Systems | Development | Analytics | API | Testing

Why Autonomous AI Agents Can't Run on SaaS Infrastructure

The era of the “copilot” is ending. We are moving rapidly toward the era of the autonomous software factory, where autonomous agents don’t just autocomplete our code—they investigate, plan, test, and merge entire features while we sleep. But this shift has exposed a critical flaw in how we consume AI. For the past decade, the default motion for enterprise software has been SaaS. It’s easy, frictionless, and managed by someone else.

Open Banking: The Guide on APIs, Regulations, and the Future of Finance

The global financial services industry is undergoing a massive, API-driven revolution. With the global open banking market valued at $31.61 billion in 2024 and projected to grow to $135.17 billion by 2030, this shift is accelerating worldwide. This definitive guide explores the core APIs, the evolving global regulations (including FAPI 2.0, PSD3, and Section 1033), and the massive opportunities shaping the future of finance for banks, fintechs, and enterprises.

From Datadog to CI Tests: Catch Regressions Before Deploy

I worked in observability for years, and the same pattern showed up across teams. An alert fired, the on-call rotation scrambled, and everyone did what they had to do to stabilize production. Then came the retrospective. Once the immediate pressure was gone, the conversation shifted to one question: how do we make sure this never happens again? My friend Jade Rubick coined a name for that principle: DRI, “don’t repeat the incident”.

Why do AI agents fail in the enterprise? #aiagents #shorts

Intelligence isn't enough. To make smart decisions, AI agents need context. Shafrine (WSO2) breaks down why integration is the secret sauce to moving AI from a pilot project to a high-performing "agentic" workforce. Learn how connecting your siloed systems provides the "informed decision-making" power agents need to actually get work done.

Custom MCP Server vs. AI Data Gateway: Which Is Right for Enterprise AI?

The Model Context Protocol (MCP) is quickly becoming the standard for how large language models connect to enterprise data. As adoption accelerates, engineering teams face a foundational decision: build a custom MCP server from scratch, or adopt an AI data gateway that ships with MCP support, security, and governance out of the box. Both paths have real tradeoffs. This post breaks them down so you can make the right call for your stack, your team, and your risk profile.