Agentic RAG: A new AI architecture for ROI

Most Retrieval-Augmented Generation (RAG) pipelines today stop at a single task — retrieve, generate, and respond. That model works, but it’s 𝗻𝗼𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁. It doesn’t adapt, retain memory, or coordinate reasoning across multiple tools. That’s where 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗥𝗔𝗚 changes the game. 𝗔 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 In a traditional RAG setup, the LLM acts as a passive generator. In an 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 system, it becomes an 𝗮𝗰𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺-𝘀𝗼𝗹𝘃𝗲𝗿 — supported by a network of specialized components that collaborate like an intelligent team. Here’s how it works: 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿 — The decision-maker that interprets user intent and routes requests to the right tools or agents. It’s the core logic layer that turns a static flow into an adaptive system. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 — Maintains awareness across turns, retaining relevant context and passing it to the LLM. This eliminates “context resets” and improves answer consistency over time. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗟𝗮𝘆𝗲𝗿 — Divided into Short-Term (session-based) and Long-Term (persistent or vector-based) memory, it allows the system to 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. Every interaction strengthens the model’s knowledge base. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗟𝗮𝘆𝗲𝗿 — The foundation. It combines similarity search, embeddings, and multi-granular document segmentation (sentence, paragraph, recursive) for precision retrieval. 𝗧𝗼𝗼𝗹 𝗟𝗮𝘆𝗲𝗿 — Includes the Search Tool, Vector Store Tool, and Code Interpreter Tool — each acting as a functional agent that executes specialized tasks and returns structured outputs. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽 — Every user response feeds insights back into the vector store, creating a continuous learning and improvement cycle. 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Agentic RAG transforms an LLM from a passive responder into a 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗲𝗻𝗴𝗶𝗻𝗲 capable of reasoning, memory, and self-optimization. This shift isn’t just technical — it’s strategic It defines how AI systems will evolve inside organizations: from one-off assistants to adaptive agents that understand context, learn continuously, and execute with autonomy.

𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝘀𝘁𝗮𝘁𝗶𝗰. 𝗧𝗵𝗲𝘆 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲, 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲, 𝗮𝗻𝗱 𝘀𝘁𝗼𝗽. Agentic RAG systems reason, adapt, and improve over time. The difference is architecture: 1. Agent Orchestrator routes requests based on intent 2. Context Manager eliminates memory resets between turns 3. Memory Layer stores both session and long-term knowledge 4. Tool Layer executes specialized tasks through functional agents 5. Feedback Loop turns every interaction into training data This is the shift from chatbot to cognitive system. Your AI assistant shouldn't just answer questions. It should remember context, coordinate tools, and learn from usage. One of the gaps between prototype and production AI is memory and orchestration. Build for both from day one.

Correct me if I’m wrong, but isn’t this just a 1980s-style Expert System architecture, with the #LLM functioning as a loose natural language UI? You have a planner, a rule engine (RAG knowledge base), a context tracker, à history tracker … surely this is just a bigger and faster MYCIN? I have been saying for about 3 years now that an LLM #frontend might make sense, but that it needs a back end similar to Good Old-Fashioned AI (#GOFAI) and obviously there needs to be some checking of the #UI because of known #GenAI errors such as #hallucination and #confabulation. Have we finally reached that state?

TL;DR: Claim: Classic RAG is “retrieve→generate→respond,” which is too static. Proposal: “Agentic RAG” adds orchestration, multi-type memory, richer retrieval, tool use, and a feedback loop so the system can adapt and improve.

🤖 Most RAG systems stop at one job — Retrieve → Generate → Respond. It works. But it’s not intelligent. It doesn’t adapt. It doesn’t remember. And it definitely doesn’t reason across multiple tools or contexts. That’s where Agentic RAG (Retrieval-Augmented Generation) changes everything. 🧠 A Smarter Architecture for Adaptive Reasoning In a traditional RAG setup, the LLM is just a passive generator. In an Agentic RAG system, it becomes an active problem-solver — powered by a network of intelligent, specialized components working together like a team. Here’s what that looks like 👇 ⚙️ Core Components of Agentic RAG 1️⃣ Agent Orchestrator – The decision-maker. Understands user intent, routes tasks to the right tools or agents, and keeps workflows adaptive. 2️⃣ Context Manager – Maintains awareness across turns. No more context resets — it preserves continuity and coherence in long interactions. 3️⃣ Memory Layer – Learns from experience. Includes: • Short-Term Memory: session-based recall • Long-Term Memory: vector-based knowledge that evolves over time 4️⃣ Knowledge Layer – The foundation of intelligence. Combines embeddings, similarity search, and multi-granular document segmentation (sentence, paragraph, recursive). 5️⃣ Tool Layer – Functional agents for execution. Search, vector store, code interpreter — each handles specialized tasks and returns structured outputs. 6️⃣ Feedback Loop – The self-learning engine. Every interaction feeds insights back into the vector store — enabling continuous improvement. 🚀 Why It Matters Agentic RAG turns an LLM from a passive chatbot into a cognitive engine — capable of reasoning, memory, adaptation, and self-optimization. This shift isn’t just technical — it’s strategic. It’s how AI inside organizations evolves: from one-off assistants ➜ to autonomous agents that understand context, learn continuously, and act intelligently. 💬 What’s your take — is Agentic RAG the next major leap in enterprise AI systems? Follow: Brij kishore Pandey #AIAgents #AgenticAI #RAG #RetrievalAugmentedGeneration #LLM #LangChain #LangGraph #CrewAI #AIArchitecture #CognitiveAI #ArtificialIntelligence #AIProducts #AIInnovation #AIFuture #MachineLearning

