Enterprise AI teams are giving agents more freedom at the same moment their confidence in automated testing is collapsing.Half of enterprises have deployed an AI agent or LLM feature that passed internal evaluations and yet still caused a customer-facing failure — one in four more than once — according to the June 2026 VB Pulse survey of 157 qualified enterprise respondents at companies with 100 or more employees.The sample is self-selected rather than a probability sample, so the findings should be read as directional, not precise.But enterprises are not responding by slowing automation: 66% of respondents already permit some production deployment without human review or are building systems intended to do so within the next 12 months. Only 5% say they fully trust the automated evaluations that would make those release decisions.That mismatch is the evaluation gap: the autonomy ceiling is rising faster than the assurance beneath it. It also fits a broader thesis that will be explored at VB Transform 2026: enterprises ship agents first, while the control layers around identity, evaluation, cost, context and orchestration are arriving later. The next year will be a retrofit cycle, with buyers shifting budget toward the systems that make agentic deployments governable and dependable.Why a passing evaluation is not a working agentTraditional software testing usually asks whether a defined input produces an expected output. Agent testing is harder because the system may choose its own sequence of steps, call tools, retrieve data, alter state and respond differently from one run to the next.An agent can make several individually plausible decisions and still reach the wrong result. It may retrieve the co …
