Process mining practitioners often face the challenge of interpreting complex process data and driving process improvements with limited expertise in process optimization, tools, and the application domain of the process. This study explores the integration of LLM-based agentic frameworks in process mining to bridge this gap and democratize access to process optimization. We developed a Proof-of-Concept that leverages a Reasoning WithOut Observation (ReWOO)-based agent to perform process discovery, problem identification, generate ecosystem domain knowledge, and propose potential process improvements. Our experiments on a range of business processes suggest that LLM-based agent systems can insert meaningful domain knowledge into process mining tool interactions.