This episode of Techsplainers explores goal-based agents, which sit in the middle of the AI agent complexity hierarchy. These agents go beyond simple reflexes by incorporating planning capabilities that consider future states when making decisions. The podcast explains how goal-based agents work through four stages: goal definition, planning, action selection, and execution. We examine a real-world example of warehouse automation robots that plan efficient paths rather than simply reacting to obstacles. The episode also discusses when to use goal-based agents versus more complex types like utility-based agents, and how different agent types can work together in multi-agent systems, as illustrated through a healthcare example where five specialized agents handle different aspects of hospital management based on their complexity requirements. 

Find more information at https://www.ibm.com/think/podcasts/techsplainers. 

Narrated by Matt Finio