Call barging

Call monitoring is the practice of reviewing live or recorded customer calls to check quality, compliance, and outcomes. Supervisors can listen in real time, or they can review recordings after the call ends. Traditionally, teams sampled only 5 to 10 percent of calls by hand. That left most conversations unreviewed. AI quality tools change this math. OnClarity’s AI Agent QA scores 100 percent of conversations against a company’s existing evaluation rubric within minutes of call end, instead of waiting days for a supervisor to catch up on a backlog. Recording consent rules vary by US state, and some states require consent from every party on the call, not just one. Recordings are commonly stripped of sensitive data through PII redaction before they are stored, so a reviewer can read a transcript without seeing a customer’s card number or social security number.

A warm transfer connects a customer to another agent or team only after the receiving party has been briefed on who the customer is and what they need. The customer never has to repeat themselves. This contrasts with a cold or blind transfer, where the call is passed along with no introduction, and the next agent has to start from zero. Example: in an AI voice deployment, a warm transfer means a mortgage query is handed to a human specialist along with the customer’s verified identity, an intent summary, and the full transcript so far. The trade-off is brief. Two agents are occupied during the handoff instead of one, but that small cost is offset by fewer repeat-explanation complaints from customers. See also routing and triage, two related terms already defined on the OnClarity glossary.

Quality assurance is the structured review of conversations against a scorecard, applied to human agents and AI agents alike. Traditional QA samples a handful of conversations per agent each month; automated QA scores every conversation, which changes the exercise from spot-checking into actual measurement.

Human in the loop describes a review step where a person approves or corrects AI output before it reaches the customer. Teams typically apply it to high-risk intents such as refunds, cancellations, and complaints, then relax it selectively as measured accuracy on those intents proves out.