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 evalua [...]
Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their interna [...]
Across 108 enterprises, trust in automated agent evaluation rose sharply in July — and the failure rate it is supposed to predict did not move at all. The share of organizations that fully trust aut [...]
Enterprises that already got burned by an AI agent passing its evals and then failing in production are moving faster toward removing humans from deployment decisions, not slower — even as trust in [...]
Enterprise AI has a new infrastructure problem: companies are accumulating agents faster than they are developing systems to govern them.Gartner estimates that the average global Fortune 500 company w [...]
Enterprise companies are running AI agents ahead of the controls needed to manage them — and they deployed that way knowingly. That is the central finding from VentureBeat Research's June surve [...]
Imagine you do two things on a Monday morning.First, you ask a chatbot to summarize your new emails. Next, you ask an AI tool to figure out why your top competitor grew so fast last quarter. The AI si [...]
Enterprise AI has largely been built around context engineering. Teams connect enterprise systems, generate chunks and embeddings, build retrieval pipelines, and assemble the context needed by individ [...]
Across 107 enterprises, AI agents are being given real access to systems and data while the controls meant to contain them lag behind. More than half have already had a confirmed agent security incide [...]
As LLMs have continued to improve, there has been some discussion in the industry about the continued need for standalone data labeling tools, as LLMs are increasingly able to work with all types of d [...]