A research paper titled "TAO: Tool-Aware Joint Scheduling for LLM-Based Agentic Serving" proposes a joint scheduling approach for agentic LLM services that alternate between GPU model inference and distributed CPU tool execution. The authors argue existing systems overlook tool-return readiness in service evaluation, wasting limited resources, and rely on heuristic strategies to decide admission and placement. The work targets scheduling efficiency for LLM-based agent serving across clustered GPUs and external tools.
Agentic services alternate between model inference on clustered GPUs and external tools on distributed CPUs. In this scenario, existing systems (1) overlook tool-return readiness in service evaluation, wasting limited resources, and (2) use heuristic strategies to decide admission and placement on G
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