How I automate my own job at Hugging Face using agents — Niels Rogge, Hugging Face
Niels Rogge details how he automated his role at Hugging Face—the 'Google Drive to the hub' team—which focuses on improving the discoverability of machine learning artifacts. He built two systems: an initial deterministic workflow for outreach (using cron jobs and LLM APIs) and a subsequent fully autonomous agent loop for follow-up actions. The architecture leverages modern tooling like Modal, Bash CLI skills, and advanced models (e.g., GLM 5.2) to scale the process of identifying missing artifacts and prompting researchers to publish them on Hugging Face.
Key takeaways
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The Problem: Artifact Discoverability
ML weights and datasets are often published on third-party services (Google Drive, Zenodo) rather than the centralized platform (Hugging Face), hindering discoverability. The goal is to automate outreach to authors.
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Initial Automation: Deterministic Workflow
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The first phase used a deterministic workflow, running as a nightly cron job on GitHub Actions. This approach utilized LLM APIs in predefined steps without an agent framework, offering high predictability and control.
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Advanced Automation: Autonomous Agent Loop
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The follow-up process was automated using a fully autonomous agent loop (e.g., leveraging the Claude agents SDK). This flexible approach allows the agent to use tools and skills, such as Bash and the Hugging Face CLI, to interact with GitHub issues.
