Types of AI Agents Explained

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.

Lately, the term AI Agent has been popping up everywhere—but what actually makes an AI agent different from a regular chatbot or model? I came across this helpful guide that breaks it down beautifully. Here’s a simple summary in plain language: Core Principles Behind AI Agents: - Autonomy: They can act without constant human instructions. - Planning: They break big goals into small steps. - Reflection: They learn from past actions to improve. - Statefulness: They remember past conversations or tasks. - Prompting: They react to input or questions to decide what to do next. Key Capabilities That Make Agents Smart: - Task Decomposition: Breaking complex tasks into manageable pieces. - Memory Retrieval: Pulling information from memory to stay relevant. - Tool Use: Calling APIs, web browsers, or databases to get things done. - Observability: Tracking decisions and actions for transparency. Memory Types in Agents: - Short-Term Memory: Keeps track of recent conversations. - Long-Term Memory: Stores knowledge across different sessions. - Semantic Memory: Holds facts and meanings. - Procedural Memory: Remembers how to perform tasks. - Episodic Memory: Remembers past experiences or events. Different Agent Roles: - Researcher: Finds information from the web or data sources. - Planner: Breaks tasks into steps. - Executor/Coder: Performs the steps, like coding or summarizing text. Design Approaches: - Tool-Centric Agents: Rely heavily on external tools. - Model-Centric Agents: Depend more on language understanding and internal reasoning. - Many modern systems combine both for balance. How Agents Learn: They improve through feedback loops and self-reflection, making them smarter over time without constant human correction. The Agent Loop (ReAct Framework): Perceive → Plan → Act → Learn – a continuous cycle that makes agents adaptive and autonomous. There’s also a growing ecosystem of frameworks like LangChain, AutoGen, CrewAI, and others helping developers build smarter agents faster. AI agents are more than chatbots—they’re evolving toward systems that can think, plan, and act, much like human collaborators. Which of these agent concepts are you exploring in your work?

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