Building Multimodal LLM Agents for Multi-Object Image Generation

Standard text-to-image models often struggle to accurately place and render multiple distinct objects in a single scene. By combining the reasoning power of Large Language Models with diffusion models, you can build smart agentic systems that plan, execute, and refine complex image generation tasks. In this course, you will transition from a beginner to understanding how multimodal LLM agents orchestrate multi-object image generation. You will learn how to break down user prompts, generate precise spatial layouts, and implement iterative feedback loops to correct errors. What you'll learn: 1. Understand the foundational principles of multimodal LLMs and text-to-image diffusion models. 2. Design agentic planning systems that decompose complex multi-object prompts into structured layouts. 3. Apply progressive execution techniques to generate images step-by-step. 4. Implement automated feedback loops to evaluate and refine generated images. 5. Utilize structured JSON outputs and tool-calling patterns to coordinate agent-to-model communication. 6. Explore modern orchestration workflows for building reliable AI agent architectures. The course starts with essential terminology and foundational concepts before guiding you through the architecture of agentic planners, layout generators, and feedback loops. You will study practical code walk-throughs and conceptual design patterns to build your own image-generation coordinator. This course is designed for software developers, AI enthusiasts, and tech professionals who are new to agentic workflows. No advanced background in machine learning is required, though basic familiarity with Python is helpful. Start learning today to build intelligent agents that bridge the gap between language and vision.