Reflex Agents: Fast, Rule-Based Responses
Reflex agents act based on immediate conditions using simple if-then logic. They don’t rely on memory or context, which makes them ideal for fast, repetitive automation tasks. Examples include filtering spam emails or triggering alerts in IT systems. Think of them as digital reflexes—quick and efficient, but limited in intelligence.
Model-Based Agents: Context-Aware Decisions
Model-based agents maintain an internal state or model of the world. This allows them to connect current inputs with past events, enabling context-aware decisions. These agents are ideal for use cases where memory matters—such as customer support tools that recall previous interactions to offer more relevant responses.
Utility Agents: Strategic, Goal-Oriented Systems
Utility agents go a step further by evaluating different actions based on a utility function. They calculate the best course of action based on outcomes, making them perfect for high-stakes environments like investment analysis, predictive maintenance, or dynamic resource management. These agents aim for optimal results—not just functional responses.
Choosing the Right Agent Type
Each agent type serves a different purpose. Reflex agents prioritize speed. Model-based agents prioritize context. Utility agents prioritize outcomes. Matching your use case to the appropriate agent type ensures your AI system is both efficient and intelligent. When designing agents powered by LLMs, these patterns provide a clear foundation for scalable, reliable automation.