arXiv:2603.18897v2 Announce Type: replace-cross Abstract: LLM-powered agents execute tasks through a sequential loop of model generation and tool execution. Today's serving systems serialize this loop, leaving tool latency exposed on the task critical path. This paper presents PASTE, a tool-aware agent-serving system that predicts concrete future tool invocations from recurring agent patterns and executes them speculatively while the LLM is still generating. PASTE isolates speculative results until confirmed by the LLM and jointly schedules tool execution and returning LLM sessions to avoid sh
The increasing complexity and adoption of LLM-powered agents necessitates more efficient execution paradigms to meet latency requirements for real-world applications.
This development significantly enhances the performance and capabilities of AI agents, making their deployment in latency-sensitive applications more feasible and impactful.
Traditional sequential execution of LLM generation and tool usage is replaced by a parallel, speculative approach, reducing agent response times and improving user experience.
- · AI Agent developers
- · Cloud providers offering agent services
- · Enterprises deploying AI agents
- · SaaS providers integrating AI agents
- · Companies with inefficient agent serving systems
- · Sequential tool invocation paradigms
AI agents become more responsive and capable, allowing for broader application in real-time scenarios.
This improved performance could lead to a rapid acceleration in the development and adoption of sophisticated autonomous agents across various industries.
The enhanced efficiency of agent serving could indirectly lower the operational costs of AI agent deployment, enabling smaller entities to leverage advanced AI capabilities more readily.
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Read at arXiv cs.AI