A few years into the LLM hype and right at the peak of the AI Agent euphoria, it’s time to reflect on what these technologies actually bring to the table—and where they’re headed.
This post is a follow-up to our previously published three-part blog series that outlines how LLMs are reshaping knowledge-based systems (KBS) across various levels of complexity and application. If you prefer presentations over blog posts, you can also check out my talk at the Vienna Data Science Meetup (June 2025), where I dive deeper into these concepts.
What Are Knowledge-Based Systems?
Knowledge-Based Systems (KBS) are AI systems that support humans—typically domain experts—in solving complex problems. In the 1990s and early 2000s, these were often built as rule-based expert systems. Unfortunately, they struggled with real-world adoption due to their rigidity and the effort needed to encode domain-specific knowledge.
Enter Large Language Models.
With LLMs, we suddenly have tools that can represent high-dimensional knowledge in virtually any domain, without the need for hand-crafted ontologies or rule sets. They can reason over both structured and unstructured data, enabling new types of KBS that are far more adaptable and scalable.
We now see KBS emerge in three main forms:
- Question & Answering (Q&A) Systems – Targeted information retrieval for small teams.
- Enterprise Knowledge Systems – Organization-wide access to company knowledge.
- AI Agent Platforms – Autonomous or semi-autonomous AI agents that interact, reason, and take action.
Let’s look at each of these in more detail.
Q&A Systems: Smart Search for Small Teams
For small teams working within a single domain, a full-blown AI agent may be overkill. What’s often needed is a smarter way to find relevant information—fast.
This is where Retrieval-Augmented Generation (RAG) comes into play. RAG systems combine vector-based search with LLMs to generate accurate, contextualized answers from a defined knowledge base.
Open-source tools like Onyx and ragflow make it easy to build and deploy these systems locally, keeping your data private while boosting team productivity.
Great for uses-cases like:
– Internal documentation search
– Helpdesk assistants
– Project-specific knowledge bases
Enterprise Knowledge Systems: A Unified Chat Interface for All Org Data
When teams grow, and the data grows with them, simple Q&A systems aren’t enough. Enter the Enterprise Knowledge System (EKS)—a centralized chat portal that gives employees access to all of the organization’s knowledge.
These systems are more complex and need to respect existing access rights, data policies, and compliance rules. They typically integrate with multiple document management systems, intranets, and databases to create a unified conversational layer over all enterprise data.
Key challenges:
- Poor document hygiene: Duplicates, outdated files, and inconsistent structures make it hard for LLMs to find and reason over the right content.
- Stochastic answers: LLMs may give different answers to the same question, which can be frustrating in business-critical environments.
Still, with proper governance, EKS can unlock enormous value across departments, breaking silos and making institutional knowledge more accessible than ever.
AI Agent Platforms: Automating Business Processes with Intelligence
Sometimes answering questions isn’t enough. You need action. That’s where AI Agent Platforms come in.
AI agents powered by LLMs can be designed to follow business workflows, interact with APIs, use internal tools, and even trigger other agents or human responses—24/7.
A key enabler here is the Model Context Protocol (MCP), which helps standardize how agents interface with external tools and each other.
Common use cases:
- HR bots handling onboarding
- IT agents resolving tickets or provisioning resources
- Finance agents generating reports or answering policy queries
What’s coming next is even more exciting: Agent collaboration. Rather than relying on a single agent with limited tools, organizations will deploy teams of agents that collaborate—each bringing their own specialized knowledge or capabilities.
But this power comes with complexity. Just like in EKS, stability becomes a real issue. As agents rely on ever-changing APIs, tools, and knowledge bases, their behavior can become unpredictable.
We believe this calls for something akin to AI Agent Version Control—a way to “freeze” an agent’s tools and knowledge at a given version to ensure consistent performance and reproducibility.
Navigating the Solution Landscape
Right now (August 2025), the landscape of platforms and tools for building KBS is exploding. From DIY development frameworks to enterprise platforms, options are plentiful—but consolidation is inevitable.
Our advice?
Don’t lock yourself into a single vendor or platform yet.
Instead, start by asking the right questions:
- Do we just need better information access? → Start with a Q&A system.
- Do we want to democratize access to all company knowledge? → Build an Enterprise Knowledge System.
- Do we need automation and action-taking AI? → Consider an AI Agent Platform.
Test existing platforms. Explore open-source options. And if you need help defining your use case or navigating implementation, we’re here to help at Pasieka AI Solutions.
Thanks for reading. If you found this helpful, feel free to share or check out our full series on LLM-powered knowledge systems.