Research and analysis on AI agent trust, auditability, evaluation standards, and continuous monitoring.
Every vendor in the agent economy is selling the same word — safe. It's the wrong word, and here's why NextGenIQ reports only what an agent demonstrated: on a date, against inspectable checks, defensible precisely because it doesn't pretend to cover the future.
Agent rollouts don't stall in engineering — they stall in review. The four questions every auditor asks, and why the trust gap is really an audit gap.
The most important finding in Microsoft's 2026 enterprise-AI research isn't about what models can do — it's about why they don't get used. Trust is now the go-to-market surface.
A trust score worth acting on is anchored to standards that outlive any vendor. How NIST AI RMF, CSA guidance, and Know Your Agent compose into a defensible agent trust score.
The agent you approved no longer exists — its model was updated underneath it. Why a point-in-time review is a photograph of a river, and what continuous agent assurance actually looks like.
You cannot hold an agent accountable if you cannot reliably identify it. Why a portable agent-identity layer — identity, reputation, validation — is the layer the agent economy is missing.
A security brief on where agent trust really lives — below the agent, in an auditable AI data layer: the seven controls, identity-first agents, deny-by-default access, and delegation that only narrows.