Hi @jmb,
It sounds like using the action feature-table group would achieve what you describe. If you have a metadata category that describes which individual a sample came from, you can use group to collapse the feature table by group.
Note that the sample IDs will no longer be the original sample IDs, and will instead be the values from that metadata category (e.g., the individual ID). So you will need to create a new metadata file with these values as sample IDs for downstream analyses.
Keeping technical replicates separate also has its benefits from a statistical standpoint. Within-individual variation can be important for comparing samples, e.g., with diversity statistics. High inter-individual variation may decrease, rather than artificially inflate, confidence. But it cuts both ways. At the very least, it is worth looking at a PCoA plot to determine how much “spread” there is between technical replicates, and then group these samples if this is not artificially reducing noise and if you have sufficient replication for other tests.
I hope that helps!