RAG and AI agents, explained without the hype

RAG in one paragraph

A language model answers from what it learned in training, which does not include your company's documents. Retrieval-augmented generation (RAG) fixes that by searching your own content for relevant passages and giving them to the model along with the question. The model answers from that context, and the answer can cite where it came from.

Agents in one paragraph

An agent is a model that can use tools. Instead of only writing text, it can decide to look something up, call an API, run a workflow or ask a predictive model for a score — then use the result to decide what to do next.

What has to be true first

Both ideas are simple. Making them dependable is mostly about everything around the model.

• The content it retrieves from is current, and it is only allowed to see what the user may see.
• Answers show their sources, so a person can check them.
• Tools are well defined: clear inputs, clear outputs, and a log of every call.
• Consequential actions wait for a person to approve them.
• There is a trace of each run — what was asked, what was retrieved, what was called, what was done.

Where language models should not decide alone

A language model is a poor substitute for a purpose-built predictive model. For a credit decision or a demand forecast, a model trained on your data — with metrics and explanations — is the right tool. The agent's job is to reason over that prediction and its explanation, not to guess the number itself.

That division of labour is why AI belongs on the same platform as data and machine learning rather than beside it.