Deep Dive Into LLM System Architecture

I frequently see conversations where terms like LLMs, RAG, AI Agents, and Agentic AI are used interchangeably, even though they represent fundamentally different layers of capability. This visual guides explain how these four layers relate—not as competing technologies, but as an evolving intelligence architecture. Here’s a deeper look: 1. 𝗟𝗟𝗠 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹) This is the foundation. Models like GPT, Claude, and Gemini are trained on vast corpora of text to perform a wide array of tasks: – Text generation – Instruction following – Chain-of-thought reasoning – Few-shot/zero-shot learning – Embedding and token generation However, LLMs are inherently limited to the knowledge encoded during training and struggle with grounding, real-time updates, or long-term memory. 2. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) RAG bridges the gap between static model knowledge and dynamic external information. By integrating techniques such as: – Vector search – Embedding-based similarity scoring – Document chunking – Hybrid retrieval (dense + sparse) – Source attribution – Context injection …RAG enhances the quality and factuality of responses. It enables models to “recall” information they were never trained on, and grounds answers in external sources—critical for enterprise-grade applications. 3. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 RAG is still a passive architecture—it retrieves and generates. AI Agents go a step further: they act. Agents perform tasks, execute code, call APIs, manage state, and iterate via feedback loops. They introduce key capabilities such as: – Planning and task decomposition – Execution pipelines – Long- and short-term memory integration – File access and API interaction – Use of frameworks like ReAct, LangChain Agents, AutoGen, and CrewAI This is where LLMs become active participants in workflows rather than just passive responders. 4. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 This is the most advanced layer—where we go beyond a single autonomous agent to multi-agent systems with role-specific behavior, memory sharing, and inter-agent communication. Core concepts include: – Multi-agent collaboration and task delegation – Modular role assignment and hierarchy – Goal-directed planning and lifecycle management – Protocols like MCP (Anthropic’s Model Context Protocol) and A2A (Google’s Agent-to-Agent) – Long-term memory synchronization and feedback-based evolution Agentic AI is what enables truly autonomous, adaptive, and collaborative intelligence across distributed systems. Whether you’re building enterprise copilots, AI-powered ETL systems, or autonomous task orchestration tools, knowing what each layer offers—and where it falls short—will determine whether your AI system scales or breaks. If you found this helpful, share it with your team or network. If there’s something important you think I missed, feel free to comment or message me—I’d be happy to include it in the next iteration.

If you’re an AI engineer trying to understand how reasoning actually works inside LLMs, this will help you connect the dots. Most large language models can generate. But reasoning models can decide. Traditional LLMs followed a straight line: Input → Predict → Output. No self-checking, no branching, no exploration. Reasoning models introduced structure, a way for models to explore multiple paths, score their own reasoning, and refine their answers. We started with Chain-of-Thought (CoT) reasoning, then extended to Tree-of-Thought (ToT) for branching, and now to Graph-based reasoning, where models connect, merge, or revisit partial thoughts before concluding. This evolution changes how LLMs solve problems. Instead of guessing the next token, they learn to search the reasoning space- exploring alternatives, evaluating confidence, and adapting dynamically. Different reasoning topologies serve different goals: • Chains for simple sequential reasoning • Trees for exploring multiple hypotheses • Graphs for revising and merging partial solutions Modern architectures (like OpenAI’s o-series reasoning models, Anthropic’s Claude reasoning stack, DeepSeek R series and DeepMind’s AlphaReasoning experiments) use this idea under the hood. They don’t just generate answers, they navigate reasoning trajectories, using adaptive depth-first or breadth-first exploration, depending on task uncertainty. Why this matters? • It reduces hallucinations by verifying intermediate steps • It improves interpretability since we can visualize reasoning paths • It boosts reliability for complex tasks like planning, coding, or tool orchestration The next phase of LLM development won’t be about more parameters, it’ll be about better reasoning architectures: topologies that can branch, score, and self-correct. I’ll be doing a deep dive on reasoning models soon on my Substack- exploring architectures, training approaches, and practical applications for engineers. If you haven’t subscribed yet, make sure you do: https://lnkd.in/dpBNr6Jg ♻️ Share this with your network 🔔 Follow along for more data science & AI insights

