Table of contents
- What is an AI agent
- Types of AI agents
- How AI agents work: the technical workflow
- Real-world examples
- Summary table
- Frequently asked questions
What is an AI agent?
An AI agent is an autonomous software system that perceives its environment, processes information, and takes actions to achieve specific goals with minimal human oversight. Unlike simpler AI models that only generate responses, AI agents can plan, act, adapt, and sometimes collaborate with other agents.
In technical terms, an AI agent combines several AI technologies—such as natural language processing, planning, learning (including reinforcement learning), tool-calling, and memory—to dynamically solve tasks.
Types of AI agents
AI agents can be categorized into several types with varying levels of sophistication:
Simple reflex agents Operate using direct condition-action rules without memory or foresight. They react to the current state of the environment and work well in predictable scenarios.
Model-based reflex agents Include an internal model of the world to track its state over time, enabling better responses in environments that are partially observable.
Goal-based agents Plan actions to achieve specific objectives. They evaluate and choose actions that move them toward a goal using reasoning and decision logic.
Utility-based agents Choose actions based on a utility function that assesses multiple possible outcomes, balancing trade-offs for optimal behavior.
Learning agents Adapt and improve over time using machine learning, reinforcement learning, or other techniques (though the exact definition may vary across sources).
Agentic AI systems (multi-agent/hybrid systems) Involve multiple agents collaborating toward complex goals, with dynamic planning, memory, and orchestration capabilities.
How AI agents work: the technical workflow
Goal initialization Humans or other systems define goals, constraints, and available tools or environments.
Perception and reasoning The agent senses its environment (via input data, APIs, sensors) and, depending on its type, builds or updates its internal world model.
Plan generation and decomposition When goals are complex, the agent breaks the task into subtasks and forms a plan or workflow.
Tool use and action execution The agent may call external tools, services, or plug-ins to execute parts of the plan.
Learning and adaptation Depending on its architecture, the agent may learn from feedback or past performance to refine its behavior.
Real-world examples
Self-driving cars Typically employ model-based reflex strategies to perceive the road, predict future states, and plan actions.
Coding agents Such as Devin AI, which autonomously plans, writes, debugs software, and can dispatch subtasks to other agents.
OpenAI operator An AI agent that interacts with the web—filling forms, ordering groceries, scheduling, all through browser automation. Released in 2025 as a research preview.
Enterprise and customer-service agents Used for tasks like scheduling, reservations, support automation, workflow orchestration, and more.
Industry agents in businesses Applications in manufacturing (e.g., Siemens predictive maintenance), finance (high-frequency trading), healthcare, retail, etc.
Summary table
| Agent type | Core characteristic | Sample use case |
|---|---|---|
| Simple reflex | Stateless condition-action rules | Thermostat |
| Model-based reflex | Uses internal model to track and react | Robotics navigation |
| Goal-based | Plans toward specific objective | Navigation systems |
| Utility-based | Optimizes action via utility function | Resource allocation systems |
| Learning | Adapts from experience | Spam filters, adaptive systems |
| Agentic AI/multi-agent | Orchestrates multiple agents for complex workflows | Manufacturing orchestration, agent teams |
Frequently asked questions
What's the difference between an AI agent and a chatbot?
A chatbot is often limited to conversational interactions and predefined responses, while an AI agent is autonomous—capable of planning, taking actions, tool integration, and adapting over time.
Are AI agents just LLMs?
Not solely. While many agents use large language models (LLMs) for reasoning or language understanding, agents additionally include planners, tool integrators, memory, and autonomy to execute multi-step tasks.
Can AI agents learn on the job?
Yes—learning agents can improve through feedback or reinforcement learning. Agentic systems may employ ongoing adaptation and memory to refine decisions and coordination.
What are the main limitations of AI agents currently?
They struggle with reliability in multi-step tasks, can hallucinate, require strong governance, are resource-intensive, and face ethical and security concerns.
What are emerging agentic AI systems?
These are multi-agent systems with orchestration, memory, planning, and collaboration—for example, smart manufacturing, enterprise workflow automation, or collaborative task solving.