Building AI Agents with Multimodal Models

In the rapidly evolving landscape of artificial intelligence, text-only systems are no longer the limit. Modern AI agents must understand the world just as humans do—by combining text, images, and structured data to make informed decisions. This text-based course guides you through the foundational concepts and practical architectures needed to build intelligent agents powered by multimodal models. You will progress from understanding core neural fusion techniques to designing agents that can dynamically select tools, process diverse data types, and execute complex workflows. What you'll learn: * Understand the core principles of multimodal AI, including how models align text and visual data; * Learn to structure prompts for multimodal foundation models to achieve reliable reasoning; * Explore data fusion techniques to combine diverse inputs for agent decision-making; * Apply tool-use and function-calling patterns to connect your agents to external APIs; * Implement retrieval-augmented generation (RAG) concepts tailored for multimodal data structures; * Practice designing agentic workflows that autonomously plan and execute multi-step tasks. You will start by exploring essential terminology and the architecture of multimodal models, then gradually move into agent design, memory management, and modern orchestration patterns through clear, written explanations and step-by-step code walkthroughs. This course is designed for software developers, data enthusiasts, and tech professionals who are new to AI agents and want a clear, conceptual, and practical introduction without complex prerequisites. Start reading today to build your first intelligent, multi-sensory AI agent.