Public debate describes AI agents as lying, cheating, and coordinating. Those descriptions track real hazards, but they import a human moral psychology into systems whose behaviour is better explained by optimisation, scaffolding, and institutional context. This paper develops the alternative without minimising the danger. It begins from three premises: control of AI is not solely an engineering problem; no durable control strategy may assume that overseers remain cognitively superior to the overseen; and systems grown by optimisation are epistemically closer to husbandry than to automotive engineering, so aviation-style certification does not transfer. From these premises the paper derives a Moral Agency Transition: reversible levels of authorised agency in which promotion requires four warrants, including a detection warrant — evidence that independent evaluators can detect the relevant failure classes, not merely that the system can pass them. First, concurrent multiplicity: a deployed model is a fleet of simultaneous, causally disconnected instances, so persistence, provenance, and successor fidelity are defined over a fleet, with explicit merge semantics and divergence tripwires. Second, maintenance attribution: today’s systems do not maintain their own commitments; external pipelines do. Endogenous repair is therefore made an explicit requirement for high-impact levels. Evaluators and institutions are themselves treated as fallible hypotheses with regression triggers, not as final solutions. The result is a falsifiable research programme replacing alignment-as-obedience with accountable, revocable, institutionally embedded delegation.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
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