{
  "id": 16056496,
  "title": "Examples of Intelligent Agents in AI: The Five Types, Real-World Examples, and How Agent Memory Fits In",
  "url": "https://urgent.news/2026/10/22/examples-of-intelligent-agents-in-ai-the-five-types-real-world",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-22T14:38:37.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/open-agentic-brain-and-memory/examples-of-intelligent-agents-in-ai-the-five-types-real-world-examples-and-how-agent-memory-e87"
  },
  "original_language": "en",
  "account": "An intelligent agent is a system that perceives its environment, makes decisions, and takes actions to achieve goals. Examples of intelligent agents in AI include a thermostat, a robot vacuum, a self-driving car, a trading system, AlphaGo, and coding agents. Stuart Russell and Peter Norvig define an agent as anything that perceives its environment through sensors and acts on it through actuators, with the loop being perceive, decide, act, repeat.\n\nRussell and Norvig categorize intelligent agents into five types, from simplest to most capable:\n\n1. Simple reflex agents: such as a thermostat, which has no history or model of the environment.\n2. Model-based reflex agents: like a robot vacuum, which maintains an internal map of its surroundings.\n3. Goal-based agents: like navigation systems and self-driving cars, which have a specific goal and decide on the best route to achieve it.\n4. Utility-based agents: such as trading and recommendation systems, which weigh trade-offs between different goals.\n5. Learning agents: like AlphaGo, which improves its performance through experience.\n\nIn real-world AI applications, these types often combine. For example, a coding agent like Claude Code or Copilot may start with simple reflex actions but can learn from experience and incorporate memory to improve its performance over time.\n\nHowever, most language models used in AI do not retain information between sessions, as they have no internal memory. To address this, memory can be added as a separate layer. Open Agentic Brain & Memory (OpenAB) is an open-source, self-hosted memory system that can be integrated with various AI agents. It stores decisions, fixes, and preferences from sessions, allowing information to persist across interactions. This approach enables agents to build upon previous work and adapt to user preferences, providing a more cohesive and efficient AI experience.",
  "summary": "What is an intelligent agent in AI? An intelligent agent is a system that perceives its environment, decides what to do, and acts toward a goal. The best examples of intelligent agents in AI are the ones you can already point to: a thermostat, a robot vacuum, a self-driving car, a trading system, AlphaGo, and the coding agents many of us now use daily. The framing comes from Stuart Russell and…",
  "key_points": [
    "Intelligent agents perceive environment, make decisions, take actions",
    "Five types of agents: simple reflex, model-based reflex, goal-based, utility-based, learning",
    "Memory can be added to AI agents for persistence across sessions"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}