Every business exploring AI hits the same fork in the road: do we need retrieval-augmented generation (RAG), or do we need agents? They solve different problems, and choosing wrong is expensive.
RAG: grounded answers
RAG retrieves relevant context from your data and feeds it to the model so answers are grounded in your documents, not the model's training data. It's ideal for support, search, and knowledge questions.
Agents: actions, not just answers
Agentic systems plan, call tools, and take multi-step actions — booking, updating records, orchestrating other systems. The value isn't the answer; it's the work done.
Most real products need both: retrieval for grounding, agents for action.
If you're weighing the two for a specific workflow, let's talk.