We need less AI enthusiasts and more AI architects. Google Deepmind has dropped a gem that you can find for FREE in Google Skills website. Beyond high level overviews, this is a rigorous, university-level curriculum that forces you to confront the mathematical and structural realities of LLMs. It speaks less about the "magic" and more about the mechanics of AI. If you are looking to deepen your technical stack, here is exactly what this curriculum covers: 1️⃣ Language models architecture evolution: The courses doesn't just start with Transformers, it builds up from N-grm probabilistic models, exposing their limitations in context retention, before moving into Multilayer Perceptrons (MLPs). You learn specifically why the industry shifted, looking at the math behind backpropagation, gradients, and the bias-variance trade-off. 2️⃣ Data Representation is Destiny One of the most valuable modules focuses on what happens before the model trains. You go deep into Tokenization strategies (Character vs. Subword/BPE) and Vector Embeddings. You learn that how you represent language data, and the biases inherent in that representation, dictates the model's capabilities (and failures) in low-resource languages. 3️⃣ Demystifying the Transformer We all use Transformers, but can you build the attention mechanism from scratch? This course breaks down the Self-Attention and Masked Multi-Head Attention layers, visualizing how context is weighed and how positional embeddings allow the model to understand sequence without recurrence. 4️⃣ Research Responsibility Crucially, DeepMind integrates ethics into the engineering pipeline, not as a sidebar. You learn to use Data Cards for transparency and evaluate the sociological impact of the models you build. If you want to move from "using" AI to "researching" and "building" AI, this is the foundational knowledge you need. It’s challenging, code-heavy, and absolutely worth your time. #google #deepmind #gemini #ai

I wasted months trying to understand “how LLMs actually work” by jumping between papers, blogs, and half-baked diagrams. Terrible way to learn. Too theoretical. Too fragmented. No intuition. Then I watched Andrej Karpathy’s 𝘋𝘦𝘦𝘱 𝘋𝘪𝘷𝘦 𝘪𝘯𝘵𝘰 𝘓𝘓𝘔𝘴 𝘭𝘪𝘬𝘦 𝘊𝘩𝘢𝘵𝘎𝘗𝘛. This lecture gives a clearer mental model of LLMs than most full courses. Here’s why it’s different: ↳ It shows the real progression: 𝗯𝗮𝘀𝗲 𝗺𝗼𝗱𝗲𝗹 → 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 → 𝗿𝗲𝗮𝘀𝗼𝗻𝗲𝗿. ↳ Makes the 𝗱𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 concrete: filters, PII removal, dedup. ↳ Frames 𝘁𝗼𝗸𝗲𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 + 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 as the core architectural limits. ↳ Separates 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲, 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝘂𝗿, 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 cleanly. ↳ Exposes the model’s 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 𝗺𝗼𝗱𝗲𝘀: hallucinations, gaps, refusals, and why tool use matters. The kicker? It gives more usable intuition than weeks of fragmented reading. 𝗧𝗼𝗽𝗶𝗰𝘀 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗺𝗮𝘁𝘁𝗲𝗿 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗲𝗿𝘀: • The 𝗣𝗿𝗲-𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 → 𝗦𝗙𝗧 → 𝗥𝗟/𝗥𝗟𝗛𝗙 stack and what each stage really adds. • How 𝗱𝗮𝘁𝗮 𝗰𝘂𝗿𝗮𝘁𝗶𝗼𝗻 defines the entire parametric knowledge base. • Why 𝘁𝗼𝗸𝗲𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 + 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝘄𝗶𝗻𝗱𝗼𝘄 shape compression and reasoning depth. • 𝗪𝗲𝗶𝗴𝗵𝘁𝘀 𝗮𝘀 𝗹𝗼𝘀𝘀𝘆 𝗺𝗲𝗺𝗼𝗿𝘆, 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗮𝘀 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗺𝗲𝗺𝗼𝗿𝘆, and why retrieval + tools outperform raw parameters. • Hallucination as a 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗮𝗿𝘁𝗶𝗳𝗮𝗰𝘁, mitigated by self-knowledge probes and tool execution. • Models need “𝘁𝗼𝗸𝗲𝗻𝘀 𝘁𝗼 𝘁𝗵𝗶𝗻𝗸”: multi-step reasoning isn’t optional. • The 𝗦𝘄𝗶𝘀𝘀-𝗰𝗵𝗲𝗲𝘀𝗲 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗽𝗿𝗼𝗳𝗶𝗹𝗲: superhuman patches next to sharp failures. Most people chase paper summaries and parameter-count hype. Real intuition, the kind that lets you 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, not slides, comes from understanding this lifecycle. Full lecture: https://lnkd.in/gWHmWPtN ♻️ Repost to help someone escape “random AI paper rabbit holes”.

