arXiv:2606. 01725v1 Announce Type: new Abstract: Agentic AI completes tasks through iterative planning, tool use, and reasoning based on observed outcomes.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2607. 28629v1 Announce Type: new Abstract: The rapid transition from reactive large language models (LLMs) to persistent, action-capable systems has exposed critical gaps in the architectural understanding of Agentic AI, particularly in separating inference, orchestration, and execution layers for autonomous AI agents.
arXiv:2607. 15660v1 Announce Type: new Abstract: While LLM agents demonstrate strong reasoning abilities in compact and well-defined scenarios, they struggle to maintain robustness and effectiveness when faced with large-scale, diverse, and dynamic real-world environments that demand seamless tool integration.
arXiv:2607. 06233v1 Announce Type: new Abstract: LLM-powered data agents are playing an increasingly important role in data-driven decision making.
arXiv:2606. 31229v1 Announce Type: new Abstract: Ideation plays a pivotal role in scientific discovery.
arXiv:2608. 03018v1 Announce Type: new Abstract: Modern cities rely on an increasing number of digital services to operate, but residents' daily needs are still difficult to meet.
arXiv:2606. 11070v1 Announce Type: cross Abstract: Recent advances in reasoning and tool-calling capabilities of large language models (LLMs) have enabled increasingly capable agentic systems.