AgentX

Hey Product Hunt! 👋 AI agents are getting more capable, but evaluating and debugging them is still painful. We built AgentX evaluation framework to help teams test, evaluate, and monitor AI agents before failures reach production. Think CI/CD + observability for AI agents: • Create eval suites • Compare models across providers • Trace failures end-to-end • Get AI-powered root cause analysis and suggested fixes It also run on multiple Agent platform. Our goal is simple: help teams ship reliable AI agents with confidence. Would love to hear, what's been your biggest challenge with AI agent evaluation or debugging?

<p>The hardest part is turning an eval failure into an action boundary, not just a score.</p><p></p><p>For agent workflows, I’d want each failed case to show which tool call or write would have happened, what state it touched, and what receipt or approval would block it next time. Are you modeling external side effects in eval cases, or mostly message/tool correctness for now?</p>

<p>I like the "CI/CD for AI agents" framing. </p><p></p><p>What does a failed deployment look like in AgentX? Can teams set quality thresholds that block releases?</p>

<p>Running the same agent across multiple LLM providers to compare cost/latency is such an underrated feature. <br><br>How many providers do you support right now?</p>