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

Excited to announce my new (free!) white paper: “Self-Improving LLM Architectures with Open Source” – the definitive guide to building AI systems that continuously learn and adapt. If you’re curious how Large Language Models can critique, refine, and upgrade themselves in real-time using fully open source tools, this is the resource you’ve been waiting for. I’ve put together a comprehensive deep dive on: Foundation Models (Llama 3, Mistral, Google Gemma, Falcon, MPT, etc.): How to pick the right LLM as your base and unlock reliable instruction-following and reasoning capabilities. Orchestration & Workflow (LangChain, LangGraph, AutoGen): Turn your model into a self-improving machine with step-by-step self-critiques and automated revisions. Knowledge Storage (ChromaDB, Qdrant, Weaviate, Neo4j): Seamlessly integrate vector and graph databases to store semantic memories and advanced knowledge relationships. Self-Critique & Reasoning (Chain-of-Thought, Reflexion, Constitutional AI): Empower LLMs to identify errors, refine outputs, and tackle complex reasoning by exploring multiple solution paths. Evaluation & Feedback (LangSmith Evals, RAGAS, W&B): Monitor and measure performance continuously to guide the next cycle of improvements. ML Algorithms & Fine-Tuning (PPO, DPO, LoRA, QLoRA): Transform feedback into targeted model updates for faster, more efficient improvements—without catastrophic forgetting. Bias Amplification: Discover open source strategies for preventing unwanted biases from creeping in as your model continues to adapt. In this white paper, you’ll learn how to: Architect a complete self-improvement workflow, from data ingestion to iterative fine-tuning. Deploy at scale with optimized serving (vLLM, Triton, TGI) to handle real-world production needs. Maintain alignment with human values and ensure continuous oversight to avoid rogue outputs. Ready to build the next generation of AI? Download the white paper for free and see how these open source frameworks come together to power unstoppable, ever-learning LLMs. Drop a comment below or send me a DM for the link! Let’s shape the future of AI—together. #AI #LLM #OpenSource #SelfImproving #MachineLearning #LangChain #Orchestration #VectorDatabases #GraphDatabases #SelfCritique #BiasMitigation #Innovation #aiagents

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

Most LLM agents today still behave like procedural systems. They follow a linear plan, call predefined tools, and lose their context after each interaction. The approach works for narrow tasks but fails in open environments where the number of possible actions grows exponentially. DeepAgent proposes a very different architecture that merges reasoning, tool discovery, and execution into a single continuous loop. It is not another workflow framework but a shift toward cognitive automation, where the model plans, acts, and learns within the same reasoning space. The core of the design lies in two mechanisms: 1. The first, called autonomous memory folding, creates a structured memory system that stores and compresses reasoning traces into episodic, working, and tool memories. The agent can recall earlier decisions, detect when its logic begins to diverge, and replan without restarting the entire process. It removes the blind spot that limits most current agents, which optimize locally without remembering why a previous path failed. 2. The second mechanism, Tool Policy Optimization or ToolPO, redefines how agents learn to use external tools. It replaces fragile, slow feedback from real APIs with a simulated tool environment and assigns credit to each intermediate decision, not just the final outcome. This allows the model to refine its tool use policy through reinforcement learning that is both faster and more stable. The results are significant. On complex reasoning benchmarks such as GAIA and ALFWorld, DeepAgent delivers 20 to 30 percent higher success rates than prior architectures like ReAct or Plan-and-Solve. It continues to improve as the reasoning chain lengthens and the number of tools increases, rather than collapsing when complexity grows. This scaling behavior is important because it hints at an emerging capability: agents that can generalize across tool ecosystems and adapt to previously unseen APIs. However, the trade-offs are real. DeepAgent is computationally heavy to train, and its autonomous behavior is more difficult to monitor or reproduce. Debugging a system that can rediscover and reprioritize tools mid-reasoning is fundamentally different from tracing a fixed workflow. Still, the architectural direction feels inevitable. Future agents will no longer separate planning, execution, and learning. Memory, reasoning, and action will operate in one continuous loop. For organizations, this means moving from process automation to policy design, defining how much autonomy to grant, how to constrain exploration, and how to measure reliability when reasoning is no longer step by step but self-evolving. DeepAgent is an early view of that future, where agents begin to reason through tools, not around them, and the boundary between cognition and execution starts to disappear.

I started by asking AI to do everything. Six months later, 65% of my agent’s workflow nodes run as non-AI code. The first version was fully agentic : every task went to an LLM. LLMs would confidently progress through tasks, though not always accurately. So I added tools to constrain what the LLM could call. Limited its ability to deviate. I added a Discovery tool to help the AI find those tools. Better, but not enough. Then I found Stripe’s minion architecture. Their insight : deterministic code handles the predictable ; LLMs tackle the ambiguous. I implemented blueprints, workflow charts written in code. Each blueprint specifies nodes, transitions between them, trigger conditions for matching tasks, & explicit error handling. This differs from skills or prompts. A skill tells the LLM what to do. A blueprint tells the system when to involve the LLM at all. Each blueprint is a directed graph of nodes. Nodes come in two types : deterministic (code) & agentic (LLM). Transitions between nodes can branch based on conditions. Deal pipeline updates, chat messages, & email routing account for 29% of workflows, all without a single LLM call. Company research, newsletter processing, & person research need the LLM for extraction & synthesis only. Another 36%. The workflow runs 67-91% as code. The LLM sees only what it needs : a chunk of text to summarize, a list to categorize, processed in one to three turns with constrained tools. Blog posts, document analysis, bug fixes are genuinely hybrid. 21% of workflows. Multiple LLM calls iterate toward quality. Only 14% remain fully agentic. Data transforms & error investigations. These tend to be coding tasks rather than evaluating a decision point in a workflow. The LLM needs freedom to explore. AI started doing everything. Now it handles routing, exceptions, research, planning, & coding. The rest runs without it. Is AI doing less? Yes. Is the system doing more? Also yes. The blueprints, the tools, the skills might be temporary scaffolding. With each new model release, capabilities expand. Tasks that required deterministic code six months ago might not tomorrow.

