Implement Advanced Multimodal State Design for AI Agents

I hit a frustrating wall when I first started building AI agents. But the lessons I learned now define how I build enterprise-grade apps. Let me explain... Early on, I was extremely comfortable manipulating text. But the moment I tried to add PDFs, images, and audio, my clean architectures collapsed. I built pipelines that chained together: • OCR engines for PDFs • Layout detection for tables and diagrams • Custom classifiers for images It looks sophisticated. But behaved like a brittle machine that broke every time a document layout changed. The breakthrough came when I realised I was solving the wrong problem. I did not need to convert documents to text... I just needed to treat them as images. ... And let multimodal LLMs handle them natively. Once I understood that: • Every PDF page is effectively an image • Modern LLMs can “see” as well as they can read • Images, audio, and text all become tokens The entire system simplified. And the accuracy increased. Here's the gist: If you keep normalising everything to text, you are throwing away the information that matters most. In the next installment of the AI Agents Foundations series in Decoding AI Magazine, I break down how to build agents that work with this reality instead of fighting it. Here is what I will walk you through: • Foundations of multimodal LLMs • Practical implementation • Multimodal state management • Building the agent If you want this lesson in your inbox the moment it goes live, subscribe to Decoding AI Magazine. Link https://lnkd.in/dgKZFc5j