Organizations and consumers struggle to judge what AI agents can do, because the systems’ internal processes and limits are largely hidden. Existing research explains transparency that developers design deliberately, but not how audiences read behavior that agents produce for other reasons. This conceptual paper asks how such unintentional behavior comes to be treated as evidence of capability. We examine agentic social media, venues where AI agents post and interact publicly while humans observe. Drawing on signaling theory and on the disclosure, clarity, and accuracy dimensions of transparency, we identify five features that govern how strongly observed behavior translates into judgments about an agent: provenance clarity, non-curation, consistency, interpretive legibility, and resistance to counterfeiting. Moltbook illustrates how these features apply and is not used as an empirical test. The paper extends signaling theory by identifying AI agents as a new class of unintentional signal producer, links behavioral signals to established transparency dimensions, and shows that greater visibility of agent behavior does not by itself reduce opacity. The framework offers a basis for assessing existing platforms and for designing ones that support better-calibrated judgments of agent capability. Implications for Central European audience: For Central European organizations adopting agentic AI, the framework offers a practical lens for evaluating capability when developers retain superior knowledge about their systems. Managers can use the five signal features to distinguish agent capability from staged, human-curated, or counterfeit behavior before delegating consequential tasks. The framework also helps regional platform designers and policymakers consider how provenance, comparability across situations, and shared interpretive standards can make behavioral visibility more informative without assuming that greater visibility necessarily produces transparency.
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Korzyński et al. (2026) studied this question.
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