Agentic RAG: Adaptive AI Agents for Enhanced Retrieval

* Agentic RAG systems dynamically execute connected steps via AI agents, contrasting with traditional RAG's static, predetermined paths; this allows for adaptation based on intermediate results and decision-making regarding subsequent information retrieval.
Core components of agentic RAG include: Memory for learning from past interactions to improve performance over time, Tools as external resources (search, data processors, APIs) for task completion, and Reasoning* via LLMs for planning and decision-making.
* Agentic RAG employs a ReAct framework (Reason + Act): the agent thinks about an action, executes it, observes the result, and repeats until task completion.
* A key advantage of Agentic RAG is its ability to reduce hallucinations and improve task completion in complex workflows due to its adaptive nature, as noted in the reactions.
* Orchestration of agentic RAG systems requires careful management due to the increased number of components and dynamic interactions, a point emphasized in the reactions.
* Additional sources (Weaviate's Agentic Architectures Ebook) could not be accessed, thus limiting the ability to provide further details on implementation and architecture.