AI-powered testing guides — how to use AI to generate, run, and maintain automated tests faster.
Testing
Amazon Kiro is the most interesting AI IDE to launch since Cursor — and it has a genuinely different approach to code generation that changes how testing needs to work. Where Cursor and Cline take a prompt-and-iterate approach, Kiro is spec-driven: you describe a feature in plain English,
Testing
Cline has 4 million VS Code installs and is growing fast. It scaffolds entire features, runs terminal commands, and iterates on failures — which means it also generates a lot of code that needs testing. This guide covers how to test applications built with Cline: what kinds of bugs Cline code
AI Testing
Bolt.new just built you a full-stack app in 20 minutes. It looks right. The preview works. You're about to share the URL. Before you do: have you actually tested it? Not "does it load" — that's not a test. Have you verified that
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