AI Agents: Mastering Agentic RAG - Part 5
Microsoft Tech Community | March 31, 2025 | AI Agents Series (Part 5 of 10)
Motive / Why I Wrote This
As AI agents become more integrated into knowledge-intensive workflows, their ability to access, synthesize, and reason over external information has become a critical capability. Traditional Retrieval-Augmented Generation (RAG) approaches have made significant progress in this area, but they often lack the agency and dynamism needed for complex information tasks.
I wrote this article to bridge the gap between conventional RAG systems and truly agentic information processing. Through my work with enterprise knowledge systems, I observed that while standard RAG improves factuality, it often suffers from passive retrieval patterns, limited iterative refinement, and an inability to adapt to changing information needs. These limitations can prevent AI systems from effectively navigating complex knowledge landscapes.
As the fifth installment in the AI Agents series, this article builds upon earlier discussions of agent architectures and capabilities to introduce Agentic RAG—a paradigm where retrieval becomes an active, adaptive process driven by agent intelligence rather than static query patterns. By providing concrete implementation patterns for Agentic RAG, I aimed to help developers move beyond basic retrieval to create systems that can autonomously explore, evaluate, and synthesize information from diverse sources.
Overview
Agentic RAG represents a significant evolution in how AI systems interact with external knowledge, transforming retrieval from a passive lookup process to an active exploration guided by agent intelligence. This article provides a comprehensive examination of Agentic RAG architectures, implementation patterns, and practical applications, equipping developers with the tools to build more sophisticated knowledge-powered agents.
At its core, Agentic RAG builds upon traditional RAG by adding agency to the retrieval process—enabling systems to formulate their own queries, evaluate retrieved information critically, and iteratively refine their knowledge-seeking strategies. The article begins by establishing a conceptual framework that distinguishes Agentic RAG from conventional approaches, highlighting how agent-driven retrieval enables more nuanced information processing through metacognitive awareness, strategic planning, and contextual adaptation.
The architectural section introduces several patterns for implementing Agentic RAG, including the Query Planning Agent that decomposes complex information needs into strategic sub-queries; the Retrieval Critique Agent that evaluates information quality and relevance; and the Synthesis Agent that integrates retrieved information into coherent responses. Each pattern is presented with concrete implementation examples using frameworks like Semantic Kernel and the Azure AI Agent service, including code samples that demonstrate key components such as query formulation, retrieval orchestration, and response synthesis.
Particularly valuable is the discussion of iterative retrieval strategies, which enable agents to progressively build knowledge through multiple retrieval cycles. The article presents techniques for implementing "retrieve-then-read" patterns where agents first survey available information before diving deeper, and "exploration-exploitation" approaches that balance broad knowledge gathering with focused investigation of promising information pathways. These strategies are illustrated with practical examples that show how agents can navigate complex information landscapes more effectively than systems using static retrieval approaches.
The article also addresses the challenges of implementing Agentic RAG, including strategies for managing retrieval costs, techniques for preventing retrieval hallucinations, and approaches to maintaining coherence across multiple retrieval cycles. Evaluation frameworks for Agentic RAG systems are presented, highlighting metrics that capture not just factual accuracy but also retrieval efficiency, information synthesis quality, and adaptive reasoning capabilities.
Frameworks & Tools Covered
- Azure AI Search with vector capabilities
- Semantic Kernel for agent implementation
- LangChain for retrieval pipelines
- Hybrid search techniques
- Chunking and embedding strategies
- Query planning algorithms
- Information relevance scoring
- Azure OpenAI Service
- Microsoft Semantic Index
- Citation and source validation techniques
- RAG evaluation frameworks
Learning Outcomes
- Understand the conceptual foundations of Agentic RAG and how it differs from traditional retrieval approaches
- Learn to implement query planning agents that strategically decompose complex information needs
- Master techniques for evaluating and filtering retrieved information based on relevance and quality
- Develop iterative retrieval strategies that progressively build knowledge through multiple cycles
- Implement citation and source tracking to ensure verifiability of agent responses
- Create effective evaluation frameworks specifically designed for Agentic RAG systems
- Build hybrid retrieval systems that combine semantic search with structured knowledge access
Impact / Results
This article has enabled 2,500+ developers to move beyond basic RAG implementations to create more sophisticated, agent-driven knowledge systems. By providing concrete architectural patterns and implementation techniques, it has helped teams build information retrieval systems with significantly improved capabilities for handling complex queries and navigating diverse knowledge sources.
The approaches to iterative retrieval and knowledge synthesis have been particularly impactful, with readers reporting substantial improvements in their systems' ability to answer multi-faceted questions that require integrating information from multiple sources. Several enterprise teams have successfully implemented the query planning techniques described in the article, resulting in more efficient retrieval processes and higher-quality responses to complex information needs.
Community Engagement: 2,500 views on Microsoft Tech Community
Series Navigation
Series: AI Agents Series (Part 5 of 10)
Previous Article: Agentic Tool Use (Part 4)
Next Article: Building Trustworthy Agents (Part 6)