Systems | Development | Analytics | API | Testing

Traditional test coverage focuses on code paths and user flows but agentic AI doesn’t follow flows. It reasons, adapts, and improvises. That means your 95% coverage report might look solid while the system still makes unsafe, biased, or unexpected decisions. To test these systems, coverage must evolve: you now measure things like goal alignment, reasoning paths, tool usage patterns, memory accuracy, and failure behavior.

If you are a developer that is running Cursor as your IDE, you have probably had the ability to experiment with different AI agents in pursuit of productivity. One of the most exciting new offerings is DeepSeek V3 is open-source LLM, with added capabilities for code generation, reasoning, and multi-turn conversations.

Automate your Databricks to BigQuery migration. Learn how to use Gemini to translate complex SQL code accurately and save hours of manual work.

Microsoft Excel is a powerful tool, and it’s useful for more than numbers. Many people use Excel to manage tasks in their everyday lives beyond financial calculations. Most Excel users aren’t aware of all the intricate tips and tricks that help you get the most out of Excel. There are so many functions and formulas to help you slice and dice numbers or give that data a new look, it’s impossible to recount them all.

As enterprises scale their DevOps initiatives, DevOps bottlenecks are emerging from an unexpected source: inadequate test data management practices. But what can you do about it? In this blog, we will explore the common drawbacks in traditional test data management practices. Then, let’s dive into how real-life enterprises are overcoming DevOps bottlenecks with proven solutions that accelerate delivery and reduce operational risks.

Software is now shipped faster than ever and testing evolved beyond rigid scripts and predefined steps. One approach that has always embraced adaptability, critical thinking, and curiosity is exploratory testing: the process of learning, designing, and executing tests simultaneously — often uncovering issues that traditional testing might miss. As Artificial Intelligence (AI) becomes more embedded in the software development lifecycle, many wonder: will AI replace exploratory testing?

If you build APIs, you’ve probably been in two kinds of conversations. One is the war room, where the team is chasing a latency spike, an outage, or a sudden drop in traffic. The other is the boardroom, where you need to show how the platform is performing, which APIs are growing, and whether your investments are paying off. Dashboards are the tool people turn to in both cases.

Modern innovation demands fast, secure, and flexible access to data. But when organizations deal with scattered databases and strict security policies, manual API development slows everything down. The solution? Automate how APIs are built, secured, and managed—using AI and open-source tools like DreamFactory.

Tired of wasting hours triaging errors and digging through logs? With BugSnag from SmartBear and GitHub Copilot, you can now debug faster : AI surfaces the root cause, explains the issue, and helps you fix it – all from your IDE. Powered by SmartBear’s integration with the Model Context Protocol (MCP), and bringing runtime context into your development tools, errors from BugSnag are automatically enriched with the technical metadata your LLM needs to deliver accurate, relevant suggestions.