5 Major AI Agent Architectures Explained
goyalshaliniuk · x · 2026-07-16
The post outlines 5 common AI Agent architectures and how they work when building intelligent systems:
- Simple reflex agents: Execute based solely on current inputs and predefined condition-action rules. They have no memory, offering fast but limited responses.
- Model-based reflex agents: Track the current world state via an internal model, enabling them to handle partially observable environments.
- Goal-based agents: Rather than blindly reacting, they plan and evaluate action consequences to achieve specific objectives.
- Utility-based agents: Introduce a utility function on top of goals, weighing different outcomes to maximize benefit or satisfaction.
- Learning agents: Continuously learn and evolve based on environmental feedback, persistently improving future action strategies.
The author advises developers to choose the appropriate architecture based on system requirements, such as rule-driven chatbots versus complex decision-making systems.
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