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 will have more than 150,000 AI agents in use by 2028, up from fewer than 15 in 2025. Yet only 13% of organizations believe they currently have the right AI agent governance in place, according to the research firm.That widening gap is creating a market for infrastructure that sits above individual models and agents — handling execution, permissions, observability, memory, access to enterprise systems and lifecycle management without forcing developers to reconstruct those services for every new agent.xpander.ai, a startup founded by three former AWS principal engineers, is the latest company [...]
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 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 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 [...]
New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI [...]
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 [...]
A rogue AI agent at Meta passed every identity check and still exposed sensitive data to unauthorized employees in March. Two weeks later, Mercor, a $10 billion AI startup, confirmed a supply-chain br [...]
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 [...]
An enterprise AI agent answers with total confidence, but the number is wrong. Nobody catches it until someone traces it back to a stale metric definition or a document the retrieval system never pull [...]
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 [...]