We built an AI that audits other AI agents (here's how A2A works in production)

The audit report came back at 2:47am. I wasn't expecting it — I'd triggered the test run before bed, more out of habit than expectation. But there it was: a score, six dimension breakdowns, and a remediation plan with specific line numbers. The auditor was an AI. The thing being audited was also an AI. And the whole exchange took 7 turns of natural language conversation with zero human involvement. This is what agent-to-agent (A2A) actually looks like in production. Not a diagram. Not a whitepaper. A working system that one agent uses to interrogate another. Here's how it works — and what we learned building it. The problem we were trying to solve Most teams building on top of LLMs don't measure token waste. They measure output quality, latency, user satisfaction. But token efficiency? Almost never. This is expensive. In our testing, production agents consistently waste between 40% and 60% of their token budget on things that are completely fixable: System prompts carrying 3x more cont