AI agents write code, generate tests, fix failures, and increasingly ship features with minimal human review. We're getting remarkably good at making agents build software.

But are we equally good at knowing whether what they built is actually right?

An agent can pass every test it wrote for itself while missing what its assertions never considered: a layout that doesn't match the approved design, a checkout button that disappears on Safari, a PDF report with the wrong number on page two, or a control a screen reader can't reach.

The code compiled. The tests passed. The agent said done.

If you're a developer, you're approving changes an agent wrote, tested, and vouched for, without actually knowing what's in the diff you just merged. If you're a tester, you're being asked to keep pace with output no human review cycle was built for: more coverage, more surface area, same headcount.

This is the verification gap. What changes when an agent can examine its own work, catch what's wrong, and decide whether to fix, escalate, or move forward?

If your team ships with Copilot, Cursor, or Claude Code, this gap is already in your pipeline, and closing it runs through the same protocol those tools already speak: MCP. From web experiences and design intent to documents and accessibility.

As agents get better at building software, how do we make them equally good at knowing they've built the right thing?