Start with RAG: answers grounded in your data
Retrieval-augmented generation (RAG) means the model does not answer from memory. Before it writes a word, the system retrieves the relevant passages from your own content: documentation, policies, case history, product data. The answer is generated from those passages and cites them.
That single design choice changes the failure mode. An ungrounded model produces plausible text. A grounded one produces text supported by your sources, and when the sources do not support an answer, it can say so instead of inventing one.
Add the agentic part: systems that act
A chatbot ends at the reply. An agent continues: it opens the case, books the appointment, updates the record, routes the exception to a human with context attached. Agentic RAG uses grounded retrieval as its evidence base and then executes the next step inside the systems you already run.
In our deployments that looks like multi-agent systems: a coordinating Synthesiser directs specialised domain agents, each calibrated to its slice of the problem. The agents cite their sources, attach a confidence level, and push back when an input contradicts the evidence.
The test that tells them apart
Ask one question of any vendor: when the conversation ends, what changed in your systems? If the answer is nothing, it is a chatbot. If cases were resolved, records updated, and work moved forward with an audit trail, it is an agent.
The production numbers follow from that difference. A noise-filtering agent keeps 60 to 80 percent of false cases away from a support team. A first-response agent takes routine cases from hours to seconds, around the clock.