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.