As DNA sequencing becomes faster and cheaper, genomics-based approaches are being explored for their use in personalized diagnoses and treatments. Here, we provide a proof of principle for disease monitoring using personal metagenomic sequencing and traditional clinical microbiology by focusing on three adults with cystic fibrosis (CF). The CF lung is a dynamic environment that hosts a complex ecosystem composed of bacteria, viruses, and fungi that can vary in space and time. Not surprisingly, the microbiome data from the induced sputum samples we collected revealed a significant amount of species diversity not seen in routine clinical laboratory cultures. The relative abundances of several species changed as clinical treatment was altered, enabling the identification of the climax and attack communities that were proposed in an earlier work. All patient microbiomes encoded a diversity of mechanisms to resist antibiotics, consistent with the characteristics of multidrug-resistant microbial communities that are commonly observed in CF patients. The metabolic potentials of these communities differed by the health status and recovery route of each patient. Thus, this pilot study provides an example of how metagenomic data might be used with clinical assessments for the development of treatments tailored to individual patients.