𝐓𝐡𝐞 𝟒 𝐋𝐚𝐲𝐞𝐫𝐬 𝐨𝐟 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 🧠 If you want to build real AI agents, you need to understand what’s happening beneath the surface. Let’s break it down 👇 ① 𝐋𝐋𝐌𝐬 – 𝐓𝐡𝐞 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫 This is the engine. It’s where language models like GPT, Claude, or DeepSeek handle: - Tokenization & inference – how text is processed and predicted. - Prompt design – crafting inputs for better outputs. - LLM APIs – the interfaces that let you plug models into your system. ② 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 – 𝐋𝐋𝐌𝐬 𝐰𝐢𝐭𝐡 𝐈𝐧𝐭𝐞𝐧𝐭 Agents give LLMs the power to act — not just talk. They handle: - Tool usage & function calling (connecting to APIs or apps) - Reasoning methods like ReAct or Chain-of-Thought - Task planning and memory management for continuity This is how assistants start doing useful work, not just generating words. ③ 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 – 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 Here, multiple agents cooperate to solve complex goals. Key features include: - Inter-agent communication (A2A, ACP protocols) - Routing & scheduling – deciding which agent handles which task - State coordination – keeping a shared view of progress - Agent specialization – different agents for reasoning, retrieval, or execution Frameworks like CrewAI, LangGraph, or Autogen for orchestration ④ 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 – 𝐓𝐡𝐞 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐁𝐚𝐜𝐤𝐛𝐨𝐧𝐞 The top layer keeps everything reliable and safe: - Observability & logging (via tools like Comet’s Opik) - Error handling, retries, and recovery - Security, access control, and rate limits Workflow automation & human-in-the-loop oversight 💡 Takeaway: Agentic AI isn’t one thing — it’s a stack. Each layer builds on the one below to bring reasoning, coordination, and trust to intelligent systems.

🚨 RAG is powerful. But it's not a silver bullet. Most teams building AI agents jump straight to RAG thinking it will solve all their accuracy problems. Then reality hits: THE RAG-ONLY TRAP Here's what happens when you rely on RAG alone: RETRIEVAL FAILURES Your AI pulls the wrong chunks from your knowledge base. Users ask about pricing tiers and get generic feature descriptions instead. The result? Confused customers and eroded trust. CONTEXT LIMITATIONS RAG systems index raw document chunks, not actual answers. When a user asks "How do I reset my password?", the AI retrieves paragraphs about account security instead of the step-by-step reset process they need. DECISION-MAKING DEPENDENCY RAG depends entirely on the base model's reasoning ability. You're forced to use larger, more expensive models just to make sense of the retrieved context. Your costs spiral while accuracy barely improves. EVALUATION BLINDSPOT Most RAG solutions can't tell you if they're giving correct answers. You deploy, hope for the best, and discover problems only after users complain. THE BETTER APPROACH At Navigable AI, we flip the script. We start with fine-tuning to build deep product understanding, then add Q&A-indexed RAG for dynamic updates. Not document chunks. Actual question-answer pairs. The difference? Our customers see 90%+ verified accuracy from day one because we evaluate every response before deployment. RAG has its place. But accuracy starts with training, not retrieval. Ready to build AI agents that actually work? Download our free playbook: https://zurl.co/WK7vd Or see how Navigable AI combines fine-tuning and smart RAG: https://zurl.co/BUJRg

