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
AI Testing
Your AutoGen pipeline worked fine in dev. The Planner agent handed off to the Executor, the Executor called the right tool, the Critic reviewed the output, and everything terminated cleanly. Then you deployed it. Now the Executor occasionally ignores the Planner's instructions, the conversation sometimes loops 40 times
AI Testing
Your AI agent shipped. Traffic is flowing. Everything looks clean in your LLM observability dashboard — latency is fine, token usage is stable, no exceptions in the logs. And somewhere, quietly, your agent is returning the wrong answer to every third user. This is the silent failure problem. It is not
Testing
You built a FastAPI service. The endpoints return the right data in your terminal. You open the docs at /docs and everything looks correct. You ship it. Then a user sends a payload with a missing optional field your validator doesn't enforce, your async database call times out
AI Testing
You built an Agno agent team. The researcher agent finds the relevant data, the analyst agent synthesizes it, and the writer agent produces the final output. Each agent works correctly in isolation. You deploy the team. In production, the researcher agent returns a result in an unexpected format, the analyst
AI Testing
Your AI agent can read the filesystem, query your database, call external APIs, and write files. It does all of this through MCP tools — and you have no idea if it's doing it correctly. Welcome to the new testing gap. What MCP Is (And Why It Changes Everything)