Systems | Development | Analytics | API | Testing

What if you could build your own sales agent without relying on complex coding or technical expertise? Sadiq Abbas, part of our Growth Team, did just that using Astera’s drag-and-drop visual builder. With Astera AI Agent Builder, we aim to empower businesses to create autonomous AI agents that automate workflows using their enterprise data. Whether it's for customer support, scheduling demo meetings or lead qualification, our simple drag-and-drop interface lets you design agents that fit your specific needs.

A composite KPI is a powerful way to layer multiple chart objects—like KPIs, line charts, and gauges—within a layout container to create a unified, custom visualization. By overlapping these elements, you can combine a bold numeric value (e.g., total sales) with a trend line or mini chart that adds context and visual depth. This technique transforms static KPIs into dynamic, insight-rich visuals. The layout container’s flexibility allows precise positioning and transparency adjustments, helping you design dashboards that are both informative and visually compelling.

As the lines between analytics and AI continue to blur, data teams are expected to go beyond dashboards. They must enable predictive insights, power AI applications, and move faster than ever—all while navigating fragmented data systems and infrastructure limitations.

Creating AI agents is now faster, easier, and more unified than ever. Jay Mishra, our Chief Product and Technology Officer, shares how the Astera AI Agent Builder empowers teams to design and deploy enterprise-grade agents using a fully visual, drag-and-drop interface. With built-in state management, seamless MCP integration and direct access to business systems, our agents are context-aware and capable of making real-time, data-driven decisions.

Join us for an exclusive deep dive into Yellowfin 9.15, a milestone release that introduces groundbreaking AI-enabled Natural Language Query (AI NLQ) to transform how users interact with data with Chance Coble, Stephen Ball and Dan Cardno.