Lots of companies have already tested AI agents in small pilot projects. That part is easy. The hard part is putting those agents into real, everyday use safely. Businesses need to know: who is this AI agent acting for? What information can it see? What is it allowed to do?

Building AI agents used to feel like assembling a car from spare parts. Doable, sure, but slow, messy, and painfully technical. That's changing fast. Workato Agent Studio, paired with Model Context Protocol (MCP) integration, is turning what once took weeks of engineering into something teams can actually ship in days.

Automation just got a massive upgrade. For years, we’ve connected apps using rigid "if this happens, do that" rules. But things are changing fast, moving away from static workflows toward smart, thinking assistants.

Automation is the key to working faster and more accurately. Platforms like Workato make automation accessible. But true success isn't just about connecting apps. There has to be a strategy in place for perfect execution.

We are just at the beginning of the AI era, and by now, one thing is clear: to unlock the full potential of AI, it must be connected to the systems and data your business runs on. On its own, AI works, but without context, the impact is limited.

Your recipe won't fail. It will just return half the data. Here's how to audit before March 15.

427 GCP projects. A decade of cloud sprawl. I evaluated Google Remora, SafeScrub, and Cloud Asset Inventory. None fit my needs. So I vibe-coded a Python CLI that found 393 zombie projects. Here's the open-source tool and the lessons learned.

Manual task creation quietly kills developer productivity. This post shows how Cursor Vibe Coding and MCP make task creation part of the development flow, capturing real context from code and the browser to generate clear, high quality tasks the moment issues are discovered, without breaking focus.