Systems | Development | Analytics | API | Testing

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.

BigQuery ML TimesFM model is now in preview, and forecasts datasets through the use of generative AI.

Data AI Gateways are reshaping how businesses manage APIs by automating key processes like creation, security, and scaling. These platforms simplify API operations, reduce costs, and improve efficiency, making them essential for enterprises navigating AI adoption. Here's what you need to know: What They Do: Automatically generate APIs, enforce security (e.g., RBAC), and integrate multiple databases. Why They Matter: Tackle challenges like siloed systems, scaling, and AI governance.

RAG (Retrieval-Augmented Generation) is not a new concept in AI, and unsurprisingly, when talking to companies, everyone seems to have their own interpretation of how to implement it. So, let’s start with a refresher. RAG (short for Retrieval-Augmented Generation) is a technique that injects relevant data from an external knowledge source directly into a prompt before sending it to a Large Language Model (LLM). “But wait, my model is already fine-tuned on my domain-specific data.

Forward-thinking data leaders are using automation, real-time data movement, and genAI to personalize customer experiences, optimize operations, and unlock scalable innovation.

APIs have long been the backbone of modern software systems, architectures, and businesses. They now dominate the web, accounting for 71% of all internet traffic. Generative AI is accelerating this trend especially as we pivot our interaction with common web-based capabilities, like “search” in favour of AI-enriched variants. More AI leads to more APIs, and with that, APIs act as an important mechanism to move data into and out of AI applications, AI agents, and Large Language Models (LLMs).

AI systems need both structured data (like spreadsheets) and unstructured data (like images or text). SQL databases excel at structured data, while vector databases handle unstructured data for tasks like similarity searches. The solution? Hybrid AI stacks that combine both through unified Data AI Gateways.

In 2025, AI-powered coding platforms have rapidly moved from "nice to have " to an important part of modern developers. The tools that caught everyone’s attention in these are Windsurf and Cursor. It is hard to choose between the Windsurf vs cursor – they are mostly similar. They are both IDEs (integrated development environments) which are mostly developed for the vibe coders, i.e. non non-coders who build apps for fun and work.

Generative AI is transforming how businesses operate, with 74% of enterprises already deploying it in production by 2025. The technology offers measurable benefits like a 1.7x ROI and cost reductions of 26–31% in key areas like supply chain and customer operations. But with rapid adoption comes serious risks - data breaches, AI bias, and compliance issues are top concerns.