Human-in-the-Loop Support Patterns
Human in the loop in AI support means a person stays in the decision path for any action that needs judgment, review, or approval. The strongest pattern is the decision card: when the AI is not cleared to auto-send, it neither sends blind nor gives up, it hands a human a compact, reviewable choice. Done well, every human decision also trains the system to handle more on its own next time.
The two failure modes it prevents
Ungoverned automation fails in two opposite ways. "Send blind" is auto-sending an answer the AI is not sure about, which produces confident hallucinations. "Give up blind" is dumping the customer into a queue with no context when the bot stalls. The human-in-the-loop pattern rejects both: uncertainty becomes a structured handoff, not a gamble or a dead end.
What a good handoff carries
A useful decision card gives the human everything needed to decide in seconds: the customer request and context, the AI recommended reply or action, its reasoning and sources, its confidence and which guardrail tripped, and clear labeled choices such as approve, edit, send, escalate, or reject. Without that context, "human in the loop" just means a slower queue.
How Resolve implements it
Resolve builds the pattern around the decision card. When Reva is below its confidence threshold, it produces a card with the request, retrieved knowledge, a suggested action, its confidence, and the guard that tripped. Every cleared card sharpens the model: edits become preference pairs, approvals confirm patterns, and escalations become new cases, so the share of tickets needing a human shrinks over time.