Teams keep building smarter agents — but when the underlying documents contradict each other, no model can compensate. The next competitive edge in enterprise AI isn't a better agent. It's the shared knowledge foundation that makes every agent trustworthy. Read more
Gartner predicts 40% of today's agentic AI projects won't make it to 2028 — not because the models failed, but because governance did. Here's what the enterprises that are succeeding actually have in common, and what it means for anyone deploying autonomous agents now. Read more
Every time an agentic system routes a task from one model to another, the receiving model pays a steep recompute penalty. Nvidia researchers found that simple linear math — not deep learning — can transfer conversational memory between models up to 25 times faster, with accuracy losses small enough to matter in production. Read more
For decades, audit meant sampling. MindBridge and Fieldguide's integration changes that — embedding transaction-level AI risk analysis directly into agentic audit workflows. The shift from sample to full population is quieter than it sounds, and more consequential than most realize. Read more
Serval's Catalyst super agent scans connected systems, identifies repetitive IT work, and drafts governed automations before a ticket is ever raised. It's an early signal of where enterprise service management is heading — and why 'agentic' now means something more specific than AI-assisted. Read more
An MIT report puts the failure rate for generative AI pilots at 95%. In financial services, where compliance, data sensitivity, and workflow complexity are all elevated, that number is no surprise — but it should be a warning. Here's what production-ready agentic AI actually requires. Read more
Gartner projects Fortune 500 companies will run over 150,000 AI agents by 2028. Only 13% feel ready to govern them. A new class of vendor-neutral control plane infrastructure is emerging to close that gap — but the competitive field is already crowded and the lock-in question remains open. Read more
Most RAG systems route everything through the LLM and call it done. In regulated environments, that approach breaks under audit, scales poorly, and quietly accumulates risk. A three-stage cascade architecture changes the question from 'how do I prompt this well?' to 'what should never reach the model at all?' Read more
Sixfold and Sollers Consulting have paired AI-driven risk assessment with deep advisory capability. The move reflects a broader industry truth: deploying AI in insurance is as much an implementation challenge as a technical one. Here's what that means for carriers moving toward automation. Read more
Agentic RAG uses a dispatcher layer to decide when to loop or stop — a governance pattern that matters for enterprise AI deployments in regulated industries.