Episode 3: Reasoning methodologies for agents
The third episode of our Agentic AI and agentic workflows series, on how agents think, plan, and decide — the reasoning styles behind everything from quick answers to complex multi-step workflows, and the trade-offs of speed, depth, adaptability, and reliability.
In this episode we explore how modern AI agents approach thinking, planning, and decision-making when they tackle real-world problems. Drawing on research into agentic AI, we unpack the mental frameworks that guide large language models as they move from understanding a task to delivering an outcome. We look at how different reasoning styles shape everything from simple responses to complex, multi-step workflows, and why some approaches prioritize speed while others emphasize depth, adaptability, or reliability.
We also examine how advanced agents improve their performance through structured planning, self-evaluation, and collaboration, and we surface the trade-offs behind each strategy. By the end, we offer a practical lens for understanding how and why certain reasoning approaches are chosen, useful for anyone designing, deploying, or simply curious about the inner workings of intelligent systems.