arXiv:2505.16120v3 Announce Type: replace Abstract: The emergence of Large Language Models (LLMs) has reshaped agent systems. Unlike traditional rule-based agents with limited task scope, LLM-powered agents offer greater flexibility, cross-domain reasoning, and natural language interaction. Moreover, with the integration of multi-modal LLMs, current agent systems are highly capable of processing diverse data modalities, including text, images, audio, and structured tabular data, enabling richer and more adaptive real-world behavior. This paper comprehensively examines the evolution of agent sy
The rapid advancement of Large Language Models (LLMs) and their multimodal capabilities is enabling a new generation of agent systems that transcend traditional rule-based limitations.
This development indicates a significant leap in the autonomy and adaptability of AI systems, positioning them to fundamentally alter workflows and potentially disrupt established software layers in various industries.
AI agents are evolving from narrow, rule-bound systems into highly flexible intelligences capable of cross-domain reasoning and multimodal data processing, leading to more sophisticated real-world applications.
- · AI software developers
- · Enterprises adopting AI agents
- · Cloud infrastructure providers
- · Legacy SaaS providers
- · Traditional IT services
- · Businesses reliant on manual white-collar workflows
The deployment of LLM-powered agents will automate an increasing number of complex tasks across industries.
This automation will drive significant productivity gains and require a re-evaluation of human-computer interaction paradigms.
The widespread adoption of autonomous agents could lead to new forms of economic organization and a restructuring of labor markets.
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