Bottom‐up and top‐down problem solving with agentic AI

Agentic LLM agents can sometimes seem like magic, granting our (almost) every wish. But like the magic genies in stories like the Sorcerer's Apprentice, sometimes you have to be careful what you wish for. The difficulty of precisely specifying what it is we want and unintended consequences, sometimes derail this magic. As with genies, they grant requests in a top‐down manner. The agent autonomously breaks the wish into subgoals, until it reaches concrete actions that can be done by Agentic facilities. If left unsupervised by the user, a misunderstanding can result in disaster. But it's also possible to adopt a bottom‐up approach, starting with concrete examples, demonstrating procedures on these examples, and only then generalizing to a wider set of cases. It is often easier to control and debug, and easier for beginners and nontechnical people. Ultimately, we need flexibility to go in either direction. The contribution of this paper is to bring a bottom‐up methodology to “vibe coding” with Agentic Agents like Cowork and ChatGPT Agent. It draws on the technique of Programming by Example (also called Programming by Demonstration) for conventional programming. The advent of Agentic AI may unblock a 50‐year obstacle that has prevented PBE from more widespread acceptance.