Why Are Enterprises Adopting Agentic RAG?

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What's this Article About?
* Discusses limitations of traditional retrieval-augmented generation (RAG) models for enterprise use cases
* Highlights the need for advanced agentic RAG systems with iterative reasoning capabilities
* Covers various enterprise scenarios where agentic RAG excels

Why Read this Article?
✔ Understand key limitations of traditional RAG models
✔ Learn how agentic RAG overcomes those limitations
✔ Gain insights into practical applications of agentic RAG across industries ✔ Appreciate the business value agentic RAG can unlock
✔ Assess if adopting agentic RAG is right for your enterprise

What is RAG?
✔ Simple RAG involves retrieving relevant data and providing it to a large language model (LLM) to generate responses

Why we needed RAG at First Place?
✔ RAG enabled combining language models and information retrieval to leverage immense text data
✔ But RAG has limitations in handling complex, iterative queries
Why current regular RAG isn't enough for Enterprise settings
✔ Cannot iteratively gather more context and missing information
✔ Cannot combine information from multiple sources
✔ Cannot execute multi-step processes and planning
✔ Lacks iterative reasoning capabilities critical for real-world use cases

Why Are Enterprises Adopting Agentic RAG?
✔ Agentic RAG allows iterative reasoning - understanding context, gathering missing info, integrating multiple data sources
✔ Crucial for handling complex enterprise use cases across travel, investment analysis, legal, project management

To Adapt & Thrive, Enterprises Need Iterative Reasoning
✔ Examples showcasing how agentic RAG enables iterative reasoning that regular RAG cannot
✔ Gathering clarifying context for personalized travel planning
✔ Engaging in back-and-forth to refine and tailor information retrieval

Enterprises Need Flexible Summarization
✔ Traditional RAG has limitations in multi-document, viewpoint-driven summarization
✔ Agentic RAG allows iterative reasoning over full document set for flexible, context-aware summaries
Enterprises like Investment Firms Need Structure Analytics
✔ Regular RAG struggles with iterative, context-aware text-to-SQL conversion

Enterprises like Travel Firms Need Planning, Query Decomposition, External Tool Use
✔ Regular RAG cannot handle multi-constraint trip planning
✔ Agentic RAG can decompose queries, integrate APIs, gather missing context iteratively for cohesive planning

The key takeaway is that enterprises are adopting agentic RAG due to its powerful iterative reasoning capabilities that allow solving complex, real-world problems in a context-aware, multi-step manner that rigid regular RAG cannot match.