AgenticAI

Table of Contents Introduction From Classic RAG to Agentic RAG 2.1. What Is Retrieval‑Augmented Generation? 2.2. Limitations of the Classic Pipeline 2.3. The “Agentic” Paradigm Shift Why LangGraph 2.0? 3.1. Core Concepts: Nodes, Edges, and State 3.2. Built‑in Agentic Patterns 3.3. Compatibility with LangChain & LlamaIndex Designing an Autonomous Knowledge Retrieval System 4.1. High‑Level Architecture 4.2. Defining the Graph Nodes 4.3. State Management & Loop Control Step‑by‑Step Implementation 5.1. Environment Setup 5.2. Creating the Retrieval Node 5.3. Building the Reasoning Agent 5.4. Putting It All Together: The LangGraph 5.5. Running a Sample Query Advanced Agentic Behaviors 6.1. Self‑Critique & Re‑asking 6.2. Tool‑Use: Dynamic Source Selection & Summarization 6.3. Memory & Long‑Term Context Evaluation & Monitoring 7.1. Metrics for Autonomous RAG 7.2. Observability with LangGraph Tracing Deployment Considerations 8.1. Scalable Vector Stores 8.2. Serverless vs. Containerized Execution 8.3. Cost‑Effective LLM Calls Best Practices & Common Pitfalls Conclusion Resources Introduction Retrieval‑Augmented Generation (RAG) has become the de‑facto standard for building knowledge‑aware language‑model applications. By coupling a large language model (LLM) with an external knowledge store, developers can overcome the hallucination problem and answer domain‑specific questions with up‑to‑date facts. ...

Table of Contents Introduction RAG 101: Foundations of Retrieval‑Augmented Generation Why Classic RAG Falls Short in Production Enter Agentic RAG: The Next Evolution Core Architecture of an Agentic RAG System 5.1 Retriever Layer 5.2 Planner / Orchestrator 5.3 Executor LLM 5.4 Memory & Knowledge Store Designing Autonomous Retrieval Loops Practical Implementation with LangChain & LlamaIndex Scaling Agentic RAG for Production 8.1 Observability & Monitoring 8.2 Latency & Throughput Strategies 8.3 Cost Management 8.4 Security, Privacy, and Compliance Real‑World Deployments 9.1 Customer‑Support Knowledge Assistant 9.2 Enterprise Document Search 9.3 Financial Data Analysis & Reporting Best Practices, Common Pitfalls, and Mitigation Strategies Future Directions: Towards Self‑Improving Agentic RAG Conclusion Resources Introduction Retrieval‑augmented generation (RAG) has become a cornerstone technique for building LLM‑powered applications that need up‑to‑date, factual information. By coupling a retriever (often a dense vector search over a knowledge base) with a generator (a large language model), developers can produce answers that are both fluent and grounded in external data. ...

Introduction Agentic AI—autonomous software entities that can reason, act, and iteratively improve—has moved from research prototypes to production‑grade services. Modern agents (e.g., personal assistants, autonomous bots, and decision‑support systems) rely heavily on retrieval‑augmented generation (RAG), where a large language model (LLM) consults an external knowledge store before producing output. The knowledge store is often a vector database that holds dense embeddings of documents, code snippets, or sensory data. When agents operate at scale—handling thousands of concurrent users, processing multi‑modal streams, or persisting experience across days, weeks, or months—two technical pillars become critical: ...