How to Evaluate an Enterprise AI Agent for Production

With Cursor pivoting to enterprise agents and Notion launching workspace agents, you're going to be bombarded with pitches for AI agents that 'manage your entire workflow.' Before you buy, you need a framework to evaluate them. The key dimensions are autonomy, reliability, observability, and integration depth. Autonomy means how much the agent can do without human approval — some agents only suggest actions, others execute them. For production use, you want configurable autonomy: allow low-risk actions (like updating a task status) automatically, but require approval for high-risk actions (like deploying code or deleting data). Reliability is about consistency. Ask the vendor for their mean-time-between-failures (MTBF) for agent actions. If they can't provide it, they're not ready for production. You should also test the agent on your own data — run 100 random tasks and measure success rate, time, and error types. Observability is often overlooked but critical. Can you see why the agent took a particular action? Does it log its reasoning? Can you replay a failed action to debug it? Without observability, you're flying blind. Integration depth is the final piece. A good agent should plug into your existing tools — Slack, Jira, GitHub, Notion — without requiring custom middleware. Check if the agent supports webhooks, API access, and custom action definitions. The vendors that pass all four tests are worth a pilot. The rest are toys.