Symbolic processing was obviously a bad approach to building a thinking machine. Well, obvious now, 40 years ago probably not as much, but there were strong hints back then, too.

"AI agent" roughly just means invoking the system repeatedly in a while loop, and giving the system a degree of control when to stop the loop. That's not a particularly novel or breakthrough idea, so similarities are not surprising.

What’s interesting to me is the rise of agentic approaches which are effectively “build a plethora of tools and heuristics” with an outer loop that combines, mutates and assigns values to these components. Where before that process was more rigid, we now have access to much more fluid intelligence but the structure feels similar - let the AI prod at the world and make experiments, then look at what worked and think of some plausible enhancements. At a certain point you’re enhancing the code that enhances the enhancer and all bets are off.