Building LLM Agent Architectures on AWS - The Future of Scalable AI Workflows What if you could design AI agents that not only think but also collaborate, route tasks, and refine results automatically? That’s exactly what AWS’s LLM Agent Architecture enables. By combining Amazon Bedrock, AWS Lambda, and external APIs, developers can build intelligent, distributed agent systems that mirror human-like reasoning and decision-making. These are not just chatbots - they’re autonomous, orchestrated systems that handle workflows across industries, from customer service to logistics. Here’s a breakdown of the core patterns powering modern LLM agents : Breakdown: Key Patterns for AI Workflows on AWS 1. Prompt Chaining / Saga Pattern Each step’s output becomes the next input — enabling multi-step reasoning and transactional workflows like order handling, payments, and shipping. Think of it as a conversational assembly line. 2. Routing / Dynamic Dispatch Pattern Uses an intent router to direct queries to the right tool, model, or API. Just like a call center routing customers to the right department — but automated. 3. Parallelization / Scatter-Gather Pattern Agents perform tasks in parallel Lambda functions, then aggregate responses for efficiency and faster decisions. Multiple agents think together — one answer, many minds. 4. Saga / Orchestration Pattern Central orchestrator agents manage multiple collaborators, synchronizing tasks across APIs, data sources, and LLMs. Perfect for managing complex, multi-agent projects like report generation or dynamic workflows. 5. Evaluator / Reflect-Refine Loop Pattern Introduces a feedback mechanism where one agent evaluates another’s output for accuracy and consistency. Essential for building trustworthy, self-improving AI systems. AWS enables modular, event-driven, and autonomous AI architectures, where each pattern represents a step toward self-reliant, production-grade intelligence. From prompt chaining to reflective feedback loops, these blueprints are reshaping how enterprises deploy scalable LLM agents. #AIAgents

𝗜𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗺𝗮𝗸𝗶𝗻𝗴 𝗔𝗜 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀, 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗵𝗼𝘄 𝗺𝗼𝗱𝗲𝗹𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘁𝗵𝗲 𝗵𝗲𝗮𝗱𝗹𝗶𝗻𝗲𝘀. Andrej Karpathy’s 3.5-𝗵𝗼𝘂𝗿 𝗱𝗲𝗲𝗽 𝗱𝗶𝘃𝗲 breaks down LLMs from pretraining to fine-tuning to reinforcement learning, helping leaders and non-technical stakeholders grasp 𝘄𝗵𝗲𝗿𝗲 𝗔𝗜 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘀 𝘃𝗮𝗹𝘂𝗲, 𝘄𝗵𝗲𝗿𝗲 𝗶𝘁 𝗳𝗮𝗹𝗹𝘀 𝘀𝗵𝗼𝗿𝘁, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝗺𝗮𝗸𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗯𝗲𝘁𝘀. 𝗛𝗲𝗿𝗲’𝘀 𝗪𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: ✅ 𝗕𝗮𝘀𝗶𝗰 𝘁𝗮𝘀𝗸𝘀 (summarization, insights) → Solvable with 𝗦𝗙𝗧 + 𝗥𝗔𝗚, 𝗯𝘂𝘁 𝗥𝗔𝗚 𝗶𝘀 𝗮 𝗱𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗳𝗶𝘅, 𝗻𝗼𝘁 𝗮 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗹𝗲𝗮𝗽. ✅ 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 → True differentiation comes from 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀, not just RAG or SFT. Your AI apps need to 𝗺𝗶𝗺𝗶𝗰 (𝗼𝗿 𝗼𝘂𝘁𝗽𝗲𝗿𝗳𝗼𝗿𝗺) 𝗦𝗠𝗘𝘀 in decision-making. ⚠️ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲𝗻’𝘁 𝘂𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝗻𝗲𝘄 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴, they’re just improving data access. Be intentional: Do you need stochastic decision-making, or is a structured workflow enough? 🔗 𝗪𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝘃𝗶𝗱𝗲𝗼: https://lnkd.in/gFFYxym3 +  U𝘀𝗲 𝘁𝗵𝗲𝘀𝗲 𝘁𝗼𝗼𝗹𝘀 to help visualize AI’s full processing logic, from tokenization to decision-making, so you can spot where reasoning works and where it fails: 📌 Tokenization → https://lnkd.in/gHMPREfD 📌 Visualize Datasets → atlas.nomic.ai  📌 See LLM Architecture Flows → https://bbycroft.net/llm 📌 Bonus: Understand Transformer Steps → https://lnkd.in/g7P-C4HJ