AI agents are rapidly transforming artificial intelligence, capturing the attention of innovators and businesses. Developing AI agents comes with significant hurdles that innovators are striving to overcome, including challenges in keeping context, navigating regulations, setting up error-handling systems, integrating with existing systems, addressing security and compliance issues, managing diverse data types, enabling multimodal interactions, incorporating feedback loops, and simplifying deployment processes. These challenges highlight the need for a comprehensive understanding of AI agent development hurdles and the importance of choosing the best tools to address these challenges.