The RAG Foundation
RAG (Retrieval Augmented Generation) solved the context problem for LLMs by allowing them to access external knowledge bases. But RAG is passive—it retrieves information when asked.
Agents take this further by actively seeking context, making decisions, and taking actions based on that context.
The Agentic Evolution
Agentic workflows add:
- Proactive context gathering
- Multi-step reasoning and planning
- Tool use and API integration
- Long-term memory and learning
- Autonomous decision-making
Real-World Transformation
Companies moving from RAG to agents see 5X improvement in task completion rates, 80% reduction in manual intervention, and 90% accuracy in complex workflows.
The evolution from RAG to agents represents the shift from AI as a tool to AI as a workforce.