Here is a clean, modern way to explain how today’s GPT-style LLMs actually work under the hood. This new diagram — generated using NanoBanana Pro — captures the 2025 architecture that powers models like GPT-4-class, Llama-3, Claude-3, and Gemini Ultra. It highlights the components that matter in real production systems: • Subword tokenization + embeddings • Rotary Positional Embeddings (RoPE) applied directly inside Q/K • KV Cache for fast inference (no full-sequence recomputation) • Pre-LayerNorm transformer blocks • Parallel residual paths (Self-Attention + Gated MLP) • Modern GEGLU / SwiGLU feed-forward networks • Accurate attention math: Q = XWq, K = XWk, V = XWv • Updated decoding strategies: temperature, top-k, nucleus sampling, repetition penalty • Optional multi-token prediction, now appearing in cutting-edge models This is the architecture behind the systems we build, deploy, and optimize today — from Copilot-style assistants to enterprise-grade inference pipelines. Sharing the graphic here for anyone teaching, learning, or building with modern LLMs. Happy to share the prompt or create custom versions focused on inference, training, or optimization. #LLM #NanoBanana #Pro #Gemini #AI #Tools #Learning

LLM inference is not prompt → model → response. It is a runtime system. While working on the next part of my series Architecting LLM Inference, I drew this diagram to simplify what actually happens inside a modern LLM runtime inference engine. The detailed article is still in progress, but I thought this visual might be useful to share meanwhile. Most people think LLM inference is: prompt → model → response But production inference is much more than a simple model.forward() call. A runtime like vLLM, TensorRT-LLM, SGLang, or TGI is really a serving engine that coordinates: request processing and tokenization queueing and runtime scheduling prefill/decode batching KV cache memory management GPU worker execution attention, MLP, and logits computation sampling and stop-condition checks detokenization and streaming And when tools or agents are involved, the runtime story expands further. The model may emit a tool-call instruction, but the application or agent layer usually parses it, validates it, executes the tool, and may call the model again with the tool result. The key mental model: Scheduler decides what runs. KV Cache Manager decides where attention memory lives. GPU Workers run the transformer math. Sampler chooses the next token. Tool execution usually sits outside the runtime. This is why LLM inference is not just an ML problem anymore. It is a systems problem involving distributed serving, GPU memory management, batching, runtime scheduling, kernel optimization, streaming, and agent orchestration. Detailed article coming soon on my Substack (link in comments) as part of the Architecting LLM Inference series. Subscribe if you’re interested in deep dives on LLM runtimes, KV cache, batching, and inference optimization. Would love to hear from folks building inference platforms:

A new paper from Technical University of Munich and Universitat Politècnica de Catalunya Barcelona explores the architecture of autonomous LLM agents, emphasizing that these systems are more than just large language models integrated into workflows. Here are the key insights:- 1. Agents ≠ Workflows Most current systems simply chain prompts or call tools. True agents plan, perceive, remember, and act, dynamically re-planning when challenges arise. 2. Perception Vision-language models (VLMs) and multimodal LLMs (MM-LLMs) act as the 'eyes and ears', merging images, text, and structured data to interpret environments such as GUIs or robotics spaces. 3. Reasoning Techniques like Chain-of-Thought (CoT), Tree-of-Thought (ToT), ReAct, and  Decompose, Plan in Parallel, and Merge (DPPM) allow agents to decompose tasks, reflect, and even engage in self-argumentation before taking action. 4. Memory Retrieval-Augmented Generation (RAG) supports long-term recall, while context-aware short-term memory maintains task coherence, akin to cognitive persistence, essential for genuine autonomy. 5. Execution This final step connects thought to action through multimodal control of tools, APIs, GUIs, and robotic interfaces. The takeaway? LLM agents represent cognitive architectures rather than mere chatbots. Each subsystem, perception, reasoning, memory, and action, must function together to achieve closed-loop autonomy. For those working in this field, this paper titled 'Fundamentals of Building Autonomous LLM Agents' is an interesting reading:- https://lnkd.in/dmBaXz9u #AI #AgenticAI #LLMAgents #CognitiveArchitecture #GenerativeAI #ArtificialIntelligence

