Traditional RAG enhances Large Language Models by retrieving relevant information from documents, databases, or knowledge repositories at query time. This approach ensures responses are accurate, grounded in real data, and up to date—rather than relying solely on pre-trained knowledge.
How Agentic AI Takes It a Step Further
Agentic AI introduces autonomous, intelligent agents capable of:
Deciding what data to retrieve
Selecting the right tools or systems
Reasoning across multiple information sources
Dynamically adapting workflows based on context
This makes agentic AI especially effective for complex, multi-step business problems where reasoning and orchestration matter.


Why This Matters for Enterprise AI
By combining RAG with agentic intelligence, enterprises can unlock:

More accurate and context-aware responses
Stronger handling of complex reasoning and decision flows
Seamless knowledge integration across diverse systems


Our Perspective at Akantik

At Akantik, we see the fusion of RAG and agentic AI as a key enabler for next-generation enterprise solutions—particularly in knowledge management, intelligent automation, and decision support systems.
We’d love to hear your perspective: how do you see these hybrid AI approaches shaping the future of enterprise technology?