Agentic System Generation

Papers on using LLMs or coding agents to synthesize and optimize end-to-end systems and application deployments.

Serving System Generation

• FlashRT: Agent Harness for Guiding Agents to Deploy Real-Time Multimodal Applications (arXiv:2607.18171) [arXiv] [Code] [Homepage]

  • CMU & AMD & University at Buffalo

  • Introduces FlashRT, an agent harness that transforms simple single-GPU multimodal reference applications into optimized multi-GPU deployments.

  • Uses a chain-of-program workflow to derive and validate an intermediate representation, identify candidate transformations through static analysis, and iteratively implement, verify, and benchmark them.

  • Optimizes placement, streaming, and intra-model parallelism across hardware budgets and latency or throughput objectives.

• VibeServe: Can AI Agents Build Bespoke LLM Serving Systems? (arXiv:2605.06068) [arXiv] [Code]

  • UW

  • Introduces VibeServe, a multi-agent loop that synthesizes complete LLM serving stacks for a target model, workload, and hardware configuration.

  • Uses an outer loop to plan and track the system-design search and an inner loop to implement candidates, check correctness, and benchmark performance.

  • Remains competitive with vLLM in standard deployments and exploits specialization opportunities in six non-standard serving scenarios.