Systems | Development | Analytics | API | Testing

Learn the latest best practices for knowing which mobile devices to test on, plus our recommended devices to test on by region and business size.

Process automation has been around for decades, but the tools under this technology umbrella have multiplied over the years. Robotic process automation (RPA) was an early tool for handling simple, routine tasks, and it’s still powerful to have in your intelligent automation arsenal. But when technologies like intelligent document processing, business rules, and workflow orchestration entered the scene, they brought new capabilities to the process automation suite.

Have you ever found it a bit tedious to enter the same release notes text every time you submit an app for review in the stores? In this article, I’ll introduce a method to automate this small but repetitive task. It’s very easy to implement, so please read on.

Just like every impressive building starts with a strong foundation, every remarkable capability in an AI agent can be traced back to its framework. AI agent frameworks or agentic AI frameworks make it possible to create smart, efficient AI agents that can serve as simple chatbots, facilitate agentic automation, or contribute to complex use cases in finance, supply chain, healthcare, manufacturing, and robotics as part of a multi-agent system. But what are AI agent frameworks?

Anyshift provides AI context for your infrastructure, powered by Annie—an AI infrastructure assistant trained on your environment. From answering complex infrastructure questions to suggesting Terraform code and catching hidden issues, Annie helps teams manage, monitor, and optimize their infrastructure with ease and precision. Unlike generic AI copilots, Anyshift provides context-aware insights based on your actual infrastructure and codebase—not just LLM guesses.

Emerging technologies like generative AI (GenAI) and agentic AI are poised to significantly enhance IT operations. These advancements offer new, truly transformative ways to manage, optimize and automate IT environments, and are certain to improve efficiency and foster innovation. GenAI’s ability to process vast amounts of unstructured data and agentic AI’s autonomous decision-making capabilities span predictive analytics to automated problem-solving.

BigQuery ML adds TimesFM, LLM-based structured output generation, row-wise inference functions, expanded model choice, and Contribution Analysis GA.

Your organization may be one of the many that is rushing to implement AI. But do you know where you fall on the AI maturity model? More than just a framework for understanding AI, the AI maturity model is a strategic guide that helps turn AI investments into tangible business results. A 2024 IDC study commissioned by Microsoft titled “The Business Opportunity of AI” found that organizations gain a $3.7x return for every $1 spent on generative AI.

For the past several years, there have been murmurs of an incoming convergence between API management and event management.