{"slug": "tao-tool-aware-joint-scheduling-for-llm-based-agentic-serving", "title": "TAO: Tool-Aware Joint Scheduling for LLM-Based Agentic Serving", "summary": "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.", "body_md": "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", "url": "https://wpnews.pro/news/tao-tool-aware-joint-scheduling-for-llm-based-agentic-serving", "canonical_source": "https://aiflash.com/news/141631/", "published_at": "2026-10-29 04:00:00+00:00", "updated_at": "2026-10-29 04:12:23.657317+00:00", "lang": "en", "topics": ["ai-agents", "large-language-models", "ai-infrastructure", "ai-research"], "entities": ["TAO", "LLM-based Agentic Serving"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/tao-tool-aware-joint-scheduling-for-llm-based-agentic-serving", "markdown": "https://wpnews.pro/news/tao-tool-aware-joint-scheduling-for-llm-based-agentic-serving.md", "text": "https://wpnews.pro/news/tao-tool-aware-joint-scheduling-for-llm-based-agentic-serving.txt", "jsonld": "https://wpnews.pro/news/tao-tool-aware-joint-scheduling-for-llm-based-agentic-serving.jsonld"}}