Agentic RAG Survey (2025)
FreeAgents embedded in RAG pipelines — dynamic, reasoning-driven retrieval beyond static pipelines
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About Agentic RAG Survey (2025)
This survey paper examines Agentic Retrieval-Augmented Generation (Agentic RAG), which enhances traditional RAG by embedding autonomous AI agents that use agentic design patterns such as reflection, planning, tool use, and multi-agent collaboration. It provides a taxonomy based on agent cardinality, control structure, autonomy, and knowledge representation, and analyzes design trade-offs across frameworks. Applications in healthcare, finance, education, and enterprise document processing are explored, along with practical lessons and open research challenges.
Key Features
Embeds autonomous AI agents into RAG pipeline
Agentic design patterns: reflection, planning, tool use, multi-agent collaboration
Dynamic retrieval strategies and iterative context refinement
Taxonomy based on agent cardinality, control structure, autonomy, knowledge representation
Comparative analysis of design trade-offs across frameworks
Applications in healthcare, finance, education, enterprise document processing
Identifies open research challenges in evaluation, coordination, memory management, efficiency, governance
Pros & Cons
Pros
- Provides a principled taxonomy of Agentic RAG architectures
- Comparative analysis of design trade-offs
- Practical lessons for system designers and practitioners
- Comprehensive overview of current Agentic RAG systems
Cons
- Primarily a theoretical survey; no executable code or implementation provided
- Limited to published research; may not cover all proprietary systems
- Does not provide a ready-to-use tool for deployment
Best For
Enterprise document processing
FAQ
What is Agentic RAG?
Agentic RAG enhances traditional Retrieval-Augmented Generation by embedding autonomous AI agents that use agentic design patterns (reflection, planning, tool use, multi-agent collaboration) to dynamically manage retrieval strategies and adapt workflows.
What applications are covered in the survey?
The survey examines applications in healthcare, finance, education, and enterprise document processing.
What are the key contributions of this survey?
It provides a principled taxonomy of Agentic RAG architectures, a comparative analysis of design trade-offs, practical lessons for system designers, and identifies open research challenges.