Build Agentic Computer Vision with Roboflow Workflows

Agentic computer vision is a system where a vision model's output feeds a reasoning step that decides what to do next and takes an action, instead of stopping at a box or a label. In practice, that means a detector such as RF-DETR for perception, a vision language model for judgment, and tool calls or integrations for action, chained in a loop that can check its own result, using Roboflow Workflows. Computer vision has traditionally been built around prediction. An image enters a model and the system returns an object class, bounding box, segmentation mask, keypoint, count, or another structured result. That model output is useful, but it does not necessarily answer the operational question. For example: A conventional system then needs application code around the model to decide whether the observation matters and what should happen next. Agentic computer vision extends this architecture. Instead of treating perception as the endpoint, it treats perception as the first step in a system that can interpret what it sees, use additional context, select an allowed action, observe what happened, and continue from there. The result is not simply a computer vision model. It is a closed-loop computer vision application. What Is Agentic…