Systems | Development | Analytics | API | Testing

The workplace is on the brink of a transformation unlike anything we’ve seen before. With the rise of AI agents—autonomous software entities capable of executing tasks, making decisions, and even optimizing workflows—the way we define work itself is evolving. While automation and AI-assisted processes have been gradually reshaping industries, the concept of the agentic enterprise takes this a step further, shifting from AI as a tool to AI as an active participant in business operations.

AI agents are all the rage these days. Poised as the next big thing after Gen AI…is there substance underneath all the hype? The answer is a resounding yes. For instance, the 2024 State of AI Agents report revealed that 51% of AI professionals are already using AI agents, while 78% of enterprises and mid-sized companies have active plans to put AI agents into production. However, doing this successfully requires paying attention to certain key factors.

Many pharmaceutical companies have been around for decades and must continually adapt to a rapidly evolving, technology-driven world. Even those that keep pace with digital trends often struggle with data silos and disparate systems, making it difficult to share information across platforms. This fragmentation hinders their ability to meet patient expectations, accelerate drug launches, and develop personalized therapies.

Multiplayer just made system architecture diagrams effortless—with auto-generated diagrams, CSV imports, and smart layouts. Say goodbye to whiteboards and hello to real-time, accurate visibility. If you’ve ever wasted hours dragging boxes around in Lucidchart or updating outdated diagram images in Confluence, we hear you. Visualizing your system shouldn’t feel like a second job—it should be part of your workflow.

Healthcare software applications have evolved beyond administrative tools, becoming pivotal in patient care, diagnostics, and Electronic Health Record (EHR) management while ensuring regulatory compliance. It ensures that applications like electronic health records (EHRs), telemedicine platforms, or diagnostic systems work accurately, securely, and comply with regulations.

Imagine a world where you could deploy your API gateways across any cloud, enforce enterprise-grade policies, and meet compliance standards all within a single Terraform apply. Most teams think about infrastructure as code only in terms of spinning up VMs, networking components, databases, or maybe a Kubernetes cluster. But what if your API platform was just as programmable? With Kong’s Konnect platform and its support for Terraform, that reality is here today!

Managing operations means overseeing a variety of moving parts. For SAP users, that complexity is amplified. Gathering data, reconciling accounts, and chasing down discrepancies can feel like a never-ending puzzle. Then comes the looming task of migrating to S/4HANA, the next generation of SAP. By December of 2027, SAP intends to phase out SAP ECC in favor of S/4HANA. Because SAP S/4HANA operates in the cloud, it provides enhanced flexibility, cost savings, and scalability.

Organizations are increasingly adopting AI to make quick decisions, understand data, and automate processes. However, this innovation comes at the cost of navigating complex data and AI compliance regulations. While AI regulations are still evolving worldwide, existing privacy laws and regulatory frameworks already apply to AI implementations. These laws, such as GDPR, CCPA, and HIPAA, create a complicated landscape for businesses.

Prior to coming to ThoughtSpot, I was at Salesforce where my team and I built Einstein Analytics. I fundamentally believe the role of Analytics and BI is not just to give you a chart that shows what happened—it also needs to show you why it happened, what could happen, and tell you what you can do about it. That's what being data-driven means.