AI-powered testing guides — how to use AI to generate, run, and maintain automated tests faster.
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
AI Testing
Your n8n workflow runs fine in your test environment. You've triggered it manually a dozen times. Everything looks good. Two weeks after you ship it, a user submits a form, the AI node returns an unexpected JSON structure, the downstream transformation fails, and your workflow silently stops. No
MCP
You built an MCP server. You tested it locally. Claude can call your tools. You're ready to ship. Then a user pastes a prompt, your tool gets called with an edge-case input, and it silently fails. No exception. No error. The agent just gets back garbage and hallucinates
AI Testing
You built a LlamaIndex RAG pipeline. The retrieval looks right on your test documents. The LLM synthesizes clean answers. You ship it. Then users start getting wrong answers. The retrieval is returning the right documents — mostly. The LLM is synthesizing correctly — mostly. But "mostly correct" at retrieval times
AI Testing
It's 2 AM. Your customer support chatbot — built on a LangFlow pipeline — starts returning blank responses. Turns out someone updated the OpenAI model name from gpt-4 to gpt-4o in the LangFlow UI three days ago. The change looked fine in manual testing. No alerts fired. By the time
AI Testing
You built a Haystack pipeline. The retriever finds relevant documents, the prompt template structures them correctly, and the generator produces clean answers. It works in your test environment. You deploy it. Three days later a customer reports that the chatbot is confidently answering with information from six months ago. The
AI Testing
Google's Agent Development Kit (ADK) makes it fast to build production-grade AI agents. Within a few hundred lines of Python, you have a multi-agent system with tool use, session management, and streaming responses. What ADK doesn't give you is a testing framework. That's your
AI Testing
Google released Gemini CLI in 2026, and developers are already using it to build and scaffold applications at speed. Like Cursor and Claude Code before it, Gemini CLI dramatically accelerates how fast you can go from idea to running code. The problem is the same one that emerged with all
AI Testing
You built a Flowise chatflow, connected a few nodes, and it works perfectly in the canvas. Then you deploy it, a user asks a slightly different question than your test prompts, the chain returns a hallucinated answer, and the retrieval node quietly returned zero results. You find out from a
AI Testing
Your DSPy program compiles cleanly. The optimizer finds a configuration that scores 87% on your dev set. You ship it. Three days later, your support queue fills up with users saying the outputs are wrong. The optimizer did its job. It improved accuracy on the metric you gave it. But