Basic RAG retrieves documents first, then generates answers; agentic RAG lets the agent decide whether to retrieve at all.
Key differences:
| Aspect | Basic RAG | Agentic RAG |
|---|---|---|
| Retrieval trigger | Automatic for every query | Agent-driven, context-dependent |
| Query handling | Fixed retrieval method | Rewrite, decompose, adapt queries |
| Iteration | Single pass | Multiple retrieval loops |
| Relevance evaluation | User evaluates output | Agent evaluates and re-retrieves |
| Error handling | No recovery from bad retrieval | Agent detects and corrects |
Agentic RAG excels when:
- Queries require multi-step reasoning (“What products did we sell to this customer, and compare their margins to industry averages?”)
- Documents may be irrelevant or conflicting
- Complex workflows need intelligence about when external knowledge is needed
For production agentic workflows, the vector database (Milvus) becomes the agent’s dynamic memory—not a static document store. This is the 2026 enterprise baseline.
Related Resources: