The most dangerous trait of AI agents is not always giving a wrong answer. Sometimes the problem is that the wrong answer looks convincing. An agent can generate a claim. Another agent can write that claim to memory. A tool can then take an action based on that information. Before that happens, a few questions matter: — Which source actually supports this claim? — Which workspace and scope is valid here? — Does this information contradict something already accepted as fact? — Who or what approved before the action was taken? HUQAN is not another model replacing models. It adds a local-first trust boundary around AI-mediated workflows. Claims, memory writes and risky actions are assessed through evidence, provenance, scope, policy and approval — and resolved to ALLOW, BLOCK or ESCALATE. The goal is not to say “AI never makes mistakes.” The goal is to make it harder for wrong information to settle into a system as convincing fact. What is the first piece of evidence you would want to see before trusting a claim produced by an AI agent? #HUQAN #AIAgents #AISafety #OpenSource