# Complexity and Scale in AI-Assisted Workflow Management: A Federated Learning Case Study

arXiv: https://arxiv.org/abs/2606.18425 · HTML: https://mlsanity.com/paper/2606.18425/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [AFlow: Automating Agentic Workflow Generation](https://mlsanity.com/paper/2410.10762/) (2024)
- [Spec-Driven Development:From Code to Contract in the Age of AI Coding Assistants](https://mlsanity.com/paper/2602.00180/) (2026)
- [From Prompt to Pipeline: Large Language Models for Scientific Workflow Development in Bioinformatics](https://mlsanity.com/paper/2507.20122/) (2025)
- [From Research Question to Scientific Workflow: Leveraging Agentic AI for Science Automation](https://mlsanity.com/paper/2604.21910/) (2026)
- [From Sensors to Insight: Rapid, Edge-to-Core Application Development for Sensor-Driven Applications](https://mlsanity.com/paper/2605.02859/) (2026)

## Abstract (arXiv)

Federated learning over medical images is a demanding workflow application. Each round fans out across parallel client jobs and converges on an aggregation step that feeds the next round. At scale this yields 101 sub-workflows and 2,679 jobs on GPUs at four sites, which takes an expert months to build, mostly on workflow mechanics rather than science. We ask how far AI assistance can automate such workflows. An LLM agent, grounded in a released plugin of Pegasus-specific skills, first produces a reviewable specification of checkable constraints and acceptance criteria, then generates the executable workflow. A validation loop repairs runtime failures, checks code against those constraints, and regenerates the implementation from the specification alone. We evaluate three LLM agents, report end-to-end runs on the FABRIC testbed, and show how conformance checking against the specification caught three silent errors that failure-driven debugging missed, including one that trained 1,700 jobs on random tensors.