Getting AI agents to perform reliably in production — not just in demos — is turning out to be harder than enterprises anticipated. Fragmented data, unclear workflows, and runaway escalation rates are slowing deployments across industries.“The technology itself often works well in demonstrations,” said Sanchit Vir Gogia, chief analyst with Greyhound Research. “The challenge begins when it is asked to operate inside the complexity of a real organization.” Burley Kawasaki, who oversees agent deployment at Creatio, and team have developed a methodology built around three disciplines: data virtualization to work around data lake delays; agent dashboards and KPIs as a management layer; and tightly bounded use-case loops to drive toward high autonomy.In simpler use cases, Kawasaki says these practices have enabled agents to handle up to 80-90% of tasks on their own. With further tuning, he estimates they could support autonomous resolution in at least half of use cases, even in more complex deployments.“People have been experimenting a lot with proof of concepts, they’ve been putting a lot of tests out there,” Kawasaki told VentureBeat. “But now in 2026, we’re starting to focus on mission-critical workflows that drive either operational efficiencies or additional revenue.”Why agents keep failing in productionEnterprises are eager to adopt agentic AI in some form or another — often because they’re afraid to be left out, even before they even identify real-world tangible use cases — but run into significant bottlenecks around data architecture, integration, monitoring, security, and workflow design. The first obstacle almost always has to do with data, Gogia said. Enterprise information rarely exists in a neat o …
