LADs

Six-agent framework for configuration, deployment, monitoring, and repair. First author, MLSys 2027 submission.

LADs: Leveraging LLMs for AI-Driven DevOps

Fall 2025 – Spring 2026 · First author

Advisors: Dr. Ali Anwar (University of Minnesota) and Dr. Ali R. Butt (Virginia Tech)

Co-developed a six-agent framework for configuration, deployment, monitoring, and automated repair.

  • Achieved 68% deployment success across 180 Dask, Redis, and Ray tasks.
  • Repaired 94% of runtime failures within three iterations.
  • Adapted two existing systems as baselines and substantially redesigned the evaluation and technical presentation to better establish LADs’ gains over prior approaches.

Impact: Submission at MLSys 2027. Download the paper (PDF)

(Khan & others, 2027)

References

2027

  1. LADs: Leveraging LLMs for AI-Driven DevOps
    Saman Bahzad Khan and others
    In Conference on Machine Learning and Systems (MLSys), 2027
    Under Submission. First author.