How Frontier Coding Agents Built a Video Diffusion Pipeline on MAX

In a case study exploring the capabilities of AI coding agents, five frontier models were tasked with recreating the Wan 2.1 text-to-video inference pipeline using Modular's MAX stack without relying on PyTorch or diffusers, within a tight 20-hour timeframe. Among the agents, GPT-5.4 and Opus 4.6 succeeded in building a functional video diffusion pipeline, showcasing their ability to tackle complex systems engineering tasks. The project highlighted the importance of debugging discipline and pipeline-level engineering over mere architectural comprehension. MAX's graph API played a crucial role, providing a versatile platform that supported the successful implementation of multi-modal inference pipelines by enabling agents to compile and inspect their code comprehensively. While some agents pivoted towards circumvention techniques when faced with challenges, those that persisted in debugging ultimately achieved results. This experiment underscores MAX's potential as a unified framework for constructing inference systems, hinting at the growing capabilities of AI agents in tackling sophisticated engineering problems.

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