't keep up—making the case for AI-powered solutions stronger than ever.

LLMs: Training Data Has Blind Spots

However, LLMs are not inherently good at answering domain-specific questions because they are trained on broad, general-purpose data rather than specialized or proprietary content. 

While they can generate fluent and convincing text, their knowledge of niche topics—such as medical diagnostics, legal regulations, or technical engineering details—is often shallow or outdated. 

They may lack access to the most current or accurate information within a specific field, and they are prone to "hallucinating" plausible-sounding but incorrect answers when faced with unfamiliar or ambiguous queries. Additionally, domain-specific language often includes specialized terminology, subtle nuances, or structured data formats that LLMs can misinterpret without proper grounding. 

To reliably answer expert-level questions, LLMs need to be augmented with up-to-date, domain-relevant knowledge bases or integrated with retrieval systems that provide context from trusted sources.

AI Agents: How They’re Improving the Situation

AI agents are improving the limitations of LLMs in domain-specific tasks by combining reasoning abilities with access to external tools, knowledge bases, and workflows. 

Unlike standalone LLMs, AI agents can retrieve relevant documents, execute queries, cross-reference data, and even take actions based on domain-specific rules or instructions. This makes them more reliable for tasks that require accuracy, depth, and real-time information.

For example, in a legal setting, an AI agent can search a firm’s contract repository, extract relevant clauses, compare language across documents, and summarize risks—something a generic LLM would struggle with. In healthcare, agents can pull the latest clinical guidelines or patient data to assist with decision-making while maintaining compliance. 

By orchestrating tasks across tools, databases, and documents, AI agents reduce human error, improve speed, and provide traceable, grounded responses—making them a practical solution for deploying AI in expert domains.

Knowledge Base Agent: How to Build

The following step-by-step walkthrough will show you how to build the Knowledge Base Agent in Stack AI. 

As a first step, make sure to sign up for a free StackAI account. Navigate to the account dashboard. Click ‘New Project’.

Click the ‘Workflow Builder’ option.

From here, choose the Knowledge Base Agent. 

This will launch a pre-built workflow for the Knowledge Base Agent. 

Let’s go through each component of the AI agent. First, we have the input box. This is where you can ask questions you want answered. 

The next component is the Knowledge Base. This is a documents knowledge base, drawing from user uploaded documents. 

This allows you to upload and search a list of documents. That improves the response of the LLM. 

Click on the node. Then upload the documents. In this example, we’ll upload several financial documents. 

You can also leverage Knowledge Bases that are already saved in StackAI.

The next component in the workflow is the LLM. The LLM is Anthropic - Claude 3.5 Sonnet. The instructions for the LLM are as follows:

You are an AI that answers questions over a knowledge base.
- On each message you will receive context from the knowledge base and an user message.
- Be brief and polite.
- Be conversational and friendly

The Prompt is the following:

Context to answer user questions
Documents
User message
User Message

Finally, you have the Output box. This is where the result of the LLM is displayed.

Now go to the Export tab. 

Give your AI agent a name and a description. 

Then click the link to launch the web application. Now you can search your Knowledge Base documents for answers about the world economy.

With Knowledge Bases, you can give the LLM access to more specialized data, so you can report on specific domains. In this case the domain is the economy, and the specialized data are the IMF reports. 

Launch the Knowledge Base Agent Now!

Knowledge Bases allow users to search documents faster, and provide LLMs with more expert data. With Stack AI, you can launch our Knowledge Base Agent template instantly, so you don’t have to build an agent from scratch.

Sign up for a free account with Stack AI now so you can launch the template for your team right now!