Trusted AI Starts With the Data Stream: How to Govern the Real-Time Data Your AI Agents Act On

AI agents don't just summarize documents. They make decisions: approving a payment, placing an order, recommending the next step. If the data behind those decisions is stale or ungoverned, the agent gets it wrong in seconds, and nobody can say why. In this on-demand webinar, Kai Waehner, one of the best-known voices in real-time data streaming, shows how to govern, protect, and trace your real-time data so you can trust your AI agents, and what that looks like in practice with Confluent and IBM.

Watch this free webinar to:

Understand why data freshness is now a trust requirement. When an agent acts on stale data, the cost is a wrong decision, not a slow report.
Choose the right pattern for each use case. Event streams, message queues, APIs, and batch, and why they work best together.
Pick a deployment model from your requirements. Fully managed, self-managed, or bring your own cloud, and how regulation, latency, and sovereignty rule options out early.
Govern events the way you govern APIs. Data contracts, a data catalog, a gateway, and end-to-end lineage across streams, tables, and API calls.
Make every agent decision traceable. Record each agent action as an event, so you can show which agent decided what, and on which data.
See a reference architecture. How Confluent, now part of IBM, works alongside IBM MQ and webMethods without replacing what you already run.

A look inside Kai's presentation

From wrong reports to wrong actions: why AI agents raise the stakes on stale data, and how governed streams, the right deployment choice, and an immutable event log keep them trustworthy.

Watch the full recording to hear Kai walk through every slide and learn how to answer the question every auditor asks: "Why did the agent do this?"