Table of Contents
1. What this is really about
In almost every serious AI project, the first question is not the model name. It is: Should the solution explain content, provide reliable company knowledge or actually perform operational work? Once those layers are separated, the terminology becomes much easier.
The fastest way to a good project is therefore not the biggest system, but the smallest system that solves the problem properly. That approach saves budget, shortens adoption time and reduces later corrections.
Immediately usable
Our AI glossary also translates the terms into plain English.
If you just want a simple explanation, use the glossary or the guide in the bottom right as a starting point.
2. The fast decision logic
Use an LLM when
- You need drafting, brainstorming or language polishing.
- The answer can stay general and does not need to cite internal documents.
- You want a fast, low-friction pilot with visible value.
Use RAG when
- Your team needs answers from internal policies, manuals or knowledge bases.
- The main risk is hallucination or outdated information.
- You want trust, traceability and a narrow scope.
Use an AI Agent when
- The task has multiple steps and the AI must use tools.
- There is a clear input, target state and measurable output.
- You want automation, not just a better chat interface.
Use Agentic AI when
- Several roles must collaborate on a larger workflow.
- You need review, control and fallback logic around the automation.
- The process is important enough to justify orchestration and guardrails.
3. The four terms in plain language
LLM
A strong text engine for drafting, summarizing and explaining.
Best for: Knowledge work that does not depend on company documents
Watch out when: When answers must be grounded in verified internal sources.
RAG
A language model that retrieves from your own documents before answering.
Best for: Internal knowledge, support, policies and document search
Watch out when: When the task also requires tool use or multi-step execution.
AI Agent
A system that can plan, use tools and continue until a task is done.
Best for: Workflows with steps, systems and handoffs
Watch out when: For simple Q&A where retrieval is enough.
Agentic AI
A coordinated setup of several specialized agents with roles and checks.
Best for: Larger process automation with research, drafting and quality control
Watch out when: As a first project if process ownership is still unclear.
In one sentence
If the system only needs to generate text, an LLM is often enough. If the answer must be grounded in company knowledge, you want RAG. If the system needs to do something, you need an agent. If multiple agents must work together, you are in Agentic AI territory.
4. When each system makes sense
| Topic | LLM | RAG | AI Agent | Agentic AI |
|---|---|---|---|---|
| Knowledge questions | Fine for general questions | Best when documents matter | Only if follow-up steps are needed | Rarely the first choice |
| Process automation | Limited | As knowledge layer | Very good | Very good for complex workflows |
| Traceability | Weak | Strong | Depends on logging | Depends on orchestration |
| Implementation effort | Low | Medium | Medium to high | High |
5. Common project patterns
In practice, the cleanest sequence is often: first a RAG system for knowledge, then an agent for the repetitive steps, and only then a broader multi-agent orchestration. That gives the company trust early, before the solution becomes complex.
- Support: RAG answers policy questions, an agent creates a ticket if needed.
- Sales: an LLM drafts text, RAG pulls product knowledge and references from your own material.
- Back office: agents process receipts, collect data and hand controlled results into the ERP.
Bottom-right guide
The AI guide in the bottom right now points directly into this decision path.
That means your visitors can move from vague curiosity to a concrete architecture choice much faster.
6. The most common mistakes
- Starting with the fanciest architecture instead of the business problem.
- Using a pure LLM where document accuracy is required.
- Building a RAG system without clean source documents and ownership.
- Calling everything an agent, even when no tool use or planning exists.
- Skipping guardrails, logging and human review on sensitive workflows.
7. Next steps
If you want the right architecture for your company, we should not start with buzzwords. We should start with your process. Then it becomes clear whether an LLM, RAG, an agent or a layered setup makes the most sense.
Does this setup match your company?
If you want, we can review the right architecture for your business in the AI initial consultation. The focus is not technology for its own sake, but impact, risk and effort.
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