Imagine an AI agent that can not only understand your requests but also interpret images and use tools to complete complex tasks. Researchers are moving beyond text-based commands and developing agents that can process visual information to reason more effectively. A new method called Multi-modal Agent Tuning trains AI agents to efficiently utilize tools by creating a massive dataset of multi-modal tasks and their solutions. This dataset, known as MM-Traj, contains thousands of tasks that require the agent to use various tools, from web searches and image analysis to face detection and file inspection. The result? An AI agent that demonstrates improved tool usage and reasoning capabilities compared to its predecessors. These advancements are tested on benchmarks like GTA and GAIA, which evaluate the agent's ability to solve multi-step problems involving both images and other file types like PDFs and spreadsheets. While current models shine in scenarios involving multiple images and complex code generation, challenges remain, especially when dealing with longer, more intricate trajectories or competing with the vast knowledge base of closed-source models like GPT-4. However, this research paves the way for more capable and versatile AI agents that can interpret the visual world and use tools effectively, much like humans do.