A step-by-step tutorial on building an agentic AI vision system that combines SAM 3 (open-vocabulary segmentation) with Qwen2.5-VL (vision-language model) to perform iterative, self-correcting object segmentation. The system interprets natural language instructions, converts them into segmentation prompts, runs SAM 3, verifies results with the VLM, and refines prompts in a feedback loop until the correct object is segmented. Full Python code is provided covering model loading, VLM inference, SAM 3 text-prompted segmentation, the agentic loop with iterative refinement, and result visualization.
Table of contents
Agentic AI Vision System: Object Segmentation with SAM 3 and QwenWhy Agentic AI Outperforms Traditional Vision PipelinesWhy Agentic AI Improves Computer Vision and Segmentation TasksWhat We Will Build: An Agentic AI Vision and Segmentation SystemAgentic AI Workflow: Vision-Language Reasoning and Segmentation LoopAgentic AI Architecture: Combining VLMs and SAM 3 for VisionFinal Output: Agentic Vision System with Segmentation and ReasoningKey Takeaway: VLM + SAM 3 = Intelligent Vision AgentConfiguring Your Development EnvironmentPython Setup and Imports for Agentic AI Vision SystemLoading SAM 3 and Qwen Vision-Language Models in TransformersImplementing VLM Inference for Agentic Vision Reasoning with Qwen2.5-VLImplementing the SAM 3 Text-Prompted Segmentation FunctionImplementing the Agentic AI Segmentation Pipeline with Iterative RefinementVisualizing and Saving the Segmentation ResultsRunning the Agentic AI Vision System on Real ImagesAgentic Segmentation Output: Iterative Prompt Refinement in ActionSummary1.7K Impressions