Every week brings a new breakthrough in AI, and a new strain on infrastructure. One day, you’re fine-tuning a small model on a local machine. The next, you’re trying to schedule workloads that consume dozens of GPUs across multiple locations. And that doesn’t include the pace of new hardware, which increases what you can do.

You’ve spent months building a modern and intuitive app. It's fast, user-friendly, and visually consistent. But your embedded analytics is still a clunky iframe that is totally disconnected from your UX. Users get frustrated, and engagement flatlines. In today's data-driven business landscape, embedded analytics has become a critical competitive differentiator.

Multi-agent workflows are the latest technological gen AI advancements. In this blog, we explore how to develop such systems, overcome operational challenges, improve system observability, and enable seamless collaboration between agents in complex AI pipelines. We’ll cover architecture, A2A and MCP protocols and introduce Google Cloud’s agentic marketplace.

Your customers want dashboards and real-time analytics from your data. You’ve built a REST API thinking you’re providing great data access. But there’s a fundamental architectural mismatch creating hidden friction.

Discover insights from a recent TDWI expert panel on integrating data and AI platforms.

We are entering a new era in enterprise data: the era of the Data Operator. As AI becomes core to every business process, every team is being asked to move faster, act smarter, and operate with real-time data. But the old stack isn't built for that. It's built for centralization. For gatekeeping. For data engineers and IT teams to own every flow, sync, and transformation. That model is breaking down. Why? Because the need for data has exploded at the edge of the business. Customer teams. RevOps.