Atlas AI Agent

Atlas' proprietary AI Agent is trained to assemble game ready workflows using a diversity of AI models and act as a design co-pilot to expose users to the diversity of models available on the platform

Underpinning the platform is a multi-agent system trained to help users build workflows, diagnose issues and propose improvements based on extensive 2D and 3D benchmarking not only research results, but practical out-of-distribution stress tests, and critically real-world artist usage. Every AI model we integrate into the platform gets tested against real tasks by real artists. We iterate based on what they actually ship.

Aside from allowing us to hide a majority of very specific and technical AI hyperparameters from the user (which the agent automatically sets), the agent is fundamentally a way to help someone unfamiliar with AI (like an artist) navigate and understand what these diverse models are capable of both in isolation and in combination.

Key User Features

• Conversational Interface. Describe what you need in plain language. Refine through dialogue. No prompt engineering required.

• Context-Aware Generation. The agent understands your project, style guidelines, and previous assets. Outputs stay consistent.

• Real Workflow Training. Trained on professional game art pipelines, knows the difference between concept and production as well as various art styles and direction.

Key Technical Features

• Context engineering. The agent decides what goes into the context window. You don't manually copy-paste code files and error logs. The app does the retrieval, embedding, and curation. This is a ton of hidden work.

• Multi-call orchestration. Under the hood, there are embedding models for your files, chat models for reasoning, models that apply nodes. The user sees one experience. The app runs a whole orchestra.

• Application-specific GUI. Text is hard to audit. Seeing nodes and connections uses your visual system, which is way faster than reading, and it gives the artist way more control.