What is the difference between RAG and Agentic RAG?

Traditional RAG uses a fixed, single-pass retrieval approach: query the vector database, retrieve documents, and generate a response. Agentic RAG embeds autonomous AI agents that dynamically decide when to retrieve, which sources to query, whether to refine the search, and how to synthesize multiple results. The agent can take non-linear steps, use multiple tools, and self-correct based on intermediate results—transforming retrieval from a static pipeline into an intelligent, adaptive process.

Which framework should I use for agentic RAG—LangChain or LlamaIndex?

If your focus is search and retrieval with document-heavy workflows, LlamaIndex wins with its advanced indexing and 300+ data connectors. If you need flexible AI pipelines with extensive tool integrations and complex orchestration, LangChain (with LangGraph for multi-agent systems) is better. For enterprise RAG systems, many teams combine both—using LlamaIndex for high-quality retrieval and LangChain for workflow orchestration.

What are the four agentic design patterns?

The four foundational patterns are: (1) Reflection—agents evaluate and iteratively refine their outputs; (2) Tool Use—agents interact with external tools, APIs, and databases beyond their training data; (3) Planning—agents break down complex tasks into structured, multi-step workflows; and (4) Multi-Agent Collaboration—specialized agents work together, each contributing expertise to complete complex tasks.

How does agentic RAG help with AI platform optimization for law firms?

AI platforms like ChatGPT, Claude, and Perplexity use RAG systems to retrieve and cite sources when answering user queries. Law firms optimized for these platforms—through proper GEO services, structured data markup, and citation-worthy content—become the sources these systems retrieve. Understanding how agentic RAG works helps firms create content that AI systems can easily find, validate, and cite.

What is Self-RAG and how does it improve response quality?

Self-RAG trains models to decide when to retrieve information and to critique their own outputs. Rather than always retrieving or never questioning responses, Self-RAG systems evaluate whether external information is needed, retrieve it if so, then assess whether the generated response faithfully uses that information. This self-reflection and refinement loop significantly boosts factuality and citation accuracy.

What challenges should enterprises expect when implementing agentic RAG?

Key challenges include: high computational costs for training and deploying RAG models; complexity of integrating with legacy IT infrastructure; data privacy and security concerns in regulated industries; lack of standardized frameworks for evaluating RAG performance; and the unpredictability of agentic workflows compared to deterministic pipelines. Start with well-defined, low-risk use cases before expanding to complex multi-agent systems.

How do vector databases fit into agentic RAG architectures?

Vector databases like Qdrant, Pinecone, and Milvus serve as the foundation for semantic search in both traditional and agentic RAG. In agentic systems, the agent uses vector search as one of many available tools. Instead of a fixed query-retrieve-generate pipeline, agents dynamically choose when to query the vector store, can reformulate queries based on initial results, and combine vector search with other tools to construct comprehensive answers.

11 - 2025 - Intercore Technologies

Agentic RAG: Everything AI Engineers Need to Know in 2026

📑 (Click to expand) What Is Agentic RAG? Why Simple RAG Systems Fall Short The Four Agentic Design Patterns Implementation Frameworks Compared Enterprise Adoption and Market Trends Frequently Asked Questions Getting Started with Agentic RAG Simple naïve RAG systems are rarely used in real-world applications anymore. As AI engineers scale their retrieval-augmented generation solutions, they quickly discover that static, single-query architectures cannot handle the complexity of enterprise demands. The solution? Adding agency to the RAG system—ideally, a minimal and controlled amount that transforms retrieval from a one-shot process into an intelligent, adaptive workflow. There is no single blueprint for extending RAG systems to solve your specific business use case. You will need to adapt. For this, you need to understand the potential moving pieces in Agentic RAG and how they work together. This comprehensive FAQ guide covers everything AI engineers need to know about Generative Engine Optimization and agentic retrieval architectures—from foundational design patterns to production-ready frameworks. 💡 Key Insight According to research from Grand View Research, the … Learn More

Amazon’s $2.5 Billion Dark Patterns Settlement: What Every Law Firm Needs to Know

📋 Click to expand Case Overview: FTC v. Amazon.com, Inc. Legal Theories: ROSCA, FTC Act, and Dark Patterns Liability The Legal Teams: Who Litigated This Historic Case Trial Strategy and Key Evidence Practice Implications for Attorneys Client Advisory: ROSCA Compliance Framework Frequently Asked Questions On September 25, 2025, Amazon agreed to pay $2.5 billion to settle Federal Trade Commission allegations that its Prime subscription program violated federal consumer protection laws—the largest civil penalty ever obtained by the FTC for a rule violation and the second-highest consumer restitution in agency history. For attorneys practicing in consumer protection, corporate compliance, e-commerce litigation, or advising technology clients, this settlement represents a watershed moment. The case establishes that “dark patterns”—manipulative user interface designs—constitute actionable violations of the Restore Online Shoppers’ Confidence Act (ROSCA) and Section 5 of the FTC Act, with billion-dollar consequences and personal liability exposure for executives. This analysis examines the legal theories, trial strategies, and practice implications that make FTC v … Learn More

Day: November 30, 2025

Agentic RAG: Everything AI Engineers Need to Know in 2026

Amazon’s $2.5 Billion Dark Patterns Settlement: What Every Law Firm Needs to Know

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