An AI agent that strings together heterogeneous image tools - rather than leaning on one model - is detailed in a new paper (arXiv:2607.05465) published July 8, 2026.
The authors introduce two artifacts: CanvasCraft, a dataset of 140,000 annotated image-editing trajectories plus 10,000 reinforcement-learning task specifications, and CanvasAgent, the agent trained on it. The workflow is multi-turn: the agent inspects intermediate outputs, tracks visual assets across steps, and adjusts its next tool call based on what the canvas actually looks like mid-task. Training uses supervised fine-tuning first, then GRPO - a reinforcement method - with a hybrid reward that grades both the final image and each intermediate action.
Most multimodal agents today are built around perception: they look at images and answer questions. CanvasAgent flips that orientation toward manipulation, where the agent must actively change visual state across a pipeline that can involve synthesis, object localization, region segmentation, inpainting, compositing, OCR, and enhancement in sequence. That is a meaningfully different capability class, and the 140K trajectory dataset addresses a gap the paper identifies explicitly: there is almost no large-scale supervised data for this kind of multi-tool image creation.
The benchmark is promising on paper, but CanvasCraft is a self-constructed dataset, so independent validation of the agent's real-world usefulness remains an open question - a familiar caveat for capability claims in AI research.