Completely disagree, what i see agentic coding agents do in combination with LLMs is seriously mind-blowing. I don't care how much knowledge is compressed into an LLM. What is way more interesting is what it does when it misses some knowledge. I see it come up with a plan to create the knowledge by running an experiment (running a script, sometimes asking me to run a script or try something), evaluating the output, and then replan based on the output. Full Plan-Do-Check-Act. Finding answers systematically to things you don't know is way more impressive than remembering lots of stuff.