If you mostly use chatbots and you're starting to budget for agents, use one filter first: no eval set, no real agent budget. For step-by-step AI helpers, spending before you define pass/fail tasks is guesswork. [C002]
You see a new agent tool, almost scroll past, then think: should I follow this or ignore it? Don't judge an AI update by the feature list. Judge it by whether it changes your next decision. That is the real point behind How to manage AI investments in the agentic era. [C001]
The costly mistake is copying SaaS logic: buy access, roll it out, watch usage. That breaks here. A step-by-step agent can fail in the model, the external tool, or the workflow. The bill will not tell you where the leak is.
OpenAI's eval guidance is plain on this: use representative sample tasks and human-written ground truth. In normal English, build a small exam before you scale. If you do not know what passing looks like, you do not know what you are paying for.
The OAgents paper (arXiv:2506.15741) adds the second warning: many agent results are hard to reproduce and can vary a lot across runs. So a flashy demo matters less than a boring pass/fail set. My boundary: this is for planning a step-by-step AI helper before production, not ranking one live stack. Next move: write the small test set first. If your team is about to approve spend without it, share this.