Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling

AI agents increasingly interact with unfamiliar black-box counterparts whose behavior emerges from language models, prompts, memory, and control logic. We study whether a counterpart's next decision can be predicted from a small set of observed interactions, without access to its implementation. We cast this problem as target-adaptive text-tabular inference, combining behavioral examples from the specific target with the current strategic state, the dialogue, and a representation from a frozen language model Observer. Across bargaining and negotiation environments and a diverse population of independently built agents, adaptation to the target improves prediction in ways that additional population data cannot reproduce. Observer representations provide complementary information beyond explicit state and generic text features, but their value depends on what the structured interaction history already captures. These findings separate learning the behavior of a particular agent from representing the strategic situation, and provide a practical framework for predicting unfamiliar agents from limited interaction.