Wouter van Heddeghem’s Post

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.

Love this breakdown of Agentic RAG. To add to this... One pattern I’m seeing in AI applications delivering ROI is the move from one monolithic LLM to multiple SLMs acting as agents per function — collaborating in a microservices-style architecture. I touch on this in the AI Building Blocks chapter of my upcoming book "Orchestrating Intelligence: The AI Playbook", which culminates with the Orchestrator’s Compass — a practical guide for navigating the AI landscape. #nvidia #aws #azure #agenticai #rag #llm #orchestratingintelligence

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

AI agents aren’t magic, they’re a stack you evolve. Here’s a super simple breakdown of how AI agents rise in 6 clear phases (save this for later): 1. Foundation: Basic LLM Input → LLM → Output ✅ Trained on static text corpora ✅ No tools, no memory ✅ Short attention span 2. Documents: Bigger Brains ✅ Input/output includes files & documents ✅ Larger context windows ✅ Still limited to pretraining knowledge 3. RAG + Tools: Real-World I/O ✅ Pull in fresh data on demand ✅ Call APIs to take action ✅ Fewer hallucinations, more task-specific performance 4. Memory: Context That Sticks ✅ Remembers users and prior interactions ✅ Personalizes responses ✅ Enables long-term, multi-step workflows 5. Multimodal: Beyond Text ✅ Understands images, tables, and mixed input ✅ Generates richer, more dynamic output 6. Agentic Loop: Goals → Plans → Actions ✅ Breaks tasks into steps ✅ Selects tools intelligently ✅ Self-monitors, retries, and adjusts in real-time Want to build AI agents that work in the wild? Think like an operator, not a researcher: ✅ Start small: add one capability (usually RAG) ✅ Integrate thoughtfully: every new layer adds complexity ✅ Monitor what matters: accuracy, retries, time saved, satisfaction ✅ Ship weekly: double down on what works, cut what doesn’t Your architecture checklist: ✅ Strong base model ✅ RAG that actually retrieves ✅ Tools that do real work ✅ Memory that matters ✅ Multimodal I/O ✅ Self-checks and retries ✅ Guardrails for safety Your turn: Which phase are you at, and what’s the next capability you’ll ship this week? Follow Alex for real-world, no-hype agent builds. Thanks to Manthan for another banger graphic!

𝗡𝗲𝘄 𝘀𝗸𝗶𝗹𝗹 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀. 𝗔𝘀 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗴𝗿𝗼𝘄𝗶𝗻𝗴 𝘀𝗼 𝗳𝗮𝘀𝘁, 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗲𝗻𝗼𝘂𝗴𝗵 In the early days of large language models (LLMs), the key was Prompt Engineering: how to write a smart prompt that gives you the best result. But today, with the complexity of systems and the push toward developing AI agents, that is no longer enough. This is where a new concept, Context Engineering, comes in, which has become far more important. The main difference: ☑️ Prompt Engineering = what you tell the model ☑️ Context Engineering = what the model sees Why? Because every token in the context consumes part of the model’s limited “attention budget.” Just like humans: if your brain is flooded with too much information at once, you get distracted and forget the essentials. Models work the same way. This is called “context rot”. losing focus and accuracy as the information load increases. How ? 1. Choose only what’s useful: Only show the model information directly relevant to the task. 2. Summarize outdated content: Instead of keeping the full conversation history, maintain a smart summary. 3. Fetch data on demand: Like “just-in-time delivery,” information is retrieved only when needed. 4. Organized note-taking: Create an external memory where the agent records what it will need later. 5. Task delegation: Divide work among specialized sub-agents to reduce load. Read Anthropic’s article on this topic: https://lnkd.in/dK_8CAZu

💡 MCP in plain terms: more than function calling Heard about MCP (Model Context Protocol) and thought, “isn’t that just function calling under a new label?” Close, but not quite. Function calling is one model talking to one API; however, MCP is the standard bus that lets any model plug into many tools or data sources. We can even plug into other agents cleanly and consistently. ⚙️ How is it MCP different? 🔹 Plugins are one-off integrations; contracts vary; security varies 🔹 RPC/REST are solid pipes; they assume humans orchestrate the logic 🔹 Function calling is smart inside one model’s sandbox 🔹 MCP turns tools into discoverable, versioned, interoperable resources: 💡 It’s the control plane for your AI ecosystem. Think governance, observability, and scale; not just syntax. When multiple agents need to research → summarize → code → deploy, MCP keeps context straight and makes sure everyone’s playing by the same rules. 🎯 Why does it matter? We’re shifting from model-centric to system-centric AI; the game isn’t just great prompts anymore, it’s designing a reliable ecosystem of reasoning with traceability and guardrails. MCP is how you make that ecosystem accountable as autonomy ramps up. #AI #MCP #AgenticAI #Interoperability #SystemsThinking