Understanding Compound AI System Agents

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, here are the 15 components of agentic AI you should know. Building truly agentic systems goes far beyond chaining prompts or wiring tools. It requires modular intelligence that can perceive, plan, act, learn, and adapt across dynamic environments - autonomously and reliably. This framework breaks it down into 15 technical components: 🔴 1. Goal Formulation → Agents must define explicit objectives, decompose them into subgoals, prioritize execution, and adapt dynamically as new context arises. 🟣 2. Perception → Real-time sensing across modalities (text, visual, audio, sensors) with uncertainty estimation and context grounding. 🟠 3. Cognition & Reasoning → From world modeling to causal inference, agents need inductive, abductive reasoning, planning, and introspection via structured knowledge (graphs, ontologies). 🔴 4. Action Selection & Execution → This includes policy learning, planning, trial-and-error correction, and UI/tool interfacing to interact with real systems. 🟣 5. Autonomy & Self-Governance → Independence from human-in-the-loop oversight through constraint-aware, initiative-taking decision frameworks. 🟠 6. Learning & Adaptation → Support for continual learning, transfer learning, and meta-learning with feedback-driven self-improvement loops. 🔴 7. Memory & State Management → Episodic memory, working memory buffers, and semantic grounding for contextually-aware actions over time. 🟣 8. Interaction & Communication → Natural language generation and understanding, negotiation, and multi-agent coordination with social signal processing. 🟠 9. Monitoring & Self-Evaluation → Agents should monitor their own performance, detect anomalies, benchmark against goals, and recover autonomously. 🔴 10. Ethical and Safety Control → Safety constraints, transparency, explainability, and alignment to human values - non-negotiable for real-world deployment. 🟣 11. Resource Management → Optimizing compute, memory, and energy with intelligent resource scheduling and infrastructure-aware orchestration. 🟠 12. Persistence & Continuity → Agents must preserve goal state across sessions, maintain behavioral consistency, and recover from disruptions. 🔴 13. Agency Integration Layer → Modular architecture, orchestration of internal components, and hierarchical control systems for scalable design. 🟣 14. Meta-Agent Capabilities → Delegation to sub-agents, participation in agent collectives, and orchestration of agent teams with diverse roles. 🟠 15. Interface & Environment Adaptability → Adaptation across domains and tools with robust APIs and reconfigurable sensing-actuation layers. 〰️〰️〰️ 🔁 Save and share this if you’re designing agents beyond the demo stage. 🔔 Follow me (Aishwarya Srinivasan) for more data & AI insights

Everyone is talking about AI agents, but very few people actually break down the technical architecture that makes them work. To make sense of it, I put together the 7-layer technical architecture of agentic AI systems. Think of it as a stack where each layer builds on top of the other, from the raw infrastructure all the way to the applications we interact with. 1. Infrastructure and Execution Environment This is the foundation. It includes APIs, GPUs, TPUs, orchestration engines like Airflow or Prefect, monitoring tools like Prometheus, and cloud storage systems such as S3 or GCS. Without this base, nothing else runs. 2. Agent Communication and Networking Once you have infrastructure, agents need to talk to each other and to the environment. This layer covers frameworks for multi-agent systems, memory management (short-term and long-term), communication protocols, embedding stores like Pinecone, and action APIs. 3. Protocol and Interoperability This is where standardization comes in. Protocols like Agent-to-Agent (A2A), Model Context Protocol (MCP), Agent Negotiation Protocol (ANP), and open gateways allow different agents and tools to interact in a consistent way. Without this layer, you end up with isolated systems that cannot coordinate. 4. Tool Orchestration and Enrichment Agents are powerful because they can use tools. This layer enables retrieval-augmented generation, vector databases such as Chroma or FAISS, function calling through LangChain or OpenAI tools, web browsing modules, and plugin frameworks. It is what allows agents to enrich their reasoning with external knowledge and execution capabilities. 5. Cognitive Processing and Reasoning This is the brain of the system. Agents need planning engines, decision-making modules, error handling, self-improvement loops, guardrails, and ethical AI mechanisms. Without reasoning, an agent is just a connector of inputs and outputs. 6. Memory Architecture and Context Modeling Intelligent behavior requires memory. This layer includes short-term and long-term memory, identity and preference modules, emotional context, behavioral modeling, and goal trackers. Memory is what allows agents to adapt and become more effective over time. 7. Intelligent Agent Application Finally, this is where it all comes together. Applications include personal assistants, content creation tools, e-commerce agents, workflow automation, research assistants, and compliance agents. These are the systems that people and businesses actually interact with, built on top of the layers below. When you put these seven layers together, you can see agentic AI not as a single tool but as an entire ecosystem. Each layer is necessary, and skipping one often leads to fragile or incomplete solutions. ---- ✅ I post real stories and lessons from data and AI. Follow me and join the newsletter at www.theravitshow.com

🤖 What does it really take to build an intelligent agent? Most people stop at the LLM. But real agents, ones that can think, reason, act, and learn, require much more than clever prompts. This framework breaks it down into 7 essential layers that power autonomous systems: 🔹 Experience Layer – The human interface: where users interact with the agent 🔍 Discovery Layer – How the agent gathers relevant information and context 🧠 Agent Composition Layer – Defines structure, goals, and behaviors 🗺️ Reasoning & Planning Layer – The agent’s "brain" for logic and decision-making 🛠️ Tool & API Layer – How agents act: calling APIs, running workflows, executing code 🧠💾 Memory & Feedback Layer – Enables learning, feedback, and contextual recall 🏗️ Infrastructure Layer – Scales everything: compute, orchestration, and security 💡 If you're serious about building real-world AI agents, you need more than an LLM—you need a system. A must-know mental model for founders, developers, and product leaders shaping the future of AI. #AIagents #LLM #AgentArchitecture #AutonomousAI #AgentFramework #AIproduct #AIengineering #MCP #RAG #ReasoningAI #AIinfrastructure #LLMOps #FutureOfAI #AIstrategy

