Agent Trust Hub for enterprise AI leaders

Enterprise AI programs reach a common inflection point: agents are running in production, costs are accumulating, and the question shifts from "can we use AI?" to "how do we know it's working and whether it can be trusted?"

Most teams at that point can tell you how many agents they've deployed. Fewer can tell you what data those agents are accessing, whether that data is accurate, who owns it, or what it costs per conversation. That's where trust breaks down, and where the business starts asking questions that are hard to answer.

Agent Trust Hub gives enterprise AI leaders a central environment to see what agents are doing and connect that activity to the signals that determine whether those agents can be trusted. For each connected platform, the Hub keeps a registry of agents and their conversations, then ties each conversation back to the data behind it.

In practice, that means four things:

• Usage and cost. Which agents are active, who is using them, how usage is trending, and what each conversation cost in tokens and credits.
• Conversation-to-data linkage. Every conversation resolves to the specific catalog assets it accessed, down to the tables and columns. An agent stops being an opaque event log and becomes a connected node in the same lineage graph as everything else in your data estate.
• Trust flags. Because the linkage runs through Bigeye's catalog, Bigeye checks each access against what it already knows. A conversation that read data with an open quality issue gets a flag. A conversation that touched sensitive or classified data gets a flag. You can see whether the data behind an answer was trustworthy, not just that an answer was produced.
• Coverage gaps. When an agent accesses data that isn't yet tracked in Bigeye, the Hub flags it. Teams can see where AI activity actually is, not just where monitoring was already set up.

That visibility is the foundation. Once teams understand which agents are active, what data they rely on, where trust signals are absent, and what each workflow is costing, they can move into governance: setting policies, enforcing access controls, and building accountability into the workflows where agents are embedded.

AI leaders making the case internally can point to both: here's what our agents are doing, here's the data they're using, and here's whether that data was trustworthy. That's what moves an AI program from a pilot to a production system the business can stand behind.

Agent Trust Hub is available now with a 30-day free trial. If your team is navigating this, it's worth a look.