Systems | Development | Analytics | API | Testing

dreamfactory

AI Data Gateways & Data Governance: Scaling Trustworthy LLM Agents

Feb 13, 2026 By Terence Bennett In DreamFactory
As AI agents move from prototype to production, organizations face a growing paradox: how to give these agents enough access to unlock business value—without compromising privacy, compliance, or control. This isn’t just an integration problem. As soon as you map API layers or ask how a generative agent might retrieve sensitive customer records, the challenge becomes one of governance, scale, and trust.
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smartbear

Celebrating Datalex: Setting the standard for developer visibility in API-first development

Feb 13, 2026 By Abby Myers In SmartBear
At SmartBear, we recognize organizations that improve software quality by increasing clarity, alignment, and confidence across the development lifecycle with the Developer Visibility Award. For 2025, the award goes to Datalex, a leading airline e-commerce solutions provider. Datalex equips airlines with API-driven platforms that provide tools for driving revenue and profit as digital retailers.
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speedscale

Why Your Company Will Be Running OpenClaw Next Year

Feb 12, 2026 By Josh Thornton In Speedscale
You’ve probably heard of OpenClaw. Maybe you’ve seen the demos where an AI agent opens a browser, navigates to your CRM, fills in a form, and files a support ticket. No API required. Maybe you thought “that’s cool but I’d never run that at work.” Your employees already are. According to Permiso’s research, 22% of enterprise customers have employees running OpenClaw without IT approval.
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speedscale

How AI Coding Is Breaking Synthetic Data Generation

Feb 12, 2026 By Matthew LeRay In Speedscale
Traditional synthetic data generation approaches, still called “Test Data Management” (TDM) by legacy vendor, were designed for a world where applications were monolithic, databases were the center of gravity and change happened slowly. The world looks a lot different now. Modern systems are distributed, often times event-driven, and increasingly powered by streaming data and AI agents. In this environment, batch-oriented synthetic data generation fails to capture how systems actually behave.
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speedscale

DLP, Traffic Replay, and the Missing Link to Software Quality

Feb 12, 2026 By Matthew LeRay In Speedscale
In Part 1 and Part 2 we explored why testing modern software is so difficult. Production data is the most valuable input for testing, but it’s locked away because it contains PII and sensitive context. Traditional Synthetic Data Generation (SDG) was built for batch databases, not streaming systems. And AI coding agents amplify every weakness in existing test strategies because they need current, realistic data or they generate buggy code based on outdated assumptions.
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SmartBear QMetry's AI-based test generation: Execute tests in minutes

Feb 12, 2026 By SmartBear In SmartBear
In this video, you’ll discover how SmartBear QMetry's AI-powered test generation automatically transforms requirements into complete, executable test cases within minutes. Watch as we demonstrate test generation cases from Jira, Rally, and Azure requirements, demonstrate how to refine existing tests, and save your teams hours of manual work.
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keploy

State Transition Testing: Diagrams, Tables & Examples

Feb 12, 2026 By Keploy In Keploy
Ever seen a workflow pass QA, then fail the moment users retry, refresh, or hit a timeout? That gap usually isn’t about a “wrong input.” It’s often because the system is in a different state when the same input arrives. In state transition in software testing, the state decides what’s allowed, what must be blocked, and what should happen next. It is one of the simplest ways to make these workflows behave predictably in the real world.
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speedscale

What AI Has Never Seen: The Context Gap in Code Generation

Feb 11, 2026 By Ken Ahrens In Speedscale
Your AI coding assistant has read the entire internet. It knows every programming language, every framework, every best practice documented in Stack Overflow answers and GitHub repositories. It can generate a REST API handler in seconds that looks perfect with clean code, proper error handling, following all the patterns. But here's what it's never seen: your production traffic. Data from a real API request. Someone filling out a form with messed up or incomplete data. AI is changing how we write and test code, but there's a fundamental gap between training data and production reality.
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