What does it take (for a robot) to be a ‘linguistic agent’? A linguistic agent can be visualized as a robot navigating, interacting with, and ultimately contributing to the building (“terraforming”) of a Universal Knowledge Graph. The primary criteria of success is not simply guessing the correct answer, but demonstrating a full understanding of what is being said – by generating the full logical form for any sentence or phrase that is being processed in a dialog. The Logical Form must state BOTH the denotation of a sentence, AND also the set of relevant presuppositions. Conversational agents always have to deal with other such agents. A multi-agent environment is the natural habitat of a linguistic agent. In a multi-agent setting, each agent exchanges messages with other agents. This goes to the very essence of natural language. Importantly, it must have access to a scientific linguistic parser, able to handle ‘ellipsis’ and other kinds of gaps and recover the “understood” material. As is well known, quantifiers play a very central role in the semantics of natural language. In order to support quantifiers, it is necessary to handle bound variables. Finally, no conversational agent can get away with failing to understand elementary logic – it is a basic component of the normal use of language. To qualify as a linguistic agent, a learning agent must be conversant in Second Order Predicate Calculus (and some elementary set theory) – even BEFORE starting to learn its own field of specialization. This requires machine learning to be in some way combined with symbolic reasoning.