# AI Eval Habits Every Agent Team Should Adopt > Source: > Published: 2026-10-30 10:38:02+00:00 If your eval set hasn’t changed since launch, it’s grading the agent you shipped, not the one you’re running. Three habits that fix that: → Grade the full trajectory, not just the final answer → Use code checks for facts and LLM judges for tone → Turn every failure into a regression test Platforms built for [continuous AI agent evaluation](https://www.botgauge.com) can turn red-team findings into evals automatically. Do you evaluate full trajectories or only outputs? I do both, and the trajectory is where most regressions show up. For each case I assert on the sequence of tool calls: names, argument schema validity, and no repeated side-effecting call after a retry. The final answer can look right while the agent took a wrong or duplicated step. Two additions to your three habits: run each case several times and report the share of runs that pass every check, because one clean run says little about reliability; and replay recorded tool outputs with injected failures such as timeouts and malformed payloads, to confirm the agent stops within its step budget. For regression tests built from failures, I keep the exact trace and make the check name the failure mode.