Systems | Development | Analytics | API | Testing

Ever noticed how you’re always getting relevant ads, whether you’re streaming on Netflix or shopping on Amazon? Or how sometimes, just thinking about something seems to make it appear on your phone? It feels like every application somehow knows what you’re thinking, serving up personalized suggestions with high precision.

Enterprise automation didn’t start with AI agents, but they’ve had a much bigger impact than earlier automation methods, such as software scripts or bots. Modern AI agents can do a lot more than tackle repetitive tasks. They can reason through complicated workflows, choose the best course of action, and access tools to execute said action. But to do all this, AI agents require interoperability. They need to be able to connect to numerous tools, databases, services, and APIs.

As enterprise data increases in volume, variety, and velocity, the need for a new data architecture is becoming clearer. As AI moves from generative to agentic, can enterprises also envision and adopt an agentic data architecture? It’s true that we’re already seeing AI agents implemented in functions such as customer support and marketing. But what if we could do the same for data management?