The paper introduces Aegis, a runtime governance system for agentic AI that treats model outputs as action proposals and mediates them through a trusted decision layer before tool execution. Aegis evaluates proposals against active policy, resolves provenance server‑side, fails closed under uncertainty, and routes selected cases through a Senate‑style settlement process. In a sandbox evaluation across 6,300 rows, Aegis prevented all governed mock‑tool applications and risky side‑effect completions, preserving provenance and quorum evidence for all settled cases.
By Adam Mazzocchetti
arXiv:2607. 03510v1 Announce Type: cross Abstract: Enterprise artificial intelligence is moving from experimentation into operational workflows.
By Roopam W. Sure
arXiv:2606. 02965v1 Announce Type: new Abstract: Benchmarks for autonomous agents measure whether agents complete tasks, yet this framing is systematically blind to whether an agent should have proceeded at all.
By Victor Ojewale, Suresh Venkatasubramanian
arXiv:2607. 29405v1 Announce Type: new Abstract: Agentic AI systems act through multi-step trajectories that combine planning, tool use, memory, interaction, and adaptation.
By Fabio Orazio Mirto, Luca D'Agati, Giuseppe Tricomi, Stefano Silvestri, Francesco Longo, Antonio Puliafito, Giovanni Merlino
arXiv:2607. 23532v1 Announce Type: cross Abstract: Swarms of LLM-assisted autonomous robots are increasingly proposed for cooperative intelligence, surveillance, and reconnaissance (ISR) in contested environments.
By Nikolaos Kekatos, Stylianos Basagiannis, Panagiotis Katsaros, Alexios Lekidis, Tom Nianios
arXiv:2607. 25364v1 Announce Type: new Abstract: Tool-using agents expose structured calls but commonly attach free-form rationales.
By Genliang Zhu (Accentrust, Georgia Institute of Technology), Chu Wang (Accentrust, University of Illinois Urbana-Champaign)