What happens when every AI agent action needs a human stamp of approval? We break down MCP Sampling — the Model Context Protocol's built-in mechanism for intercepting and approving AI agent actions before they execute. From zero-knowledge architectures to mobile approval UIs, we explore how to build a full governance system for AI agents using Node.js, TypeScript, React, and PostgreSQL.

00:00:00 - Introduction and D&D analogies

00:01:15 - MCP Sampling basics: how the request flow works vs regular MCP tools

00:03:00 - Concrete example: intercepting an AI agent sending an email

00:04:30 - Zero-knowledge architecture: processing actions without storing sensitive data

00:06:15 - Transit-only vs end-to-end encryption approaches

00:08:00 - Immutable audit logs in PostgreSQL with hash chaining for tamper evidence

00:09:30 - Prisma schema walkthrough for the append-only log

00:10:45 - Mobile-first approval interface: push notifications and real-time WebSocket flow

00:12:15 - Handling timeouts and offline approval scenarios

00:13:30 - Compliance PDF export: proving chain of custody for regulators

00:14:30 - Practical concerns: MCP sampling maturity, client support, and when it's worth the complexity

00:15:30 - MCP sampling vs plain webhooks with a custom approval API

00:16:00 - Wrap-up

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