Agents Recognize Source Inconsistency but Rarely Act on It: Decision-Time Mediation for Grounded Resolution

Agents routinely combine heterogeneous information, including user instructions, external web observations, and agent-maintained state, to make decisions. These sources can disagree in ways that leave no principled basis for privileging one source from the available evidence. Existing work typically assumes a predefined correct source, an authority hierarchy, or an adversarially injected signal; less studied is the broader case where multiple plausible sources support incompatible decision-relevant claims and the conflict remains unresolved at decision time. We term this problem **Source Inconsistency (SOIN)** and study whether agents can recognize it, how they behave when it remains unresolved, and whether explicit SOIN checking can improve grounded resolution. Across five models and controlled web-agent settings, we find a pronounced recognition–behavior gap: explicit SOIN detection reaches up to 100.00%, yet spontaneous resolution remains low, with agents instead showing systematic, source-dependent commitment. We introduce a training-free, model-agnostic pre-action mechanism that explicitly checks for SOIN and makes the checking result available to downstream mediation. In conflict cases, providing the full checking result raises grounded task success to 62.51–100.00%, reaching 99.13–100.00% for four of the five models, while success on no-conflict cases remains 98.82–100.00% and performance approaches the oracle condition for most models. The same recognition–behavior gap and intervention benefit persist when DOM-derived and visual observations disagree. Taken together, these results establish unresolved source inconsistency as a distinct reliability problem for agents and show that explicit SOIN checking provides a practical bridge from conflict recognition to grounded resolution.