This paper presents a model-agnostic assessment of dark energy using DESI DR1 BAO data. The study investigates whether the evidence for dynamical dark energy, previously reported in the DESI DR1 BAO data combined with CMB and SNIa observations, is robust or if it is influenced by the specific dark energy model used. The authors use Gaussian Process (GP) regression, both single-task and multi-task, to reconstruct the dark energy equation of state $ w(z) $ without assuming a specific dark energy model. The results show that the model-agnostic evidence for dynamical dark energy from DESI DR1 BAO data is much less significant than when using the $ w_0w_a $ CDM model. The analysis also provides model-independent constraints on cosmological parameters such as the Hubble constant and the matter-energy density parameter. These reconstructed values are not consistent with the results from the DESI DR1 BAO data using the $ w_0w_a $ CDM model, but they are consistent with the results from the wCDM model. The study also examines the redshift evolution of the dark energy equation of state $ w(z) $ using various combinations of data, including DESI DR1 BAO, non-DESI BAO, CMB, and SNIa data. The results indicate that the $ \Lambda $ CDM model is consistent with the data in most redshift ranges, with deviations generally within the $ 1\sigma $ confidence level. The reconstructed $ w(z) $ is found to be in the non-phantom region for most redshifts, with a small phantom crossing around $ z \approx 1.05 $. The study also explores the Hubble tension and the $ M_B $ tension, finding correlations between the Hubble constant, the $ M_B $ parameter, and the dark energy equation of state. The results suggest that higher values of the Hubble constant are associated with higher values of $ M_B $, and that SNIa data can influence these correlations. In conclusion, the model-agnostic approach using GP regression suggests that the evidence for dynamical dark energy is not as strong as previously thought when using the $ w_0w_a $ CDM model. The results indicate that the $ \Lambda $ CDM model is consistent with the data, and that the deviations from this model are not significant. The study provides model-independent constraints on cosmological parameters and highlights the importance of using multiple data sets to assess dark energy.This paper presents a model-agnostic assessment of dark energy using DESI DR1 BAO data. The study investigates whether the evidence for dynamical dark energy, previously reported in the DESI DR1 BAO data combined with CMB and SNIa observations, is robust or if it is influenced by the specific dark energy model used. The authors use Gaussian Process (GP) regression, both single-task and multi-task, to reconstruct the dark energy equation of state $ w(z) $ without assuming a specific dark energy model. The results show that the model-agnostic evidence for dynamical dark energy from DESI DR1 BAO data is much less significant than when using the $ w_0w_a $ CDM model. The analysis also provides model-independent constraints on cosmological parameters such as the Hubble constant and the matter-energy density parameter. These reconstructed values are not consistent with the results from the DESI DR1 BAO data using the $ w_0w_a $ CDM model, but they are consistent with the results from the wCDM model. The study also examines the redshift evolution of the dark energy equation of state $ w(z) $ using various combinations of data, including DESI DR1 BAO, non-DESI BAO, CMB, and SNIa data. The results indicate that the $ \Lambda $ CDM model is consistent with the data in most redshift ranges, with deviations generally within the $ 1\sigma $ confidence level. The reconstructed $ w(z) $ is found to be in the non-phantom region for most redshifts, with a small phantom crossing around $ z \approx 1.05 $. The study also explores the Hubble tension and the $ M_B $ tension, finding correlations between the Hubble constant, the $ M_B $ parameter, and the dark energy equation of state. The results suggest that higher values of the Hubble constant are associated with higher values of $ M_B $, and that SNIa data can influence these correlations. In conclusion, the model-agnostic approach using GP regression suggests that the evidence for dynamical dark energy is not as strong as previously thought when using the $ w_0w_a $ CDM model. The results indicate that the $ \Lambda $ CDM model is consistent with the data, and that the deviations from this model are not significant. The study provides model-independent constraints on cosmological parameters and highlights the importance of using multiple data sets to assess dark energy.