It starts the same way in too many organizations: a business leader asks a simple question like "Can we see churn by segment?” or “What’s the margin impact by region?” and the answer is, “Submit a ticket.”

Days turn into weeks. Priorities shift. Decisions get made without trusted data, or not made at all.

In this webinar, we’ll step back and tackle the bigger shift underway: agent-first data integration. As CIOs and data leaders push to scale analytics and AI, the real constraint isn’t ambition, it’s the time and friction it takes to turn a business question into a governed, production-ready dataset. While business users wait one to four weeks for data, technical data teams spend their time clarifying and translating requests to plans instead of delivering impact.

Attend this session to learn how IBM’s Data Engineering Agent can change that operating model, enabling business users to request data in natural language, while the agent interprets intent, generates a transparent execution plan, connects to the right sources, applies transformations, and enforces governance guardrails with human-in-the-loop oversight.

We’ll bring it to life with demos of IBM watsonx.data integration, including how teams can unify batch, real-time, and replication approaches with robust out of the box connectors, reduce tool sprawl, and maintain trust through observability, access controls, and audit trails.

Key Takeaways:

• Why agentic data management is emerging as a critical capability now

• How agent-first data ops reduces time from question to insight

• The role of Data Engineering Agents in simplifying integration

• How to scale data access while maintaining governance and trust

• The value of a unified platform for batch, real-time, and replication pipelines