NVIDIA has released an updated example for its FLARE framework, introducing Auto-FL, an automated, AI-driven research loop designed to accelerate federated learning experimentation. Federated learning research frequently stalls due to the difficulty of isolating variable impacts, maintaining fair comparisons, and tracking iterative changes across distributed training runs. Auto-FL addresses these challenges by structuring agent-led development into a controlled, reproducible workflow. The system operates through a fixed benchmark contract with a set training budget and consistent evaluation metrics. An AI agent proposes modifications within a bounded mutation surface, executes the experiment, and logs outcomes to a central ledger. This architecture prevents uncontrolled shifts in model capacity, communication budgets, or evaluation logic that typically compromise experimental validity. Researchers retain oversight by defining the research question, budget, and permissible mutations, while the agent handles repetitive trial runs and data aggregation. Auto-FL incorporates a literature-grounded recovery mechanism to address performance plateaus. When the experiment ledger indicates stalled progress, the workflow shifts to a structured review cycle. The agent analyzes failed candidates, cross-references academic sources, generates proposal cards weighted against implementation risk and expected gain, and reintroduces only contract-compliant strategies into the testing loop. Upon campaign completion, an automated reporting skill compiles progress plots, performance lifts, runtime costs, and recommended next steps into a comprehensive record, ensuring full transparency of discarded and retained iterations. The framework demonstrates broad applicability beyond its default CIFAR-10 simulation. A tested integration with medical visual language models, specifically federated Qwen3-VL LoRA training across VQA-RAD, SLAKE, and PathVQA datasets, yielded measurable improvements in token-level F1 scores. Performance gains were particularly pronounced on challenging out-of-distribution sites, highlighting the system's capacity to optimize heterogeneous, real-world deployments. Researchers can adapt the architecture to custom datasets by modifying the task profile, which defines dataset mapping, metrics, and mutation constraints, while preserving the core control plane and reporting infrastructure. By decoupling experimental logic from task-specific configurations, Auto-FL provides a portable scaffold for federated learning optimization. The system transforms open-ended AI coding prompts into disciplined research cycles, prioritizing comparative accuracy, reproducible documentation, and efficient resource allocation. Teams utilizing FLARE can deploy the provided scripts, baseline recipes, and mutation schemas as a foundation for rapid, structured model iteration.