Trustworthy AI starts with governance, not guesswork

Organizations have historically run on human judgment, but agentic AI is changing the equation. For example, a lawyer would recognize that a litigation document was outdated, despite its being in the knowledge base. An admin might notice something was “off,” or that sensitive information was being shared inappropriately in an email, as a matter of course. They didn’t notice because it was in a rulebook; they noticed because they are human. Humans make broad connections. Our brains excel at critical oversight.

By contrast, an AI agent intuits nothing, and “knows” only what is made explicit; only what's been built into its systems, what it's told directly, or what it guesses from patterns unrelated to your organization. But the solution isn’t, “Well, let’s not use agents, then.” We’ll all be using agents soon, if we aren’t already (and we probably are, even if we don’t yet know it).

This white paper unpacks what it takes to ensure your governance is up to the job of guiding and scrutinizing the actions of both humans AND AI agents. To give you a baseline for keeping your organization trustworthy and its outcomes defensible.

• Learn what it requires to be "governed" in an agentic landscape.

• Find out why governance determines whether context compounds.

• Know who is accountable when an AI-generated output goes wrong.

• Discover what Model Context Protocol (MCP) can offer.

• See what readiness looks like.

Governance is not the price an organization pays to use AI. It's the investment that makes AI trustworthy enough to rely on.