(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0.Autotelic agents are described as learning to represent, generate, select and solve their own goals, and a new generation of systems built on foundation models is reported to do so without hand-coded goal representations, without human intervention, or from zero data. This paper separates four things that the phrase "the agent generates its own goals" bundles together: the goal space in which any goal can be expressed, the selector that decides which goal to pursue next, the success test that decides whether a goal was reached, and the referee that decides whether the goals produced were new or worth having. An audit of 28 published systems and system families, from engineered-goal-space robotics to language-model task proposers, records where each component sits. The referee is outside the agent in all of them, but that is structural rather than a finding: a published evaluation is by definition its authors'. The substantive results concern the other three. The selector is computed by the agent in all 28. The goal space is written by the designers in the engineered systems and learned from designer-chosen data or objectives in the learned-space systems. In every foundation-model system it is bounded, on top of what pretraining makes available, by something the designers wrote or chose: a prompt, a seed list, an output format, a task-type menu, a simulator or the environment itself. The success test is supplied in about half of the rows (at least 13 of the 28), and where it is the model's own, the two systems that measured it against an external reference found it less accurate on later or harder self-generated goals. Where goals leave the region occupied by human examples, human raters score them as less understandable and less human-like, though the published analysis cannot separate a referee that fails to credit new goals from a generator that produces worse ones. The published ablations show that the designer-held components are not inert, but they do not rank them above the self-directed ones: in Absolute Zero, removing two designer-chosen task types and replacing the proposer's own earlier tasks with a fixed prompt cost comparable amounts, and the LMA3 comparison is qualitative. A foundation model in the loop moves the space and the referee from the designer's grammar into a pretraining corpus and a prompt; it does not remove them. On present evidence, the foundation-model systems generate goals within a supplied space, which is Sigaud et al.'s first-order open-ended case, and the tests that would support a second-order reading have not been run.