Agent-serving schedulers face a timing problem that ordinary inference queues miss: a session waiting on an external tool may not yet be ready for its next GPU step. Treating tool readiness as part of scheduling can change which session should run now—and where—rather than simply prioritizing whichever request is waiting longest.
Key Findings
- TAO combines expected near-term tool returns, node-specific cache credit that decays over time, and token footprint into a session value. This connects workflow state with GPU allocation rather than optimizing model calls in isolation.
- A dynamic program jointly selects and places waiting sessions under capacity constraints. Joint decisions can account for competition among sessions and nodes, unlike independent placement heuristics.
- Across 12 reported model, workload, and concurrency settings, the paper reports up to 26.8% lower latency, 36.3% lower mean time to first token, and 29.5% higher decode throughput versus the best evaluated baseline for each metric. These are maximum reported gains, not averages or guarantees across every setting.
How to Read the Results
The central idea is that an agent session’s usefulness to schedule depends on more than its queue position: whether its tool result is ready and whether a node can reuse relevant cached state both matter. The reported improvements are promising, but the abstract does not provide enough detail to assess the workload mix, hardware, baseline configurations, or variability. The figures should therefore be treated as author-reported results pending examination of the full evaluation.
Who Should Care
A good fit for researchers and serving-system engineers working on multi-step LLM agents, especially where tool execution and GPU inference share a service pipeline. Look elsewhere if the workload is ordinary single-turn inference with no meaningful tool waits or reusable node-local cache; the scheduling signals that motivate TAO may offer little benefit there.