A team routing queries across a coding specialist, a logic specialist, and a generalist model assumes each will cover the others' blind spots. A new study evaluating 67 frontier models from 21 providers shows that assumption is mathematically flawed — and the flaw has a name: the co-failure ceiling.The assumption works like this: as long as two models don't usually fail on the exact same prompts, combining them is supposed to create a safety net against failures.The real limit on orchestration is not how often models disagree, but the percentage of prompts where every model in the pool gives the wrong answer at once. By ignoring the co-failure ceiling, enterprises are building complex, expensive routing infrastructure to chase performance gains that do not exist. Fortunately, d [...]
Across 101 enterprises, the context feeding AI agents is failing often and repeatedly. Sixty-eight percent have traced a confident but wrong agent answer to missing or inconsistent business context in [...]
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 [...]
Across 116 enterprises, agents are in production and so are the incidents: A majority have already had a confirmed agent security event or a near-miss. Two-thirds of enterprises enforce scoped permiss [...]
Across 170 enterprises, AI infrastructure has moved decisively into production — two-thirds now run AI workloads live and three in 10 run them at scale — while the ability to account for what that [...]
Visa's president of technology, Rajat Taneja, walked the VB Transform 2026 audience through aiming Anthropic's Mythos at Visa's own payment network. The model stitched minor weaknesses [...]
Across 107 enterprises, agentic orchestration is not a choice of a single platform.The typical enterprise runs three orchestration platforms at once, and selects them for flexibility across models rat [...]
Across 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run their AI on a familiar base of hyperscalers and model [...]
Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context sourc [...]
A company builds a governed context layer specifically to stop its AI agents from confidently giving wrong answers. Once that layer is live, the company is more than twice as likely to report the fail [...]