Test Automation
The React Native Testing Stack in 2026 React Native's testing landscape has matured significantly. Three tools now cover the full pyramid: Layer Tool What It Catches Unit / Component React Native Testing Library + Jest Component rendering, user interactions, business logic Integration Jest + MSW or custom mocks API calls, state
Test Automation
Astro's build output is mostly static HTML. That makes it fast and SEO-friendly — and it also makes testing feel optional right up until something breaks in production. A misconfigured endpoint returns 500s, an interactive island stops hydrating, or a content collection query returns an empty array after a
AI Testing
You've built a CrewAI crew. Three agents, two tasks, one final output. You ran it locally, the output looked correct, and you're ready to ship. Two weeks later, a user reports the output is wrong. You run it again and it's fine. You run
AI Testing
You built an AI app with the Vercel AI SDK. Streaming works. The chat UI looks right in development. You deploy to Vercel and it works in your quick manual test. Then a user hits a path where the streaming response stalls, your tool call returns data the model doesn&
AI Testing
You prompted v0.dev, got a polished React component in 30 seconds, copied it into your Next.js app, and it looked great. You shipped it. Three days later a user fills out the contact form and nothing happens. The button submits, the loading spinner appears, and then — nothing. No
AI Testing
You built a LangGraph agent. The nodes look right. The graph compiles. You run it manually and it works. Then you ship it, and a user hits an edge case in your routing logic, your agent ends up in an infinite loop, and your production system starts burning tokens until
AI Testing
Testing an AI chatbot is fundamentally different from testing regular software. There's no deterministic output. The same input can produce different responses. The failure mode isn't a crash or a 500 error — it's a confident wrong answer, a hallucinated fact, a response that violates
AI Testing
Amazon released the Strands Agents SDK in May 2025. If your team is already deep in the AWS ecosystem — Bedrock models, Lambda, DynamoDB — Strands is the natural path to production AI agents. It integrates cleanly with AWS services and handles the orchestration layer for you. But clean integration and correct
AI Testing
smolagents is HuggingFace's lightweight Python library for building agents. "Small" is in the name — the library is minimal by design. What that means for testing: you get less scaffolding to hide behind and more direct access to the agent's internals. That's actually
AI Testing
You built a PydanticAI agent. The structured outputs look right. The tool calls return what you expect in development. You run it manually a few times and ship it. Then a user hits a case where the agent calls the wrong tool, your retry logic silently swallows the error, and
AI Testing
You built a multi-agent system with the OpenAI Agents SDK. You have a triage agent, a specialist agent, and handoffs wired between them. You run a few manual tests and the routing looks right. Then a real user hits an input that triggers an ambiguous handoff. The triage agent hands
AI Testing
Your Mastra agent works perfectly in development. You've wired up the tools, the workflow steps chain together cleanly, and the LLM picks the right tool every time you test it manually. Then you ship it. A user hits an edge case your prompt didn't cover, the