Systems | Development | Analytics | API | Testing

What Breaking AI Applications Taught Us About Building Reliable Ones

The global industry is currently in a feverish rush to "AI-enhance" every facet of the digital landscape. However, a critical distinction has emerged: while building an AI-integrated application is relatively simple, engineering one that maintains operational integrity in a production environment represents a watershed moment for modern engineering teams. BugRaptors spent the last year inside the intricate internal logic and non-deterministic layers of AI application testin g.

Conversational, contextual, immediate: Inside Yellowfin 9.17 - webinar replay with live Q and A

Business intelligence should be..intelligent. It should be helpful. It should be powerful and sophisticated, but not complicated. Welcome to the new Yellowfin 9.17 where you can talk to your data and get your data to speak volumes for you. Join us for an exclusive look at Yellowfin 9.17, where analytics becomes more conversational, more responsive, and more aligned with the way real decisions are made.

What If SAP Scale Was No Longer a Concern?

For years, SAP leaders have been told a familiar story: Scale carefully. Don’t outgrow your infrastructure. Hope your next acquisition fits inside your existing SAP footprint. Behind the scenes, many SAP teams have been managing risk not by innovating, but by working around the limits of their storage platforms. CIOs, for example, are increasingly prioritizing platform consolidation, with 75% of organizations pursuing vendor consolidation as fragmented, aging architectures become harder to manage.

AI Coding Agents Break What Works

Your AI coding agent just made every test pass. Ship it, right? Not so fast. A growing class of AI-generated bugs doesn’t come from writing bad code. It comes from the AI changing working code to accommodate its own mistakes. This isn’t a theoretical risk. It’s happening now, in production codebases, and it’s harder to catch than any bug the AI might introduce from scratch.

Appian Q1 Product Highlights: Modernize Faster, Automate Smarter

Appian’s latest updates deliver powerful new tools to consolidate legacy systems, automate complex knowledge work, and scale data integration. Modernization projects are notoriously high risk, but Composer derisks the start of your journey by ensuring total stakeholder alignment before development begins.

BearQ Q&A recap: Top questions from SmartBear's live event

Asked a question in our BearQ livestream? We’ve got your answers. We received 100+ questions during the event and couldn’t get to all of them live, so we pulled together the most common ones and answered them here. In this video, we break down what BearQ can test, how it handles authentication and complex workflows, how the AI works behind the scenes, how it fits into your existing tools, and even how to get early access.

Where Speed Meets Compliance: Spotter for Modern Financial Services Teams

This is one question a banking team can ask Spotter right now, "Which customers are likely to churn based on declining balance activity over the last 90 days?" No ticket to the data team, no waiting on a dashboard build, and no SQL. Just a plain-language question and an answer your retention team can act on today. That's the shift from reactive reporting to agentic analytics. Your data answers back.

Real Device Access API - Product Demo

Building Internal Developer Tools with a Device Lab API: Sessions, Streaming, Logs, and Automation For years, platform teams have had to choose between costly internal device labs for control or public clouds with limited access. That tradeoff ends with the Real Device Access API, the first solution to treat mobile devices as Infrastructure-as-Code—delivering direct, low-latency access to real devices without framework constraints. See how teams can retire internal racks while running any workflow on fully managed infrastructure they control programmatically.

From Microservices to AI Traffic: Kong's Unified Control Plane When Architecture Gets Complicated

Modern enterprise architecture faces a three-body problem. Three distinct traffic patterns pull your teams in different directions. External APIs serve mobile apps and partner integrations. Internal microservices communicate within Kubernetes clusters. AI and LLM calls flow to OpenAI, AWS Bedrock, and self-hosted models. Each pattern looks API-like on the surface. Yet many organizations handle them with separate tools. The result?