Selecting the right agent paradigm is critical for AI system performance. This guide explores common paradigms like reactive, deliberative, and hybrid agents, explaining their origins and core differences. Rather than listing features, it emphasizes understanding trade-offs such as complexity vs. flexibility and speed vs. accuracy. Engineers can use this framework to match paradigms to specific project needs, avoiding common pitfalls. The article provides actionable insights for tech leads and architects designing scalable agent systems.
A structured comparison of agent paradigms focusing on trade-offs and selection criteria for real-world projects.