Systems | Development | Analytics | API | Testing

AI is becoming part of every stage of the testing lifecycle. Teams are using it to analyze requirements, design test cases, generate automation scripts, and accelerate execution activities that previously required significant manual effort. Within Xray, AI already helps transform Jira requirements into actionable test cases with AI Test Case Generation.

Most enterprises are spending on AI faster than they’re becoming ready to run it. Over the next two years, IT leaders plan to grow their AI investment by 70%, according to our 2025 State of Data Infrastructure Global Report. The capability keeps arriving. The readiness to operate it does not keep pace.

You have tested hundreds of features. You know the drill. Open the test case, write the preconditions, list the steps, fill in the expected result, run it, compare. Pass or fail. Move on. Then someone hands you an AI feature. A chatbot. A "summarize this ticket" button. A search box that answers in full sentences instead of returning a list of links. You open your test case template, you get to the "expected result" field, and you stop.

Every Monday morning, someone on your team downloads a report, opens it in Excel, cleans up the column headers, removes the blank rows, and uploads it to Salesforce or Snowflake. Then they do it again on Tuesday for a different source. By Friday, half their week is gone, and the dashboard is still showing last week's numbers.

Managing data pipelines across AWS, Azure, and GCP simultaneously is one of the most demanding infrastructure challenges data teams face today. Native cloud services like AWS Glue and Azure Data Factory solve problems within their own ecosystems, but they create friction the moment data needs to move across provider boundaries.

A dropped connection used to cost a typing indicator. With AI in the product, it costs an entire response, mid-generation. That single shift is enough to reopen a decision most teams made years ago and stopped thinking about: whether to keep building realtime infrastructure themselves, or buy it. Fin, the AI agent platform formerly known as Intercom, made the call to buy. It had run its own realtime system, Nexus, for years, at the scale of one of the biggest support platforms on the internet.

A lot of teams think their AI quality problem begins when the first test run starts but it usually begins much earlier.

Node.js debugging allows us to identify and fix errors, unexpected behavior, and performance issues in server-side JavaScript applications. Instead of using the less precise console.log, we can connect a real debugger and control execution step by step. Most Node.js developers actually use one of two distinct setups: This guide will show you how to use both technologies, with a clear step-by-step workflow for each.

Five paths give Fivetran the flexibility to connect virtually any data source, from SaaS applications and flat files to application databases, proprietary APIs, and real-time event streams.