This episode will explore how Agentic AI systems “think” and “reason”—examining the difference between open-ended exploration (creative, generative, speculative) and closed-ended reasoning (focused, deterministic, goal-specific).

We’ll discuss:

• When to use each type of reasoning in AI workflows.

• The risks of open-ended thought (e.g., hallucination, inefficiency) vs. the limitations of closed-ended logic (lack of innovation, rigidity).

• How to design agentic systems that balance both—using open-ended reasoning for ideation and exploration, and closed-ended reasoning for execution and precision.

• The role of prompt design, planning agents, and model selection in shaping how “thought” happens inside AI systems.

The podcast will also touch on environmental impact—how sprawling open-ended reasoning can drive up compute unnecessarily if not constrained—and how to architect for leaner, purposeful thinking.