Architecting Specialized RAG Frameworks for Next-Gen AI Retrieval-Augmented Generation (RAG) has evolved into more than a technique—it’s now an AI architecture. Enterprises today scale intelligence by leveraging specialized RAG frameworks that bring accuracy, compliance, personalization, and reasoning depth. At YaanAI, we work across the full spectrum of RAG approaches to enable enterprise transformation: 🔹 Standard RAG → Accuracy boost, fewer hallucinations 🔹 Agentic RAG → Autonomous agents for complex tasks 🔹 Graph RAG → Knowledge graphs for reasoning in law & medicine 🔹 Modular RAG → Independent, flexible components 🔹 Memory-Augmented RAG → Long-term personalization & continuity 🔹 Multi-Modal RAG → Text + images + audio = richer insights 🔹 Federated RAG → Privacy-first for healthcare & compliance 🔹 Streaming RAG → Real-time decision intelligence 🔹 ODQA RAG → Open-domain question answering 🔹 Contextual Retrieval RAG → Conversational AI with context 🔹 Knowledge-Enhanced RAG → Structured KBs for factual grounding 🔹 Domain-Specific RAG → Tailored for legal, finance & life sciences 🔹 Hybrid RAG → Combining multiple retrieval strategies 🔹 Self-RAG → Self-reflective AI for reliable outputs 🔹 HyDE RAG → Hypothetical docs for better recall 🔹 Recursive / Multi-Step RAG → Iterative reasoning at scale 💡 The Takeaway: RAG is no longer one-size-fits-all. The right framework can unlock real-time supply chain visibility, healthcare compliance, financial trust, and cross-domain intelligence. At YaanAI, we design and implement next-generation RAG architectures that integrate predictive, prescriptive, and generative AI—building future-ready enterprises. 📩 Let’s collaborate: info@yaanai.us | 🌐 yaanai.us #AI #GenerativeAI #SupplyChain #EnterpriseAI #AgenticAI #KnowledgeGraph #StreamingAI #Innovation #RAG #YaanAI

AI agents don’t fail because they lack intelligence. They fail because they lack memory. Without memory, an agent repeats mistakes, loses context, and forgets its users. If you want an agent that actually performs in production, you need more than clever prompting — you need a structured memory system. Here’s the memory framework used to scale agents in real-world environments. 1. Long-Term Memory withPersistent Knowledge This is the agent’s evolving knowledge base — the foundation of its “mind.” Semantic Memory Stores facts and reference knowledge . Captures past interactions and experiences Holds know-how and workflows 2. Short-Term Memory . Think of this as the agent’s working memory — a temporary space for current tasks. Prompt Context -The structure of the current task, including instructions, tone, and goals Tool Awareness -Which tools are available at the moment Ephemeral Context -Temporary user data such as time zone, current query type, or page viewed Why it matters: Short-term memory enables quick, real-time decisions and accurate task execution. When combined, this architecture allows agents to: Manage complex workflows autonomously Acquire knowledge without constant retraining Personalize user experiences at scale Avoid repeating mistakes This is the difference between a chatbot that simply replies and an agent that can reason, adapt, and evolve. Most implementations only include one type of memory. The most effective agents use all of them. The true difference between short-term hype and long-term value is memory.

𝐈𝐬 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 𝐃𝐞𝐚𝐝? 𝐏𝐫𝐞𝐬𝐞𝐧𝐭𝐢𝐧𝐠 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 (𝐀𝐂𝐄) A recent Stanford, SambaNova, and UC Berkeley paper is challenging a basic practice in AI: fine-tuning. The novel framework, 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 (ACE), suggests we can achieve 𝘀𝗲𝗹𝗳-𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 LLMs not through changing their weights, but through actively constructing their context. Instead of cramming instructions into short, static prompts, ACE places context in a dynamic 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸. The playbook evolves and enhances itself continuously in a multi-agent process: · 𝙂𝙚𝙣𝙚𝙧𝙖𝙩𝙤𝙧: Executes tasks and produces reasoning trajectories. · 𝙍𝙚𝙛𝙡𝙚𝙘𝙩𝙤𝙧: Distills specific lessons from successes and failures. · 𝘾𝙪𝙧𝙖𝙩𝙤𝙧: Adds the lessons as structured, incremental context updates. The results are dramatic. On the AppWorld benchmark, 𝘙𝘦𝘈𝘤𝘵+𝘈𝘊𝘌 achieved 59.4%, a tremendous improvement over baseline methods and competitive with systems that use much larger models. On financial analysis tasks, it boosted performance by an average of 8.6%. So is fine-tuning dead? Not exactly. But it does place context engineering firmly on the map as a first-rate choice for model adaptation. The advantages are clear: · 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: ACE is reportedly reducing adaptation latency by up to ~87% and token costs significantly. · 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Context is debuggable and readable compared to parameter updates. · 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Knowledge can be inserted, or even "unlearned," effectively at runtime. ACE demonstrates that for complex, agentic tasks, performance can lie less in having a bigger model and more in having a richer, more detailed, and dynamically curated context.