LLM inferencing at scale involves finding the right combination of hardware, software, drivers, kernels, and routing. vLLM handles the engine layer. But when you scale to multiple replicas, a standard load balancer round-robins blind. It has no idea which pod already has your prompt prefix cached or which one has a full queue. That's the gap llm-d fills with its EPP (Endpoint Policy Processor).                                 I put together a 6-part hands-on series that walks through the full stack, running on a MacBook with no GPU needed:                                                               ➡️ Part 1: Deploy vLLM on a local Kubernetes cluster ➡️ Part 2: Add the llm-d gateway and EPP routing layer ➡️ Part 3: Scale to 3 replicas and observe load distribution ➡️ Part 4: Model aliasing with InferenceModelRewrite ➡️ Part 5: Fault tolerance: delete a pod mid-traffic and watch recovery ➡️ Part 6: Scrape EPP Prometheus metrics and watch pool size change live                                 Full series with architecture diagram here: https://lnkd.in/gecdtpSw                                 #llmd #Kubernetes #LLMInference #vLLM #OpenSource #GenAI 

If you’re serious about being an AI-first (not AI-curious) company here’s what that actually takes: 1️⃣ Start with systems, not slogans. A beautiful paper won’t save you when your API gateway crashes under load. Agentic and RAG-based systems need real-world scaffolding — FastAPI, CI/CD, DevOps excellence. Because you can’t amplify human potential on top of brittle infrastructure. 2️⃣ Redefine what “agents” mean. They’re not chatbots with longer memories. Real agents execute, plan, remember, and recover with fallback logic, tool orchestration, and guardrails. The question isn’t “Can it answer?” It’s “Can it fail safely at 2 a.m. when finance systems go dark?” 3️⃣ RAG isn’t about vectors — it’s about validation. Enterprise knowledge is messy. The retrieval layer is the intelligence layer. Chunking, hybrid search, reranking, and evaluation pipelines — this is where critical thinking lives in the machine. Most RAG systems don’t fail loudly; they fail quietly because no one’s asking the right questions. 4️⃣ LLM system design is its own discipline. We’ve graduated past prompt engineering. Now it’s about composition: How models, tools, memory, and decision logic interact — monitored, debugged, and deployed as living systems. That’s the architecture of amplification, not automation. 5️⃣ Deployment is the differentiator. Demos don’t have cost budgets, latency constraints, or legacy dependencies. Production does. Anyone can prototype. Few can operationalize. The future belongs to those who can ship responsibly, securely, and repeatedly — at scale. The companies pulling ahead aren’t the ones with the flashiest models or the biggest teams. They’re the ones designing systems that extend human capability — not replace it. They’re treating LLMs like infrastructure. They’re integrating agents into workflows, not chat windows. They’re building what I call “Human Amplification Systems” — where technology scales thinking, not just throughput. Because the real AI race isn’t about intelligence. It’s about critical thinking at scale — and how we use machines to magnify it, not mute it.

RAG is not “LLM + vector database.” RAG is a retrieval pipeline that helps the model answer from your own knowledge instead of guessing from memory. The architecture is simple when you break it into layers. 1. Data Sources Layer Knowledge starts with PDFs, docs, wikis, tickets, product docs, databases, and metadata. If your sources are messy or stale, your answers will be weak. 2. Ingestion & Processing Layer Raw content has to become usable. This means connectors, parsing, OCR, cleaning, metadata extraction, and document versioning. This layer tells the system what the document is and when it changed. 3. Chunking Layer This is where many RAG systems fail. You decide how content is split: fixed chunks, semantic chunks, overlap, parent-child chunks, metadata tags, and context boundaries. Chunk too small, and meaning gets lost. Chunk too large, and retrieval gets noisy. 4. Embedding Layer Chunks become vectors so similar meanings sit close together. This includes model selection, vector generation, refresh strategy, and multi-language support. The goal is not to embed everything. The goal is to preserve searchable meaning. 5. Vector & Document Store This is where retrieved knowledge lives. You store vectors, raw chunks, metadata, source links, and freshness signals. The vector index finds similar content. The document store gives real context. 6. Retrieval Layer When a user asks a question, the system finds context using vector search, keyword search, hybrid search, metadata filters, top-k retrieval, and query rewriting. Strong RAG is usually hybrid because vector-only search can miss exact terms. 7. Core Flow The flow is: Ingest → Chunk → Embed → Index → Retrieve → Rerank → Generate → Evaluate Retrieval finds candidates. Reranking improves precision. Generation answers from selected context. 8. Generation, Evaluation & Safety Layer The LLM gets the query plus retrieved context. This layer handles prompts, context limits, citations, fallback behavior, grounding checks, hallucination checks, latency, cost, access control, PII handling, source trust, and auditability. RAG without evaluation is just vibes with a vector database. The simplest way to remember it: RAG is not one component. It is a full knowledge retrieval system. That is why interviewers ask about chunk size, hybrid search, freshness, reranking, latency, hallucinations, and answer quality. Building a demo RAG app is easy. Building one people can trust is the real skill.