Organizational knowledge challenges of AI agents

Once your AI agents have been set up and governed like teammates, the next question is where their knowledge actually comes from — and who's responsible for keeping it accurate and up to date. This session separates the two problems hiding inside every "the AI got it wrong" complaint: content risk (the information itself is stale or false) versus execution risk (the agent takes a real action based on that bad information). We'll also dig into the mechanics of actually connecting an agent to live company systems — through MCP (Model Context Protocol), custom connectors, and dynamic ingestion — and why the question that matters most isn't how many integrations a platform offers, but where your credentials actually live when you use one. Here are are real-world examples from our Ejento deployments: A benefits agent confidently tells an employee last year's PTO policy. A CEO asks their own company's agent who runs the business — and gets the previous CEO's name back. In both cases, the agent did exactly what it was built to do: it found a document, and answered based on it. The document was just wrong. We will share our experience and take questions from the audience. What you'll learn: - Why "the agent hallucinated" is usually the wrong diagnosis — and what's really happening when it retrieves a confidently wrong answer - The difference between content risk and execution risk, and why the second one gets expensive fast - Why one-time exports and spreadsheet dumps quietly turn agents into confidently wrong employees within weeks - How MCP works as a universal connector standard, and when custom connectors or dynamic ingestion (site crawls, SharePoint, Google Drive) are the better fit - The real security question to ask any AI platform: not "how many connectors," but "where do my credentials actually run" - A live look at connector scopes, credential references, and explainability traces inside the Ejento platform