Human oversight remains the primary trust mechanism between operators and agents as agentic AI systems move into production. This trust rests on an unverified assumption: the approving operator/engineer actually reads and understands the action. Approval often collapses into a single click within MCP clients and agent frameworks alike.
This talk proposes two complementary controls to answer the question: Did your engineer read and understand the agent suggestions?

The first control introduces a three-level model of agent autonomy: suggest-and-approve, act-and-monitor, and independent operation. This model highlights the reading problem, breaking trust differently at each operational level.
The second control implements a comprehension gate. The system asks targeted questions about the agent's actions and potential risks before an operator approves a tool call or system change.

I will demonstrate a working prototype of agent approval flows featuring MCP tool-call dialogs and show the system's question-generation process.