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Every agent has an escalation policy. When confidence is low, the stakes are high, or a guardrail trips, the right human gets routed a complete context package — not a bare question.
Fully autonomous is rarely the goal. The goal is autonomous where it's safe and assisted where it's not. Escalation is how you tune that balance — per workflow, per action, per customer tier — and how the system gets smarter from every correction.
Confidence thresholds, action cost, customer segment, and policy rules are checked on every step. Low signal? Escalate.
Reviewers get the question, the agent's plan, the evidence, and one-click approve/revise buttons — no hunting through logs.
Human corrections feed back into the agent's evaluation harness so the same class of mistake isn't escalated next time.
Escalate a $10 refund auto-approve but a $10,000 refund to a manager — same agent, different routes, transparent rules.
Reviewers approve from Slack, Teams, email, or the platform — whichever surface they already live in.
When a human edits an agent's draft, the diff is captured as a labeled training signal — not lost in a tool.
Escalation queues expose age, volume, and resolution time so ops sees when thresholds need tuning.