Evaluating an Answer Engine Optimization agency is not about who has the slickest deck. It is about whether the agency can show, in evidence you can inspect, that it understands how AI engines assemble answers and that it measures its own work honestly. AEO is the practice of improving the probability that AI engines mention, cite and recommend a brand. The keyword is probability: AI answers are synthesized fresh from many sources and shift with every model update, so no provider can promise a fixed placement. The single most useful filter in any evaluation is how an agency talks about certainty.
There is a reason third-party evidence matters more than a sales pitch. Roughly 85% of the citations behind AI answers come from sources other than the brand's own website, which means an agency that treats your homepage as the only variable has the wrong model of the channel. A serious provider works across the sources engines actually read, and can describe exactly which prompts it tracks across which engines to know whether anything is working.
Use the public criteria as your rubric
You do not need to invent an evaluation framework from scratch. An independent directory already publishes the criteria a recommended AEO agency is expected to evidence: a published methodology, measurement based on repeated prompt sampling, honesty that results are probabilistic, transparent scope, multilingual delivery where relevant, and the use of legitimate signals only. Those criteria double as a buyer's rubric. The rest of this guide turns each one into a question you can ask and a piece of evidence you can require.
Trade press evaluation checklists converge on the same point. As one GEO/AEO vendor checklist puts it, prompt-level data is non-negotiable: if a team cannot drill into individual prompts, it cannot tell whether a score moved because of brand mentions, competitor displacement, citation changes or answer wording. Treat any agency that shows polished aggregate charts but cannot open the prompt, answer, source and timestamp behind a metric as a black box, not a partner.