I never got to try it myself, but no doubt it worked fine in those cases where correct inferences could be made based on the knowledge/rules it had! Similarly GPT-4 is extremely impressive when it's not bullshitting!
The brittleness in either case (CYC or LLMs) comes mainly from incomplete knowledge (unknown unknowns), causing an invalid inference which the system has no way to detect and correct. The fix is a closed loop system where incorrect outputs (predictions) are detected - prompting exploration and learning.
I don't know if CYC tried to do it, but one potential speed up for a system of that nature might be chunking, which is a strategy that another GOFAI system, SOAR, used successfully. A bit like using memoization (remembering results of work already done) as a way to optimize dynamic programming solutions.