Global Agentic RAG (Retrieval Augmented Generation) Solution Market Growth (Status and Outlook) 2025-2031

The global Agentic RAG (Retrieval Augmented Generation) Solution market size is predicted to grow from US$ 133 million in 2025 to US$ 857 million in 2031; it is expected to grow at a CAGR of 36.4% from 2025 to 2031.

Agentic RAG or agent-based RAG typically refers to a framework that combines the concepts of agents and Retrieval-Augmented Generation (RAG) in AI. It reshapes the approach to question answering through a cutting-edge agent-based framework.Unlike conventional techniques that depend exclusively on large language models (LLMs), agentic RAG utilizes intelligent agents to address complex questions that necessitate detailed planning, multi-step reasoning, and the integration of external tools. These agents function as expert researchers, skillfully navigating numerous documents, comparing information, creating summaries, and providing thorough, accurate responses.Furthermore, agentic RAG is designed for seamless scalability, allowing for the easy addition of new documents. Each set is overseen by its sub-agent.Together, Agentic RAG emphasizes the importance of informed decision-making and active participation in processes, whether in personal contexts or AI applications. It underscores how leveraging external knowledge can empower users or systems to achieve better results.Agentic RAG isn't just an upgrade; it's a paradigm shift. This framework heralds the redefinition of enterprise AI applications by introducing retrieval precision, generative prowess, and decision-making agents into one competent model. Be it operational efficiency or the quest to deliver unparalleled user experience, Agentic RAG presents an intelligent scalable solution.The creation of Agentic RAG marks a significant evolution in AI-assisted information retrieval. By combining the strengths of Retrieval-Augmented Generation with autonomous agent capabilities, Agentic RAG systems can not only access and retrieve information but also proactively address complex queries and tasks. This advancement represents a leap forward in the practical application of AI, enabling more accurate, comprehensive, and contextually relevant responses.Traditional RAG systems rely on pre-existing data to generate responses. While this approach works, it has limitations, especially when it comes to dealing with complex queries or ensuring the accuracy of the retrieved information. Agentic RAG, on the other hand, employs intelligent agents that can cross-reference multiple sources, verify data, and use multi-step reasoning to ensure the output is both precise and contextually relevant.In essence, Agentic RAG takes the concept of RAG to the next level, combining advanced querying capabilities with intelligent tool usage to deliver superior results.

United States market for Agentic RAG (Retrieval Augmented Generation) Solution is estimated to increase from US$ million in 2024 to US$ million by 2031, at a CAGR of % from 2025 through 2031.

China market for Agentic RAG (Retrieval Augmented Generation) Solution is estimated to increase from US$ million in 2024 to US$ million by 2031, at a CAGR of % from 2025 through 2031.

Europe market for Agentic RAG (Retrieval Augmented Generation) Solution is estimated to increase from US$ million in 2024 to US$ million by 2031, at a CAGR of % from 2025 through 2031.

Global key Agentic RAG (Retrieval Augmented Generation) Solution players cover Microsoft (SimplAI), MongoDB Atlas, Moveworks, Markovate, Ampcome, etc. In terms of revenue, the global two largest companies occupied for a share nearly % in 2024.

LPI (LP Information)' newest research report, the “Agentic RAG (Retrieval Augmented Generation) Solution Industry Forecast” looks at past sales and reviews total world Agentic RAG (Retrieval Augmented Generation) Solution sales in 2024, providing a comprehensive analysis by region and market sector of projected Agentic RAG (Retrieval Augmented Generation) Solution sales for 2025 through 2031. With Agentic RAG (Retrieval Augmented Generation) Solution sales broken down by region, market sector and sub-sector, this report provides a detailed analysis in US$ millions of the world Agentic RAG (Retrieval Augmented Generation) Solution industry.

This Insight Report provides a comprehensive analysis of the global Agentic RAG (Retrieval Augmented Generation) Solution landscape and highlights key trends related to product segmentation, company formation, revenue, and market share, latest development, and M&A activity. This report also analyses the strategies of leading global companies with a focus on Agentic RAG (Retrieval Augmented Generation) Solution portfolios and capabilities, market entry strategies, market positions, and geographic footprints, to better understand these firms’ unique position in an accelerating global Agentic RAG (Retrieval Augmented Generation) Solution market.

This Insight Report evaluates the key market trends, drivers, and affecting factors shaping the global outlook for Agentic RAG (Retrieval Augmented Generation) Solution and breaks down the forecast by Type, by Application, geography, and market size to highlight emerging pockets of opportunity. With a transparent methodology based on hundreds of bottom-up qualitative and quantitative market inputs, this study forecast offers a highly nuanced view of the current state and future trajectory in the global Agentic RAG (Retrieval Augmented Generation) Solution.

This report presents a comprehensive overview, market shares, and growth opportunities of Agentic RAG (Retrieval Augmented Generation) Solution market by product type, application, key players and key regions and countries.

Segmentation by Type:
Routing Agent
One-Shot Query Planning Agent
Tool Use Agent
ReAct (Reason + Action) Agents
Dynamic Planning Agent

Segmentation by Application:
Healthcare
Finance and E-commerce
Telecommunications
Law
Education
Other

This report also splits the market by region:
Americas
United States
Canada
Mexico
Brazil
APAC
China
Japan
Korea
Southeast Asia
India
Australia
Europe
Germany
France
UK
Italy
Russia
Middle East & Africa
Egypt
South Africa
Israel
Turkey
GCC Countries

The below companies that are profiled have been selected based on inputs gathered from primary experts and analyzing the company's coverage, product portfolio, its market penetration.
Microsoft (SimplAI)
MongoDB Atlas
Moveworks
Markovate
Ampcome
Vectorize AI, Inc
Dell Technologies
Primafelicitas
SoluLab
CloudRaft
Softtik Technologies
Hackett Group (LeewayHertz)
Fluid AI
Markovate

Please note: The report will take approximately 2 business days to prepare and deliver.