AI features stress a backend differently than classic CRUD. Requests may stream, fan out to tools, wait for model responses, retry after failures or produce partial results.
A robust AI backend has queues for long tasks, idempotency keys for tool calls, structured traces for every run and clear storage rules for prompts, outputs and user data.
The product team should also track quality metrics: acceptance rate, edit distance, tool failure rate, escalation rate and time saved. Latency alone does not tell the story.