10 agent eval patterns every AI engineer should know, from golden sets to trajectory scoring
Roger_M_Taylor · x · 2026-07-25
A reposted thread lays out the 10 agent evals every AI engineer should know. It highlights practical evaluation patterns such as:
- Golden sets: frozen test cases you rerun after prompt, model, or tool changes to catch regressions.
- LLM-as-judge: use a second model with a rubric when there is no exact ground-truth output.
- Rubric scoring: score correctness, tone, safety, and cost separately instead of collapsing everything into one number.
- Trajectory eval: grade the agent’s path, not just the final answer, including actions, decisions, and tool calls.
The thread also points to existing tools like OpenAI Evals, OpenEvals, DeepEval, and AgentEvals as examples of how teams can make evaluation repeatable and more granular.
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