arXiv:2606. 20529v1 Announce Type: new Abstract: Policy-adherent tool-calling agents in customer-service domains must maintain task states across turns while calling tools and obeying domain policies.
By Md Nayem Uddin, Amir Saeidi, Eduardo Blanco, Chitta Baral
arXiv:2606. 29225v1 Announce Type: new Abstract: LLM agents handle user requests on behalf of organizations through tool calls and must follow the company policies stated in their system prompts.
By Seongjae Kang, Taehyung Yu, Sung Ju Hwang
The paper describes a production migration of a large-scale customer‑support conversational assistant from a single blended model to a Dynamic Response (DR) architecture. DR replaces the Qwen3‑235B‑A22B responder with a bounded ReAct orchestrator that selects typed tools and a smaller generator that writes from a validated context contract. The migration yields significant improvements: precision‑first entity selection boosts reservation selector precision from 8.3% to 89.1%, typed action IDs eliminate structured‑action hallucination, and hard‑escalation responses drop from 5.60% to 3.08%. Latency is reduced from 3.87 s to 2.24 s, GPU usage is cut by roughly one‑third, and self‑hosting cuts annual model‑serving costs by more than an order of magnitude.
By Cen Mia Zhao, Peng Wang, Chuan Shi, Yufeng Zhang, Ying Lyu, Wanmeng Ren, Robert Xue, Claire Na Cheng, Yashar Mehdad
In large-scale enterprise settings, centralized multi-agent systems (MAS) are increasingly adopted, in which a coordinator delegates user requests to lightweight, domain-specialized sub-agents. While this architecture improves modularity, scalability, and cost efficiency, its reliability depends not only on accurate routing but also on sub-agents' ability to calibrate their responses to capability constraints.
arXiv:2606. 02965v2 Announce Type: replace Abstract: As large language models gain tool access and are deployed as autonomous agents capable of editing records, executing transactions, and modifying infrastructure, we still evaluate them based on the sole metric of task completion.
By Victor Ojewale, Suresh Venkatasubramanian
arXiv:2606. 30531v1 Announce Type: new Abstract: Tool-augmented language-model agents are often evaluated by whether they select the correct tool, produce valid API arguments, and complete the requested task.
By Rahul Suresh Babu, Shashank Indukuri