Imagine a team of AI agents, each with unique skills and expertise, working together to answer complex questions about images. This isn't science fiction, but the reality of a groundbreaking new approach to Knowledge-Based Visual Question Answering (KB-VQA). Traditional AI models often struggle with KB-VQA, which requires not only understanding the image but also drawing on external knowledge. This research introduces a “multi-agent voting framework” (MAVL) using Large Language Models (LLMs) that mimics a human team dynamic. Three LLM-based agents—Junior, Senior, and Manager—each have different access to tools. The Junior agent can only access basic image analysis tools. The Senior agent has access to those, plus the ability to retrieve information from knowledge bases. The Manager has access to all of the above and can generate new knowledge from another LLM specializing in that task. Like a real team, the agents collaborate, each providing an answer based on their capabilities, and then they 'vote' on the final answer, weighting the votes by seniority. This approach is yielding impressive results. Experiments on benchmark datasets like OK-VQA and A-OKVQA show significant performance improvements over existing models, demonstrating the power of teamwork in the world of AI. This multi-agent system goes beyond simply processing images and text. It uses 'planners'—also LLMs—that determine the optimal course of action for each agent, mimicking the way a human would decide whether to use a search engine or rely on their own knowledge when answering a question. The researchers found that these planners are crucial for maximizing efficiency and accuracy. The ability to generate new knowledge, rather than relying solely on pre-existing information, opens exciting possibilities. Imagine an AI system that can not only answer questions but also discover new facts and relationships between information. While the system is already exceeding expectations, there's still room for improvement. Fine-tuning the individual agents, optimizing the voting strategies, and exploring even more powerful LLMs are all promising avenues for future research. This research is a leap forward in the quest for AI that truly understands and interacts with the world, offering a glimpse into a future where AI teams can solve complex, multimodal tasks with human-like efficiency and ingenuity.