Systems | Development | Analytics | API | Testing

In 2025, the most important customer for your API isn’t a developer scrolling through documentation at 2 AM. It’s an AI agent making split-second decisions about which services to integrate, recommend, or build upon. And here’s the uncomfortable truth: if an AI agent can’t find you, you don’t exist. This isn’t hyperbole. It’s the new reality of how software will get built, integrated, and scaled in an AI-first world.

Starting a newsletter from scratch is a laborious and time-consuming process. You have to develop structure, tone, copy, layouts, visuals, and so many other things before you can send out your newsletter. But what if you could use AI to automate this whole process? Well, ChatGPT can empower you to create newsletter templates that are reusable, customizable, and effective, in a matter of minutes!

As artificial intelligence transforms the software development world, junior testers may find themselves asking: Where do I fit in? The rapid adoption of AI tools across the software lifecycle is changing what entry-level testers are asked to do. That’s true from code generation to automated testing. But instead of eliminating opportunities, AI is shifting the career landscape, creating new expectations and valuable growth areas for testing professionals.

Fivetran’s natural language interfaces for AI lower the barrier for business users and boost query velocity for technical teams.

Large Language Models (LLMs) like ChatGPT and Claude offer powerful ways to extract insights from enterprise data. But connecting them directly to your backend databases—without security safeguards—can lead to disaster. A naïve setup, such as giving an LLM raw SQL login credentials, exposes your business to massive risk: credential leaks, SQL injection attacks, and unauthorized data access.

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.

When you add AI to your product, the hardest part is not building the feature but making sure it works safely, reliably, and as intended in the real world. Hemraj Bedassee , Delivery Excellence Practitioner,

Taking place in London and Nashville, Tricentis Transform brings together quality engineering leaders and professionals to hear insights from celebrity keynote speakers, Tricentis executives and customers, and industry experts. They’ll share perspectives on topics ranging from delivering better and faster software with AI to building performance into quality engineering strategies.

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.

As Artificial Intelligence (AI) becomes deeply embedded in decision-making across fraud detection, chatbots, and virtual assistants, trust in AI agents is now critical. Users and stakeholders need clear assurance that these systems will behave fairly, clearly, evidently, and reliably in all situations. However, building that trust does not happen by chance; it requires smarter testing strategies specifically designed for the non-deterministic and robust nature of AI.