AI Agent Guardrails: Human Approval and Failure Recovery

Description and use

<p>A practical control system for deciding what an agent may do, what a person must approve and how the workflow returns to a safe state when something fails.</p> <p><strong>Reliable AI agents use several controls, not one safety prompt.</strong></p> <p>Give the agent least-privilege access, validate inputs and tool calls, require human approval before consequential side effects, verify the real result and define recovery states for every failure. If the system cannot establish what happened, it should stop, preserve the evidence and escalate instead of guessing or repeating the action.</p> <h

Problem

<p>A practical control system for deciding what an agent may do, what a person must approve and how the workflow returns to a safe state when something fails.</p> <p><strong>Reliable AI agents use several controls, not one safety prompt.</strong></p> <p>Give the agent least-privilege access, validate inputs and tool calls, require human approval before consequential side effects, verify the real result and define recovery states for every failure. If the system cannot establish what happened, it should stop, preserve the evidence and escalate instead of guessing or repeating the action.</p> <h

Outcome

<p>A practical control system for deciding what an agent may do, what a person must approve and how the workflow returns to a safe state when something fails.</p> <p><strong>Reliable AI agents use several controls, not one safety prompt.</strong></p> <p>Give the agent least-privilege access, validate inputs and tool calls, require human approval before consequential side effects, verify the real result and define recovery states for every failure. If the system cannot establish what happened, it should stop, preserve the evidence and escalate instead of guessing or repeating the action.</p> <h

Evidence

AI Agent Guardrails: Human Approval and Failure Recovery

Source