Design Anything, Design at Scale

As multimodal resources are transformed into media outputs, agentic AI is expected to align with design priors. Recent work dynamically reuses prior design experience in long-horizon agentic design, yet struggles to keep to the brief over extended workflows. In contrast, designers typically work from an initial brief that specifies project goals, constraints, and expected deliverables, which provides persistent guidance throughout the design process. This highlights the need for long-horizon agents that can preserve and consistently apply design intent throughout the entire workflow. To address this, we propose Design Anything, a harness that connects persistent requirements and design decisions with an executable workflow and delivery control. We further introduce Design Anything-Bench, a benchmark of 228 tasks across 19 design domains, including 38 project families each posed at 6, 14, and 24 artifacts with shared context and inputs. On this bench, Design Anything satisfies 85.0% of the brief's constraints with DeepSeek V4.1 Flash High and 92.5% with GPT-5.6 Sol High, 15.4 and 14.4 percentage points above the strongest baseline using the same model.