Abstract In this demonstration, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making. Imposing transparency and explainability requirements on such agents is crucial for establishing human trust and common ground with an end-to-end automated planning system. Visualizing the agent’s internal decision making processes is a crucial step towards achieving this. This may include externalizing the “brain” of the agent: starting from its sensory inputs, to progressively higher order decisions made by it in order to drive its planning components. We demonstrate these functionalities in the context of a smart assistant in the Cognitive Environments Laboratory at IBM’s T.J. Watson Research Center. Recently, there have been concerted efforts towards making the outputs of planning processes more palatable to human decision makers – e.g. eXplainable AI Planning (XAIP) [Fox et al., 2017; Langley et al., 2017]. One of the key features that an XAIP agent must support is visualization. For an endto-end planning system – which goes from lower level sensory data (e.g. vision, speech) to progressively higher level decision-making capabilities (planning, plan recognition) – this becomes even more challenging. It is in this spirit that we present Mr.Jones, a set of visualization capabilities for an XAIP agent that assists with human-in-the-loop decisionmaking in an instrumented meeting space. Introducing Mr.Jones – Mr.Jones [Chakraborti et al., 2017c], situated in the CEL – the Cognitive Environments Laboratory – at IBM’s T.J. Watson Research Center is designed to embody the key properties of a proactive assistant while fulfilling the properties desired of an XAIP agent. Sim-ilar to [Manikonda et al., 2017], we divide the responsibilitieof Mr.Jones into two processes (c.f. Figure 1) -– Engage, where plan recognition techniques are used to identify the task in progress; and Orchestrate, which involves active participation in the decision-making process via real-time plan generation, visualization, and monitoring.