Why AI Agent Eval Suites Miss Critical Production Failures Beyond Final Output

AI agent evaluation frameworks that only score final answers are giving development teams a false sense of security, according to a widely discussed analysis in the developer community. An agent can produce a correct-looking response while having accessed unauthorized data, leaked private context, or triggered irreversible side effects that never surface in the output string. This "final answer bias" means evaluation suites routinely mark dangerous or inefficient runs as successful. Gartner projects that over 40 percent of agentic AI projects will be canceled by end of 2027, with 32 percent of organizations citing output quality as the top deployment barrier. Experts argue that robust evaluation must shift focus to trajectory-level metrics — examining intermediate steps, tool calls, and resource access — not just the last message returned.