Nuclei segmentation and classification in Hematoxylin and Eosin (H&E) stained histology images play a vital role in cancer diagnosis, treatment planning, and research. However, accurate segmentation can be hindered by factors like irregular cell shapes, unclear boundaries, and class imbalance. To address these challenges, we propose the Adaptive Gated Attention Fusion Network (AGAFNet), which integrates three innovative attention-based blocks into a U-shaped architecture complemented by dedicated decoders for both segmentation and classification tasks. These blocks comprise the Channel-wise and Spatial Attention Integration Block (CSAIB) for enhanced feature representation and selective focus on informative regions; the Adaptive Gated Convolutional Block (AGCB) for robust feature selection throughout the network; and the Fusion Attention Refinement Block (FARB) for effective information fusion. AGAFNet leverages these elements to provide a robust solution for precise nuclei segmentation and classification in H&E stained histology images. We evaluate the performance of AGAFNet on three large-scale multi-tissue datasets: PanNuke, CoNSeP, and Lizard. The experimental results demonstrate our proposed AGAFNet achieves comparable performance to state-of-the-art methods.