How Enterprise AI Agents Should Be Evaluated Before Production
QCoding argues that an AI agent can produce a correct final answer while its execution path remains flawed, such as selecting the wrong tool, making duplicate calls, or entering a loop. The article divides enterprise agent evaluation into result, process, and engineering evaluation, covering task success, answer groundedness, tool accuracy, trajectories, latency, token use, and release gates. It says Spring AI provides relevancy and fact-checking evaluators plus traceable tool-calling loops, but applications must still define business-specific correctness and thresholds.
The article offers a practical, repeatable framework for deciding whether an AI agent is ready for production based on both its outputs and its execution behavior.
前言
前面我们已经聊过：
- Tool Calling 怎么让大模型调用业务能力；
- ReAct Agent 怎么动态决定下一步动作；
- Graph / Workflow 怎么编排复杂 AI 流程；
- 企业级 RAG 怎么解决引用、拒答与知识治理。
当 Agent 真正跑起来以后，下一个问题其实更加重要：
Agent 能跑，就代表它能上线了吗？
很多团队刚开始测试 Agent，方法非常直接：
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