Escalation and Human Review
Explore the principles of escalation and human review within AI agent workflows. Understand the four key conditions that require escalation, how to create concise approval packets for reviewers, and methods to implement escalation signals that pause autonomous actions. This lesson helps you design escalation strategies that balance operational efficiency with necessary human oversight, ensuring reliable and compliant AI system behavior.
Escalation is the mechanism by which an agent signals that it cannot, or should not, proceed autonomously. It is not a last resort triggered by catastrophic failure; it is a designed workflow stage that fires when the action ahead is beyond what the agent is authorized to decide alone. An agent that never escalates is either operating in a domain where every action is low stakes and reversible, or it is taking actions it should not. An agent that escalates well produces a structured handoff packet, a compact document that tells the human reviewer exactly what decision is needed, why the agent stopped, and what information is available to inform the decision. This lesson covers when to escalate, what the packet must contain, and how to design the escalation signal so that the workflow can resume after the reviewer acts. By the end of this lesson, we will be able to:
Identify the four conditions that should trigger escalation
Write an approval packet that contains the information a reviewer needs, and nothing else
Implement an escalation signal that halts the agent loop and surfaces the packet to the reviewer
Distinguish over-escalation from appropriate escalation and explain why both are costly
The four escalation conditions
An agent should escalate when it encounters one of four conditions. These conditions share a common property: The agent has the ...