Agentic RAGs: The Future of AI Workflows Leading AI companies like Glean, Perplexity, and Harvey use sophisticated Agentic RAGs to handle complex enterprise tasks. This powerful combination leverages the strengths of both: - RAG (Retrieval Augmented Generation): - AI Agents: How Agentic RAGs Combine Strengths: - Smarter Retrieval: AI agents intelligently manage RAG's data retrieval, dynamically selecting sources based on query needs. - Enhanced Prompts: Agents build on RAG's augmented prompts by adding planning and real-time, tool-based data, feeding a highly refined input to the LLM. Operational Workflow (Simplified): 1. Query In: Agent receives user query. 2. Context & Plan: Agent uses memory and plans data retrieval strategy. 3. Data Fetch: Tools (like vector search) gather relevant data. 4. Prompt Optimize: Agent combines data, query, and prompt, applying reasoning. 5. Response Out: LLM generates final, intelligent output. Agentic RAGs are transforming how AI tackles complex challenges in the tech industry.

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