Understanding AI agent types: A guide to categorizing complexity

Understanding AI agent types: A guide to categorizing complexity 1. Functional agents: Prioritizing essential functionality 2. Simple reflex agents: The basics of reaction 3. Model-based reflex agents: Adding internal awareness 4. Goal-based agents: Planning for the future 5. Learning agents: Adaptation through experience 6. Utility-based agents: Optimizing trade-offs 7. Hierarchical agents: Structured decomposition 8. Multi-agent systems: Collaboration and emergence Conclusion: Choosing the right complexity for your AI agents Get started with AI agents Get started with AI Inference About the author Richard Naszcyniec More like this Blog post Blog post Blog post Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share In their most advanced form, AI agents are autonomous systems designed to perceive their environment, make decisions, and take actions to achieve specific goals. As AI technology evolves, agents are becoming pervasive across many industries like finance, healthcare, manufacturing, and customer service. However, not all AI agents are created equal—their capabilities vary widely in terms of autonomy, decision-making, adaptability, and interaction with their environment. When thinking about AI agent use cases and the effort to implement them, it is important to understand the complexity of every agent being considered.