Join David Brader in Part 2 of "Change Detection, Decoded," where we take the change detection system built in Session 1 and give it a brain. In this hands-on session, we'll layer a Domo AI Agent on top of the existing pipeline. One that doesn't just detect a signal, but understands what it means and recommends what to do next. Using Domo's AI Library, Toolkits, and MCP, you'll build an agent with access to your process data and detection history that can provide real-time context, explain why a signal fired, and surface recommended actions directly inside your workflow, whether that signal came from a sales pipeline, a manufacturing line, a call center queue, a claims process, or any other metric your team tracks. You'll walk away knowing how to turn any detection or monitoring system in Domo, across any industry or department, into an intelligent, action-oriented AI Agent.

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