Understanding Cognitive Architectures in AI

Mapping Brains to Agents: Cognitive Evolution of LLMs Human intelligence as we grow up doesn't emerge in a day, it takes years and evolves in layers: 1) Memory (like RAG): We learn to recall knowledge—not everything is stored in working memory. We pick up books, search references, and pull the right fact at the right time. 2) Tool Use (like MCP & Function Calling): We extend our cognition by using tools—rulers, microscopes, calculators, laptops—enhancing our mental limits with external interfaces. 3) Agency and ownership (like Agentic Workflows): We show intent, delegate tasks, adapt, and build systems of self-reflection and self-correction. We’re now watching LLMs evolve along the same axis. LLM intelligence = Human Cognitive Infrastructure = Base_Model + RAG + Tools(MCP) + Agents(Planning + Memory + Goal-Oriented Execution) Just like humans didn’t stop at memory recall, LLMs didn't stop at RAG. True transformation will when LLMs reason, retrieve, use tools, and act with autonomy. The future of enterprise AI won't be built on static chatbots—it'll be shaped by agentic AI, mirroring the very way humans evolved intelligence. 🔄 If your LLM is still just answering questions, it’s in its "prefrontal cortex loading..." / "early development" phase. We all will be forced to soon push the envelope. #RAG #MCP #Agents #EnterpriseAI #LLMOps

🤖 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

If you’re serious about building AI agents, start here 👇 To design agents that actually reason, plan, and act. You need to understand the spectrum of agent architectures. Here are 9 types of AI agents every AI engineer should know: 💡 1. Simple Reflex Agents React to current input using rule-based logic (IF-THEN). → No memory or learning. Example: A thermostat that turns on if temp < 20°C. 💡 2. Model-Based Reflex Agents Use an internal model of the world to infer hidden state. → Enable smarter decisions in partially observable settings. Example: A robot navigating a known room layout to avoid collisions. 💡 3. Goal-Based Agents Act to achieve defined objectives. → Involve search, planning, and pathfinding. Example: Navigation system choosing optimal route to destination. 💡 4. Utility-Based Agents Maximize a utility function (e.g. safety, speed, efficiency). → Handle trade-offs between competing outcomes. Example: A self-driving car balancing passenger comfort with urgency. 💡 5. Learning Agents Improve via feedback—using RL, supervised learning, or both. → Adapt and evolve over time. Example: Chess agent refining tactics through self-play. 💡 6. Multi-Agent Systems (MAS) Multiple agents interact, collaborate, or compete. → Used in swarm robotics, distributed simulations, and games. Example: Financial agents negotiating trades in a simulated economy. 💡 7. Agentic AI Systems (LLM-based agents) This is where it gets powerful. → Built on large language models + tools + memory + control flow. → Can decompose tasks, call APIs, search docs, invoke sub-agents. Examples: Autogen, CrewAI, LangGraph. 💡 8. Embodied Agents Agents with a physical presence—robots, drones, etc. → Interact with the real world via sensors and actuators. Example: Boston Dynamics Spot navigating stairs autonomously. 💡 9. Cognitive & Conversational Agents Human-facing agents designed for natural language interaction. → Chatbots, virtual assistants, AI tutors. Examples: ChatGPT, Alexa, Claude. If you’re an engineer, don’t just study these, build them. Get hands-on with open-source agent stacks: → Autogen (multi-agent orchestration): https://lnkd.in/d36cU42f → CrewAI (LLM agents with roles + memory): https://lnkd.in/dHBCPmkX → LangGraph (agentic control flow, built on LangChain): https://docs.langgraph.dev Start with their cookbooks or clone a working repo. The fastest way to understand agents… is to ship one. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and 🔔 subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg