arXiv:2605.20485v1 Announce Type: new Abstract: As autonomous agents increasingly execute end-to-end tasks under fixed monetary budgets, the pressing open question shifts from whether the budget is respected, to how to spend it effectively. Existing budget-aware methods typically control reasoning step-by-step within a single agent, or learn resource allocation policies via RL. None address how to split a budget across the composing phases of a multi-agent pipeline at inference time. We propose ZEBRA, a zero-shot framework that reduces multi-phase budget allocation to a continuous nonlinear kn
As LLM agents move from research to autonomous, budget-constrained applications, efficient resource allocation becomes a critical immediate problem. This paper addresses a gap in managing multi-phase budget distribution for these advanced AI systems.
This mechanism directly impacts the economic viability and efficiency of deploying autonomous AI agents, determining how effectively fixed budgets are utilized across complex tasks. For strategic readers, optimizing resource allocation maximizes ROI and unlocks scalability for agentic systems.
The ability to perform zero-shot, continuous, non-linear budget allocation for multi-agent LLM pipelines at inference time is now a more concrete possibility, moving beyond step-by-step or RL-based methods.
- · AI Agent developers
- · Cloud service providers
- · Enterprises deploying autonomous AI
- · LLM orchestration platforms
- · Inefficient AI agent systems
- · High-cost LLM providers without efficiency gains
More cost-effective deployment and scaling of complex autonomous AI agents in real-world applications.
Increased adoption of multi-agent LLM pipelines as economic barriers are lowered and performance becomes more predictable.
Acceleration of white-collar workflow automation and the creation of new AI-driven service layers, impacting labor markets and business models.
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Read at arXiv cs.LG