arXiv:2601.14192v2 Announce Type: replace Abstract: Recent years have witnessed increasing interest in extending large language models into agentic systems. While the effectiveness of agents has continued to improve, efficiency, which is crucial for real-world deployment, has often been overlooked. This paper therefore investigates efficiency from three core components of agents: memory, tool learning, and planning, considering costs such as latency, tokens, steps, etc. Aimed at conducting comprehensive research addressing the efficiency of the agentic system itself, we review a broad range of
The accelerating advancement and deployment of large language models are pushing the boundaries of AI agent capabilities, making efficiency a critical and timely focus for real-world application.
Efficiency in AI agents directly impacts their scalability, deployment costs, and practical utility, thereby accelerating the transition from experimental systems to pervasive, economically viable solutions.
The focus is shifting from pure capability demonstration to optimizing the practical and economic viability of AI agents through improved memory, tool learning, and planning efficiency.
- · AI platform providers
- · Enterprises adopting AI agents
- · Developers of efficient AI models
- · Cloud infrastructure providers
- · Inefficient AI agent startups
- · Companies with high operational costs
- · Legacy workflow automation providers
More cost-effective and scalable deployment of AI agents in various industries.
Increased adoption of AI agents will drive further innovation and competition in efficiency and specialized applications.
The economic impact of automated tasks could accelerate, leading to significant shifts in labor markets and business processes.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI