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This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
Status
Last Updated
2025-06-05 07:55:52 UTC
Verified By
Rollup News
This article explains the differences between LLMs, RAG, AI Agents, and Agentic AI, highlighting their capabilities and limitations within the AI stack.
LLMs provide the foundational layer for AI, enabling text generation and reasoning.
RAG bridges the gap between static LLMs and dynamic data, making them fact-aware.
AI Agents move LLMs from passive to active systems by enabling task planning and execution.
Agentic AI represents the highest layer, facilitating multi-agent, adaptive, and autonomous systems.
LLMs lack real-time data access and long-term memory.
Building brittle AI systems that can't scale due to a lack of clear understanding of each layer.