This paper investigates the integration of cognitive agents powered by Large Language Models (LLMs) within the Scaled Agile Framework (SAFe) to reinforce software project management. By deploying virtual agents in simulated software environments, this study explores their potential to fulfill fundamental roles in IT project development, thereby optimizing project outcomes through intelligent automation. Particular emphasis is placed on the adaptability of these agents to Agile methodologies and their transformative impact on decision-making, problem-solving, and collaboration dynamics. The research leverages the CogniSim ecosystem, a platform designed to simulate real-world software engineering challenges, such as aligning technical capabilities with business objectives, managing interdependencies, and maintaining project agility. Through iterative simulations, cognitive agents demonstrate advanced capabilities in task delegation, inter-agent communication, and project lifecycle management. By employing natural language processing to facilitate meaningful dialogues, these agents emulate human roles and improve the efficiency and precision of Agile practices. Key findings from this investigation highlight the ability of LLM-powered cognitive agents to deliver measurable improvements in various metrics, including task completion times, quality of deliverables, and communication coherence. These agents exhibit scalability and adaptability, ensuring their applicability across diverse and complex project environments. This study underscores the potential of integrating LLM-powered agents into Agile project management frameworks as a means of advancing software engineering practices. This integration not only refines the execution of project management tasks but also sets the stage for a paradigm shift in how teams collaborate and address emerging challenges.

Machine Learning Enabled Systems (MLS) are becoming integral to real-world applications, but ensuring their sustainable performance over time remains a significant challenge. These systems operate in dynamic environments and face runtime uncertainties like data drift and model degradation, which affect the sustainability of MLS across multiple dimensions: technical, economical, environmental, and social. While Machine Learning Operations (MLOps) addresses the technical dimension by streamlining the ML model lifecycle, it overlooks other dimensions. Furthermore, some traditional practices, such as frequent retraining, incur substantial energy and computational overhead, thus amplifying sustainability concerns. To address them, we introduce HarmonE, an architectural approach that enables self-adaptive capabilities in MLOps pipelines using the MAPE-K loop. HarmonE allows system architects to define explicit sustainability goals and adaptation thresholds at design time, and performs runtime monitoring of key metrics, such as prediction accuracy, energy consumption, and data distribution shifts, to trigger appropriate adaptation strategies. We validate our approach using a Digital Twin (DT) of an Intelligent Transportation System (ITS), focusing on traffic flow prediction as our primary use case. The DT employs time series ML models to simulate real-time traffic and assess various flow scenarios. Our results show that HarmonE adapts effectively to evolving conditions while maintaining accuracy and meeting sustainability goals.

The largest ICT employer in Hungary, Deutsche Telekom IT Solutions (formerly IT-Services Hungary, ITSH) is a subsidiary of the Deutsche Telekom Group. Established in 2006, the company provides a wide portfolio of IT and telecommunications services with more than 5000 employees. ITSH was awarded with the Best in Educational Cooperation prize by HIPA in 2019, acknowledged as one of the most attractive workplaces by PwC Hungary's independent survey in 2021 and rewarded with the title of the Most Ethical Multinational Company in 2019. The company continuously develops its four sites in Budapest, Debrecen, Pécs and Szeged and is looking for skilled IT professionals to join its team. We seek our new passionate Tech Lead for our existing MLOps platform.

Artificial intelligence (AI) is a cutting-edge technology that enables robots to learn from their own experience. AI can be found in self-driving cars, smart homes, and chess computers, to name a few. They are based on deep learning and are equipped with artificial intelligence. Computers can execute complex tasks using these technologies. As a result, businesses are recognized for their enthusiasm for AI to obtain a competitive advantage over their competitors.

Are you looking for a new challenge or a fresh start? Do you leave work each day feeling frustrated? If you answered yes to these questions, SAIC is looking to help jump start your career with new and exciting challenges. SAIC is a premier technology integrator, solving our nation's most complex modernization and systems engineering challenges across the defense, space, federal civilian, and intelligence markets. Our robust portfolio of offerings includes high-end solutions in systems engineering and integration; enterprise IT, including cloud services; cyber; software; advanced analytics and simulation; and training.

This is an exciting and challenging opportunity for an ambitious graduate, supported by Kingston University academics and based at Instinet, a leading global broker of equities, in London. This KTP project will develop and deploy innovative machine learning techniques for trading execution performance and monitoring. You will lead the development of theoretical, practical and organizational capabilities, to deploy machine-learned products to improve the execution performance of equity trading, and machine-learned tools to monitor this performance. You will also have responsibility for authoring reports and academic publications that describe aspects of your work, and the potential impact for Instinet and its clients. You will be working alongside another KTP Associate, employed as a Machine Learning Scientist.

I took my first ride in a self-driving car yesterday. It was during the grand opening of Intel's Advanced Vehicle Lab in San Jose, Calif. Automotive supplier Delphi used some Intel electronics and other tech to retrofit an Audi SUV into an autonomous vehicle. I piled into the back with a couple of other journalists and we took a two-mile drive through real Silicon Valley traffic. We had a safety driver who could take over the car in case of emergency.