I can appreciate your honesty. Setting up successful algo trading isn't like just baking a cake laugh

There's no golden standard for robustness/over-fitting (both are -IMO- pretty much the same), so I can only speak for myself.
I look for decent OOS statistics, enough trades (say 50+), combined with a decent PF (say 1.25+).
More precise would be to look at the performance offsets between your in- and out-of-sample stats.

To read it from a different perspective, check this interesting blog article https://quantlane.com/blog/avoid-overfitting-trading-strategies/

Believe me, even with 10 years of data with 1000+ trades you can have over-fitting, so a large in-sample set is not a guarantee for robustness.
This largely depends on the complexity of your approach, the more complex, the higher the likelihood of over-fitting.