Your AI agent aced every test in your staging environment. The demos were flawless. The PM was impressed. Three weeks into production, you're fielding bug reports about responses that sound correct but are subtly, catastrophically wrong.
I've been on the receiving end of that call. In 2025, I watched a team ship an AI agent built on early AWS Agent Toolkit previews that confidently hallucinated product pricing for enterprise customers. The agent's confidence score was 0.94. The actual accuracy was maybe 60%. Nobody had built an evaluation pipeline because the tooling didn't exist yet.
That's changing fast. AWS Agent Toolkit GA and MCP Server GA are recent releases (as of mid-2026), and with them comes an emerging discipline: Agent Skills evaluation. A Qiita post from Japanese developer community highlights a gap most English-language resources haven't caught up with yet — how to actually measure whether your AI agent's skills are performing reliably in production.
The Problem Nobody Talks About
Here's what I've observed across three production AI agent deployments: teams spend enormous effort on agent architecture — tool definitions, prompt engineering, orchestration logic. Then they ship and hope.
