Compound AI Systems vs LLM Performance

𝗟𝗟𝗠 -> 𝗥𝗔𝗚 -> 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 -> 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 The visual guide explains 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.

Over the past few weeks, I validated several patterns that reveal how AI agents truly behave in production. Autonomy is impressive, but structure still delivers the most consistent results. In a traditional LLM workflow where logic and reasoning are fully orchestrated, the same model ran twice as fast and used twelve times fewer tokens than an agentic setup. Efficiency scales best when reasoning is guided, not left open-ended. When deterministic logic was moved into the orchestration layer, the agent gained flexibility, but it came at a cost: more time and higher token usage. Predictable performance, yet less efficient overall. The biggest insight came from reasoning models themselves. GPT 5, with its superior compression and contextual efficiency, outperformed GPT 4o not because it was larger, but because it reasoned more precisely. What my findings validated: For simple and well-defined use cases, LLM workflows can achieve over 99% reliability without complex agent logic. A verifier layer - a lightweight “check my work” agent, can further improve reliability and confidence. For complex, critical, or regulated processes, orchestration remains faster, cheaper, and more auditable. Autonomy sounds exciting, but it isn’t always the optimal path. The smartest systems know when to act independently and when to rely on structured reasoning. AI agents perform best within boundaries that balance adaptability with control. Use them where discovery and contextual reasoning create value. Rely on orchestration where precision, governance, and cost efficiency are non-negotiable.

🤖 𝗟𝗟𝗠𝘀 𝗔𝗿𝗲𝗻’𝘁 𝘁𝗵𝗲 𝗣𝗿𝗼𝗱𝘂𝗰𝘁. 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗔𝗿𝗲! In an insightful article by Marc Brooker, he argues that the power of AI doesn’t lie in isolated models. It lies in systems built around them. We’ve all seen the buzz around whether LLMs can 𝘀𝗼𝗹𝘃𝗲 𝗹𝗼𝗴𝗶𝗰 𝗽𝘂𝘇𝘇𝗹𝗲𝘀 like Towers of Hanoi. But here’s the real question: Can systems built with LLMs solve them? The answer is a resounding YES and it has been for several LLM generations. The 𝗿𝗲𝗮𝗹 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗶𝘀𝗻’𝘁 𝘁𝗵𝗲 𝗟𝗟𝗠 𝗮𝗹𝗼𝗻𝗲. It’s what happens when you combine it with other tools: 🔹LLM + Code Interpreter = scalable logic execution 🔹LLM + SMT Solver = formal reasoning 🔹LLM + DBs/Browsers = real-time data intelligence Even generating and running a simple count_rs() Python function becomes a powerful proof-of-concept: systems can outperform LLMs alone in cost, speed, and reliability. This isn’t a retreat from AI’s big dreams, it’s a leap forward in 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴. We’re not embedding “how we think we think.” We’re empowering AI to discover, reason, and compute using decades of 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲. This aligns with my conviction that 𝘄𝗵𝗼𝗹𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 and 𝗺𝗼𝗱𝘂𝗹𝗮𝗿𝗶𝘁𝘆 are key. The true potential of AI emerges when you thoughtfully orchestrate 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 each 𝗱𝗼𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝗶𝘁’𝘀 𝗯𝗲𝘀𝘁 𝗮𝘁. This is the foundation of 𝘁𝗵𝗲 𝗰𝘆𝗯𝗲𝗿𝗻𝗲𝘁𝗶𝗰 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. A modular, adaptive operating system where agents, tools, and human teams interact in feedback loops. LLMs become one node in a living, evolving system, not the center of it all. 𝗧𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀, 𝗶𝘁’𝘀 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. And the future of business? Cybernetic by design. 🔗 Link to the article in the comments. #AI #LLM #SystemsEngineering #CyberneticEnterprise #FutureOfWork #IntelligentSystems #MachineLearning #EnterpriseAI #LLMops #AItools