AgentX

AgentX is an AI agent evaluation and observability framework for testing production agents, surfacing failures, and feeding fixes back into the release loop. Its official evaluation page describes continuous evals, multi-step traces, agent CI/CD gates, drift monitoring, and LLM-judge based scoring.

• Evaluation from real data — builds test sets from documents, knowledge bases, and production traces • Multi-step agent analysis — inspects prompts, skills, memory, tools, MCPs, workflows, and execution timelines • CI/CD guardrails — can block deployments when evaluations fail and promote agents when they pass • Failure diagnosis — reports score breakdowns, hidden behavior patterns, and suggested fixes • Production monitoring — official copy covers regression suites, benchmark suites, prompt drift, dataset drift, and business-impact metrics