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GuideRAGAI agents
Building a genuinely useful AI agent with agentic RAG
Everyone talks about AI agents. Without business context, an agent stays a demo. That is exactly what RAG fixes.
An agent that knows nothing about your company answers beside the point. RAG gives it access to your documents at the moment it needs them, rather than stuffing everything into the prompt and hoping.
Agentic RAG goes further. The agent decides for itself how to fetch context, reformulates its search, cross-checks several sources and verifies before answering. That is the difference between a search engine and a colleague.
The document is built for production, not for theory. It goes all the way down to n8n workflows you can copy and a real company case.
Inside
What you will find inside
01Why RAG is essential to move from demo agents to useful ones.
02The difference between classic RAG and agentic RAG, and what it changes in practice.
03n8n workflows to vectorise your documents and keep the base up to date.
04The full setup of an agent, prompt, retrieval, re-ranking and orchestration.
05A real case in tourism, with three workflows to copy.