Agentic AI systems are increasingly deployed as personal assistants and virtual employees, and are likely to become a common object of digital investigations and incident response. Yet little is known about how their internal state and actions can be reconstructed during forensic analysis, and systematic forensic approaches remain largely unexplored. We present the first empirical forensic study of a personal AI assistant, with OpenClaw as a case study. We examine its technical design via static code analysis and apply differential forensic analysis to identify recoverable traces across stages of the agent interaction loop. We classify and correlate these traces, structuring the examination around representative investigative questions. We propose a five-plane agent artifact taxonomy that captures recurring investigative patterns; its structural completeness is assessed by cross-mapping against published artifact inventories of two related AI systems. To aid investigations, we release an open-source artifact-extraction tool with a machine-readable schema. Finally, we highlight a foundational challenge: LLM-mediated execution adds both an abstraction layer and substantial nondeterminism to trace generation. Because tool selection and state transitions are co-determined by the model, the execution environment, and the evolving context, identical inputs need not yield identical traces.

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