Before you start building AI agents, it’s important to understand the core components they’re built on. AI agents are more than just clever wrapping around language models. Behind every powerful, goal-driven agent is a carefully engineered stack of concepts that determines how agents interpret input, reason through tasks, interact with external tools, and make safe decisions in real-world environments. Here’s a break down of 16 essential concepts every builder, product leader, and AI enthusiast should know: - Attention & Memory : How agents focus and recall important context. - Tokenization & Embeddings : Turning raw input into numerical formats machines understand. - Transformers : The core engine powering modern LLMs. - Prompt Engineering : Crafting inputs to get the best outputs. - Retrieval-Augmented Generation (RAG) : Enhancing responses with external, up-to-date knowledge. - Safety Guardrails : Ensuring your agent remains ethical, safe, and policy-compliant. - Fine-Tuning & Planning : Specializing agents and breaking down tasks into steps. - Multi-Modal Inputs : Going beyond text to handle images, audio, or video. - Tool Use & Orchestration : Letting agents perform actions through APIs and coordinating workflows. - Reasoning Chains : Techniques like CoT & ReAct for logical thinking and decision-making. - Frameworks & Deployment : Delivering and scaling agents in real-world environments. To build agents that are not just functional but also dependable, adaptable, and scalable, you need to master the ecosystem that powers them, from transformers and embeddings to memory, orchestration, and guardrails. Have I missed anything? #AIAgents

🚀 Anatomy of AI Agents: Structure, Layers, and Systemic Intelligence 🧠🤖 We’re entering a new era in AI. What started as predictive modeling has evolved into intelligent agency — where AI systems can perceive, think, plan, remember, feel, and act autonomously. But how do these agents actually work? 🧩AI Agents Core Components 🧠 Cognition — Reasoning, planning, and goal decomposition through structured (trees, graphs) and unstructured (CoT, few-shot) thinking. 🧬 Memory — Hierarchical context: from short-term attention buffers to long-term procedural and episodic recall. 🌍 World Models — Internal simulations that empower foresight, imagination, and counterfactual reasoning. 🧭 Reward Systems — Both extrinsic goals and intrinsic motivations like curiosity drive learning and prioritization. 💬 Emotion Modeling — Simulated affect enables social intelligence, empathetic interaction, and tonal modulation. 👁️ Perception — Multimodal inputs (text, vision, audio) power grounding in physical and digital spaces. 🛠️ Action Systems — Execution via APIs, GUIs, robotics, and toolchains brings agency into the real world. 🔧 AI Agents Layered Architecture Input Layer → Contextual Layer → Predictive Layer → Cognition → Motivational Layer → Action These layers operate in real-time feedback loops, mirroring biological systems and enabling adaptive behavior. 💡 What’s next? Architectures that learn across time, align with values, and self-reflect — blending modularity, emergence, and goal-alignment into one coherent agentic framework. Reference Advances and Challenges in Foundation Agents: https://lnkd.in/eTPm_pBd #AIAgents #LLMs #Architecture 

Here's the difference between AI agents and agentic AI 1. Most AI tools today do one task well. That’s an AI Agent. 2. The future is systems that think, plan, and collaborate. That’s Agentic AI. 3. AI Agents are doers. Agentic AI are decision-makers. 4. AI Agents follow rules. Agentic AI writes new ones on the fly. 5. One works solo. The other is a team of AIs working together. 6. AI Agents help with email replies. Agentic AI can run your research lab. 7. Agentic AI uses memory, planning, and collaboration to handle chaos. 8. Imagine 10 AIs, each with a role, working as a team. That’s Agentic AI. 9. AI Agents are task-focused. Agentic AI is outcome-focused. 10. AI Agents get stuck in loops. Agentic AI adapts and moves on. 11. AI Agents break when context shifts. Agentic AI re-plans. 12. You can’t scale AI without memory. Agentic AI solves that. 13. The key is orchestration, getting agents to cooperate without chaos. 14. Think supply chains, hospitals, game engines, and Agentic AI fit here. 15. Chatbots aren’t intelligent systems. That’s the trap. 16. Real intelligence comes from coordination, not just generation. 17. Long-term reliability needs memory, reflex, and collaboration. 18. Agentic AI uses feedback loops: act → observe → adjust. 19. AI Agents hallucinate when out of context. Agentic AI learns from mistakes. 20. Managing Agentic AI needs governance, not just prompts. 21. The biggest challenge is coordination without confusion. 22. When your AI team argues with itself, things break. 23. Agentic AI needs transparency, traceability, and strong rules. 24. Tool use isn’t enough. The system must choose the right tool. 25. RAG, ReAct, memory layers, these are real building blocks, not buzzwords. 26. Most startups today are still building AI Agents. 27. The next wave is tools that help multiple agents work together. 28. This paper gives a roadmap for the next 5 years of AI. 29. If you’re building AI products, this is the playbook to study. 30. Don’t just build a smart agent. Build a smart system Share this with anyone unclear on AI agents vs. agentic intelligence.