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A customer's data lives in an eventually-consistent store. How do you reason about what consistency your AI workflow actually needs?

CAP is a trap if you recite it. The interviewer wants to see you map a customer's real datastore to a real failure mode, then decide where stale reads are free and where they corrupt an agent's decision.

Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.

CAP is a trap if you recite it. The interviewer wants to see you map a customer's real datastore to a real failure mode, then decide where stale reads are free and where they corrupt an agent's decision.

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FEDITOR'S NOTE

The discriminator is whether the candidate connects consistency to a specific step in the AI workflow rather than the datastore in the abstract. Strong answers say 'reads for retrieval context can be stale, but the write that flips a ticket to resolved must be read-your-writes for the same agent.' Watch for the CP-vs-AP recitation with no per-operation reasoning; push them with 'the customer says their DB is strongly consistent, do you believe it?' and look for awareness that 'strong' usually means per-key/per-partition, not cross-partition.

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